rafmacalaba/data-use-mentions-extended huggingface dataset:
These reforms affected all types of ECED establishments; however, we focus on preschools due to data availability.<sup>3</sup> Preschools are non-mandatory, formal ECED establishments intended for children between the ages of 4 and 6. They offer academic preparation for primary education. Figure 2, Panel B shows the upward expansion of preschools overtime. Between 2003, when the first reforms were enacted, and 2014, the average number of preschools per 1,000 children of eligible age almost doubled, from 3.7 in 2003 to 6.6 preschools in 2014. As a result, the preschool enrollment rate (3-6) years more than doubled during the same time, from 25.7 percent in 2003 to 57.9 percent in 2014 (World Development Indicators, 2022). Figure 2, Panel A illustrates the geographical variation of preschool access across the country. In the next section, we elaborate how we exploit spatial and temporal variations in preschool access to estimate its impact on firm productivity.

# 3. Empirical strategy and data

We combine several data sets to estimate the impact of access to preschools on firm-level outcomes. We use PODES, the village census, and the annual National Socioeconomic Survey (SUSENAS) to construct a measure of preschool density: the number of preschools per 1,000 children of eligible age (3 to 6 years old). Although preschools are intended for children aged 4 to 6, following Halim et al. (2022), we define preschool density with a broader age range, as age requirements are not strictly enforced. PODES is fielded approximately every three years. We use data from 2002 to 2014. SUSENAS is annual, and we merge corresponding years to PODES data. We measure preschool availability at district, rather than village level, as this is the lowest level geographical identifier available in all datasets that we use. Since decentralization reform in 1999, districts have often split over time. In 1993, there were 290 districts; by 2014, there were 511
*|21.2|
|Firms’ value lost due to power outages|% sales|7.0|**2.9**|6.5|
|Access to electricity (from utility)|% population|46.1|**0.0**|15.0|
|Urban access to electricity|% population|79.4|**0.0**|57.6|
|Rural access to electricity|% population|28.0|**0.0**|3.9|
|Growth in access to electricity|% population/year|2.4||0.8|
|Revenue collection|% billings|81.1|**93.0**|93.1|
|Distribution losses|% production|25.8|**24.8**|23.7|
|Cost recovery|% total cost|53.9|**55.8**|84.4|
|Total hidden costs|% of revenue|168.3|**158.0**|68.8|
|U.S. cents|**Liberia**||Predominantly thermal<br>generation|Other developing regions|
|Power tariff (residential at 75 kWh)|**43.0**||14.5|5 – 10|
|Power tariff (commercial at 900 kWh)|**43.0**||18.8||
|Power tariff (industrial at 50,000 kWh)|**43.0**||14.2|5 – 10|

Source: Eberhard and others 2009. Derived from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data

## **Achievements**

Despite high tariffs, revenue collection ratios for Liberia’s power utility are
Policy Research Working Paper 9268

# **Abstract**

As COVID-19 continues to wreak havoc across the world, researchers are attempting to quantify the economic fallout from the pandemic as it continues to unfold. Estimating the economic impacts of a prevailing pandemic is fraught with uncertainties about the epidemiology of the disease and the breadth of disruption of economic activities. This paper employs historical and near real-time Google search data to estimate the immediate impacts of COVID-19 on demand for selected services across 182 countries. The analysis exploits the temporal and spatial variations in the spread of the virus and finds that demand for services that require face-to-face interaction, such as hotels, restaurants and retail trade, has substantially contracted. In contrast, demand for services that can be performed remotely or provide solutions to the challenges of reduced personal interactions, such as information and communications technology (ICT), and deliveries, has increased significantly. In a span of three months, the pandemic has resulted in a 63 percent reduction in demand for hotels, while increasing demand for ICT by a comparable rate. The impacts appear to be driven by supply contractions, due to social distancing and lockdown measures, and demand shocks as consumers shelter in place, with the latter dominating for most services. The magnitude of the changes in demand varies considerably with government responses to the pandemic..

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ktafere@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors
The advantage of this approach is that while we do not have data on savings and consumption choices with skills, we do have data on household wealth and skills acquisition from, for example PISA to estimate the skills cost function. How this is estimated is discussed below. The disadvantage is that the capital stock, _K_ , is fixed; here we define it as the sum of all endowments, . KK= LL∫kkff<sup>kk</sup> (kk)ddkk By modeling education cost as a function of initial endowment of wealth, we can remove credit constraints and assume the following budget constraint: (h) − EEtt where (h) is the wage for a unit of labor with skill level cc1,tt + cc2,tt ≤(1 + rr _h_ , tt)kk+ ττ is the skills cost LL + wwtt function, is the redistribution of revenue from emissions tax receipts, and L is the total stock tt ee(h, kk, θθ) ww ee(h, kk, θθ) EEtt of labor which is assumed to be fixed. Because there is no credit constraint, the solution to the ττ LL ∂ ∂ (h) consumer problem can be characterized by the choice of _h_ alone where _h_ : = ~~;~~ (h,kk,θθ) optimal choices for 1 2 follow from _h_ . ∂∂h ∂∂h cc , cc Education cost, , is defined as (h−γ )<sup>2</sup> 0 1α 2α + 2 bb 3 . In equilibrium, ee(h, kk, θθ)∂ = ∂ (h) ~~,~~ and as a result, ee(h, kk, θθ) = γγaa (γγ + γγ∂ (h) 1(kk) + γγ0 1α (kk)<sup>2</sup> (h,kk,θθ) γγ2αθθ)<sup>−1</sup> 3 which
indicators of welfare, even if this skepticism has been moderated in recent decades. There is, of course, an immense body of economic literature that uses indicators of self-assessed well-being and health (e.g., Headey et al. 2010), including indicators collected by Gallup (Kahneman and Deaton 2010; Deaton 2010; 2011). On the positive front, comparisons of self-assessed poverty and objectively measured indicators of poverty have uncovered close relationships between the two (Ravallion 2012). A recent assessment of food insecurity questions in six developing countries also found that questions pertaining to more severe forms of deprivation were highly comparable across countries, although concepts related to anxiety and dietary quality were not (Deitchler et al. 2010). In addition, there are longstanding concerns that such measures are sensitive to framing, question ordering, and other response biases. In terms of the third item, there is an extensive body of literature that examines biases in self-reported indicators (see, e.g., Benitez-Silva et al. 2004; Krueger and Schkade 2008; Ravallion 2012). A specific concern in the context of food security is that respondents may believe that more negative answers increase their chances of accessing food or cash transfers. Many such biases may only exist at certain levels but disappear when trends in the data are observed. However, any changes in question ordering could bias results, as a recent paper by Deaton (2011) shows. Substantial measurement errors could also mean that subjective indicators perform adequately in the cross-section but poorly in first differences (Bertrand and Mullainathan 2001). Clearly, there are important reasons to explore the validity of trends in the Gallup indicator, not just at certain levels.

# **<<A>>III. EXPLAINING CHANGES IN SELF-ASSESSED FOOD INSECURITY**

In this section, we explore the validity of changes in self-assessed food insecurity at the national level by gauging whether trends in the GWP indicators are explained by changes in 15
paid directly by non-bank financial institutions – such as money transfer companies, exchange bureaus, credit unions, post offices, etc -- are frequently not covered in balance of payment statistics.<sup>3</sup> As shown in Table 2, in all 40 countries covered in the survey commercial banks participate in the payment of remittances. 90% of countries collect remittance data from commercial banks. In 20 of 40 countries, exchange bureaus, credit unions, and other non-bank financial institutions pay remittances, but only 65% of those countries collect remittance data from those institutions. In 39 of 40 countries, money transfer companies pay remittances, but only 38% of central banks collect data from them. Finally, in 26 of 40 countries post offices pay remittances, but only in 35% of those countries remittance data is collected from post offices.<sup>4</sup>

**Table 2. Central Banks' Sources of Data on Remittance Inflows**

|||**Commercial**<br>**Banks**|**Exchange**<br>**bureaus and**<br>**other inst.**|**Money**<br>**Transfer**<br>**Companies**|**Post**<br>**Offices**|
|---|---|---|---|---|---|
|**a. **|**Countries where remittances**<br>**are paid by:**|40|20|39|26|
|**b. **|**Central banks that collect data**<br>**on remittance payments made**<br>**by:**|36|13|15|9|
|**c.**|**Percent of central
with the pandemic, in opposite directions, driving the decision of businesses to invest in and expand the use of digital technologies. On the one hand, the reduction of economic activity and the increased uncertainty may reduce the overall willingness to invest. On the other hand, because a key source of the pandemic restrictions is associated with a lack of mobility of consumers and workers, digital technologies have the potential to minimize disruptions by facilitating home-based work and online sales<sup>1</sup> . So far, the evidence from different sources suggests that the increased demand for the use of digital technologies is prevailing. Highfrequency indicators suggest a significant jump in the demand for online sales. For example, Google trend indices for the expression “online shopping” in different languages suggest that interest in the use of digital technology reached a historical peak at earlier stages of the pandemic (Figure 1).

> 1For example Apedo-Amah et al. (2020) shows how digital sales channels helped firms coping with the first wave of the pandemic, and Kim (2020) shows how consumers preferences changed with the pandemic, allowing firms that digitalized to access a larger market

2
. In a handful of other cases in which we cannot resolve these problems, we instead record tariffs as missing.

Third, tariff lines (products) are not defined consistently across countries at the most disaggregated (HS-8+) level. Therefore, we take the unweighted mean across (HS-8+) tariff lines within each HS 6-digit Harmonized System category, which are standardized across countries. We then classify these HS 6-digit categories into final versus intermediate use using BEC classifications as described in the text.

Fourth, some HS 6-digit tariff lines have multiple preferences recorded in the data. For example, Canada may report two tariffs for imports from Mexico: one under NAFTA and another under GSP. When one of the reported tariffs derives from an Article XXIV free trade agreement or customs union, we treat that tariff as the applicable tariff. When two or more non-FTA/CU tariffs are present, we adopt the lower of the two rates as the applicable tariff. In the end, we have information on the preference scheme under which every bilateral preferential tariff is offered in the data.<sup>61</sup> Fifth, there are several technical issues that need to be addressed pertaining to exit/entry of HS 6-digit codes in the data (either over time or across countries at a given point in time) and non-ad valorem tariffs. We start with a data set that includes all available HS 6-digit tariffs. We then refine the data in two dimensions. First, we discard all HS 6-digit sectors (by importer) in which tariffs are applied exclusively as specific duties.<sup>62</sup> Second, we retain

> 61One hurdle to identifying preference programs is that program identifiers in the raw UNCTAD/TRAINS data are often difficult to parse. When necessary, we cross-reference various secondary sources to identify the relevant preference schemes.

> 62To clarify, some importers may apply ad valorem tariffs in a given HS 6-digit sector, while others apply

51
From that point on, growth must depend on the steady accumulation of fundamentals emphasized by neoclassical growth theory. Long-term successes, such as Britain, Germany, and the United States, have all gone through these phases, as have more recent examples, such as Japan, South Korea, and Taiwan, China. If doubts remain about China’s economic future, it is because so much of the country’s institutional transformation, particularly with respect to political institutions, still remains ahead of it.

This typology helps clarify one of the puzzling aspects of cross-national data: institutional quality and human capital are both highly correlated with income levels, yet improvements in institutions and human capital are not a reliable predictor of economic growth. It suggests that this empirical finding is not a contradiction. Only countries that steadily enhance their fundamental capabilities eventually become rich. But investment in fundamentals is not the quickest or easiest way of getting there, at least during the early stages of development. Early on, it is rapid industrialization that fuels growth, and this requires policies that may differ considerably from conventional fundamentals. Countries that rely exclusively on building up broad-based capabilities are rewarded with modest growth, and may in fact be diverted from those policies as a result (Rodrik 2013a).

We will use this typology to interpret the experiences of our country examples. None of them can be said to have made it definitively to the nirvana of quadrant (4). Botswana has high fundamentals but limited structural change, while Vietnam has relatively rapid structural change but relatively low fundamentals. Our other African examples (Ghana, Nigeria, and Zambia) typically have had episodic growth-promoting structural change at best, moving back and forth between quadrants (1) and (2), although Ghana has recently moved into quadrant (3). Brazil has moved from quadrant (2) to quadrant (3), with greatly improved fundamentals but much weaker growth underpinned by slow structural change. India meanwhile has not experienced the kind of structural change that import-substituting countries (such
no contribution to make to this discussion and simply use the Hart Index as our basic measure of mobility.

> 6For an interesting exercise which compares results on poverty vulnerability (the propensity to move into poverty) obtained using panel data on incomes with those obtained from repeated cross-sections instead and finds that model parameters recovered from pseudo-panels approximate reasonably well those estimated directly from a true panel, see Bourguignon, Goh and Kim (2006)

> 7See, for example, Aiyagari (1994), Heaton and Lucas (1996), and Levine and Zame (2002) and Section III for further discussion of the issue of transitory income shocks. Clearly, in the case of measurement error there is even more compelling reason to neglect the i.i.d. component in the welfare analysis. We should also note that while we consider self-insurance against persistent income shocks, we do not model any alternative schemes that may provide insurance against variations in persistent income. We believe this characterization to be closer to that of developing economies, but this analysis would be relevant in any contexts where such (social) insurance schemes are absent.

8Moreover, Krebs (2007) shows that even in the case of persistent, but not necessarily permanent, income shocks (AR(1) process with auto-correlation coefficient less than one) consumption still responds one-forone to income shocks if there are costs of financial intermediation that generate a sufficiently large spread between borrowing rate and lending rate.

3
## I Table 2

# DISTRIBUTION OF SAMPLE BY LOCATION AND SECTOR

||Metropo|litan<br>Lima|Other ur|ban areas|<br>Sect|or totals|
|---|---|---|---|---|---|---|
||Number|ColumnZ|Number|Column Z|Number|Column %|
|Wage employees|||||||
|Number|756|63.9|527|51.3|1,283|58.1|
|Row Z|58.9|(34.2)|41.1|(23.9)|100.0|(58.<br>"!|
|Self-employed|||||||
|Nuaber|427|36.1|500|48.7|927|41.9|
|Row!|46.1|(19.3)|53.9|(22.6)|100.0|(41.9)|
|Location totals|||||||
|Number|1,183|100.0|1,027|100.0|2,210|100.0|
|Row_%_|53.5|(53.5)|46.5|(46.5)|100.0|(100.0)|

Note: Percentages of the grand total (2,210) are in parentheses.

8
# **A Historical Lens**

Using a recently available data set from the Groeningen Growth and Development Centre’s 10-sector database (hereafter “GGDC data”) that provides employment and value added for ten disaggregated product sectors for a large set of countries historically, we begin the paper with high-level analyses investigating cross-country trends in sectoral productivity convergence and how the relationship between levels of development and economic structure has changed over time.

The GGDC data “provides a long-run, internationally comparable dataset on sectoral productivity performance in Africa, Asia, and Latin America. Variables covered in the data set are annual series of value added, output deflators, and persons employed for 10 broad sectors” (Timmer, de Vries and de Vries 2014). These ten sectors comprise agriculture, mining, utilities, construction, manufacturing, wholesale and retail trade, FIRE industries, transport, government services, and other services (restaurants, hospitality, etc.). Among Sub-Saharan African countries, it contains data from Ethiopia, Botswana, Ghana, Kenya, Malawi, Mauritius, Nigeria, Senegal, South Africa, Tanzania and Zambia.

These data allow a more nuanced look at the role of specific sectors in structural change, particularly separating construction and mining from manufacturing aggregates, and FIRE industries, government services, transport, and utilities from services aggregates, allowing greater specificity in understanding which sectors are drivers of structural change. Many of the countries in the recent release have reliable data going back to the 1970s or earlier, so we are able to build a long historical quantitative view of structural change. This, in turn, allows us to place economies’ current and recent trends in comparative historical context.

# **Changing Relationship between Income and Economic Structure**

Figures 1 and 2 use the GGDC data to investigate the cross-sectional relationship between country income levels and economic structure (as embodied by the share of employment in the focal sector). As can be seen clearly, there is a clear trend in the changing nature of the relationship between
), PfR-type interventions instead reward attainment of predetermined targets, typically set on the basis of extrapolating from what recent historical trajectories have attained.

> 21 According to the Program Appraisal Document “each state would be eligible for a grant worth $325,000 per the percentage point gain they made above average annual gain in the sum of six indicators of health service coverage.” The six indicators are: Vitamin A, Pentavalent3 immunization, Use of ITNs by children under 5, Skilled birth attendance, Contraceptive prevalence rate, and Prevention of mother-to-child transmission of HIV.

11
interested in the year of recovery, the academic year 2000/01. Since the 1999 violence occurred mainly in the summer and fall of 1999, we can assume with a certain degree of confidence that the academic year of 1998/99 was not affected by conflict, while the academic year of 1999/00 started under the wave of violence. Almost all schools were destroyed during the violence in 1999 and most teachers escaped to West Timor during the 1999/00 academic year. Therefore, very few children were able to attend school that year. The very few that attended school were in makeshift schools in IDP camps or in the open air. We report in table 1 average school attendance rates, disaggregated by gender and age groups, across these three academic years. The sample includes children aged between 7 and 12 years old over the three years considered: the sample is aged 7-10 in 1998, 8-11 in 1999 (during the violence) and 9-12 in 2000.

In general, we notice that attendance rates have increased over time and are generally higher for girls. The difference in outcomes between boys and girls is not statistically significant, but is nonetheless an interesting result. The higher attendance rates observed among girls in 1998/99 show evidence for a gender catching up phenomenon, which started before 1999. In the post-violence year, the difference in attendance rates between girls and boys becomes statistically significant, particularly for the younger cohort. This seems to suggest that educational outcomes among girls recovered faster in the post-conflict period.

In addition to the analysis of school attendance, we explore also the impact of the 1999 violence on school grade deficit. The grade deficit is computed as follows: The TLSS 2001 does not allow us to distinguish whether the grade deficit is due to drop outs, delayed entry or grade repetition. We restrict our sample to only those children that attend all three years consecutively in order to isolate the consequences of the violence on the sample of individuals that do not drop out.

The attendance rate over the three
To develop the structural gravity model of FDI, we follow the approach suggested by Kox and RojasRomagosa (2020). First, even though trade gravity models usually depend on flows, we use the stock of (sectoral) bilateral FDI as our dependent variable, as it is much less volatile than FDI flows and tends to be non-negative (which would be problematic for some of the estimation methods). We are careful to distinguish between real zero FDI stocks and missing (or non-reported, suppressed) data. Second, we employ a Pseudo-Poisson maximum likelihood (PPML) estimator, because it effectively deals with zero bilateral FDI stocks and accounts for the presence of heteroskedasticity in FDI data (Santos Silva and Tenreyro, 2006). Third, alongside our standard set of gravity variables we also follow the best practice of using country-pair fixed effects to account for any unobservable time-invariant FDI barriers, which has been proven to be a better measure of the bilateral costs (Agnosteva, Anderson, and Yotov, 2014; Egger and Nigai, 2015). Finally, we use source-year and host-year fixed effects to properly account for multilateral resistance terms in panel data gravity estimations (Olivero & Yotov, 2012). The country of origin generates the outward FDI stock and the destination country the inward FDI stock. The origin-time and destination-time fixed effects also absorb the country size variables from the structural gravity model, in addition to all other observable and unobservable time-varying country-specific characteristics, including different national policies, institutions and exchange rates (Yotov et al. 2016).

Based on this, our main specification is: FDIijst = β1 IPAstrategyjst + αij + αit + αjt + αst + αjs + εijst (2) This specification is similar to (1), but is now set up in a bilateral framework where FDIijst is the inward FDI stock from source country _i_ to the host country _j_ , for sector _s_ in period _t_ . β1 is still the coefficient of interest, capturing the average effect of the IPA strategy of the host country on sectoral FDI stocks