rafmacalaba/datause-encoder-data huggingface dataset:
as geographic distance between source and host countries. This evidence suggests that OECD capital stocks in Latin American economies relative to China changed between 1990 and 1997, but not between 1997 and 2003. At the same time, we did not find any statistically significant change in the stocks relative to China plus Hong Kong (China) and India during this period, thus implying that China plus Hong Kong (China) and India receive FDI from different sources than Latin American economies. Nevertheless, these stylized facts do not tell us much about whether FDI flows to China, Hong Kong (China) plus China, or India since 1990 have come at the expense of FDI to Latin American countries. Further econometric analysis is required to address this question (see, for example, Eichengreen and Tong 2005; Cravino, Lederman, and Olarreaga 2006; Garcia Herrero and Santabárbara 2005; and Chantassasawat et al. 2004).

The rest of this paper is organized as follows. Section 2 describes the data. Section 3 compares FCS levels in Latin America, China, China plus Hong Kong (China), and India. Sections 4 and 5 analyze the evolution of these stocks. Section 6 studies the conditional relative stocks, and section 7 provides concluding remarks.

# **2. Data Description**

The analyses in this paper use data on aggregate inward FCS, outward stocks from some OECD countries, outwards stocks from the U.S., and outward stocks from the U.S. in the manufacturing sectors of China, China plus Hong Kong (China), India, and Latin American countries. Data on aggregate stocks was collected from UNCTAD, which are available from its website.<sup>5</sup> UNCTAD reports aggregate FCS at book values or at historical cost in millions of U.S. dollars. The aggregate inward data is attractive since it

> 5 www.unctad.org

4
# **1. Introduction**

In many developing countries, a significant barrier to economic growth is an unreliable supply of electricity. Electricity shortages in South Asia are especially widespread, with the average firm experiencing nearly an outage per day, lasting roughly 5.3 hours each. Within South Asia, Pakistan has the most severe power shortages where firms report 2.5 outages each day with an average duration of 13.2 hours. More than 75 percent of firms in Pakistan identifying lack of reliable electricity as a major constraint to their operation and growth (Figure 1).

Absent a reliable supply of electricity, manufacturing firms must substitute away from energy-intensive capital or divert investments toward diesel generators, and credit constraints and market imperfections can compound these inefficiencies. In the absence of alternative sources of electricity, particularly for unanticipated outages, firms must send workers home, which decreases the productivity of labor.

Several recent studies quantify the impacts of electricity shortages on various firm- and household-level outcomes. Allcott, Collard-Wexler, and O’Connell (2016) estimate the productivity impacts of large manufacturing firms in India. Grainger and Zhang (2017) follow a similar identification strategy but focus on micro-, small, and medium Indian enterprises, and they find much larger impacts on unit costs. Fisher-Vanden, Mansur, and Wang (2015) examine the productivity and environmental effects of electricity shortages in China. Foster and Steinbuks (2009), Alby, Dethier, Straub, et al. (2011) and Andersen and Dalgaard (2013) examine the impact of outages on firm size and technological adoption. Samad and Zhang (2016, 2017) analyze the effects of power outages on household welfare in India and Bangladesh. Chakravorty, Pelli and Marchand (2014) examine the effects of power supply reliability on the benefits of electrification.

Despite the wide recognition that electricity shortages result in substantial losses for the economy in Pakistan, empirical study that quantifies the impact is still lacking. In this paper, we study the impact of power shortages on manufacturing firms in Pakistan. We match firm-level data from the Census of Manufacturing Industry conducted by the Pakistan Bureau of Statistics with district-level power shortage data reported by distribution utilities. Using a sample of 4,500 manufacturing firms for 2011, we estimate the impact of electricity shortages on firm-level value-added and total product value while controlling for each firm’s sector, labor costs, the costs of raw materials, the value of fixed capital assets, and the value of energy inputs. Our estimates suggest that a 10 percent increase in the total hours of outages on average leads to a 0.14 percent 2
top wealth quintile, and parents with higher education) almost always attend university while those from most disadvantaged backgrounds (such as those from rural areas, lower wealth quintile, and less educated parents) have almost no chance of doing so.

Third, Egyptian pupils are tracked into general and vocational education at the senior secondary level, likely leading to divergence in educational and labor market outcomes. As the Egyptian education system relies on meritocratic selection into different education tracks, educational opportunities at the early stage could largely determine later education trajectories. Admission to general secondary and higher education institutions is based on performance in a high stakes national examination. A small minority of pupils, mainly from relatively better-off family backgrounds, meet the admission criteria, while a majority is tracked into vocational secondary schools <u>(Heyneman, 1997; World Bank, 2007). Private expenditures in tutoring play an important</u> role in students’ performance on national examinations.

Finally, existing empirical evidence shows large heterogeneity in schooling conditions for Egyptian youth with important implications on educational outcomes. In the early 1980s, <u>Loxley 1983 looked at the issue of the relative contribution of family and school inputs in learning</u> and found that school resources do matter and help pupils from poorer backgrounds and compensate to some extent for the lack of family inputs. Lloyd et al. (2003) have used data from a survey of young people and the schools they attend in the late 1990s to document the effects of schools’ inputs and environments on attainments. The study found that school conditions significantly affect schooling decisions.

This paper documents the level and evolution of inequities in access to education and educational achievements in Egypt. First, it analyzes the distribution of access to education and educational attainments, and the relationships between a set of circumstances, or characteristics determined at birth and lying outside a student’s control, and educational outcomes. Second, the paper measures the inequalities in learning achievements and their evolution across educational levels and generations of Egyptian youth. For this, three main sources of data are used: (a) Egypt’s 3
inducing greater local value added, lead to greater LBD and more durable effects on production and exports.<sup>13</sup> Unfortunately, it is difficult to test this more interesting conjecture in the AGOA context since almost all African countries were allowed access under identical liberal ROO.

# **3. Data**

The analysis in this paper is based on a new highly detailed database that we constructed by combining US trade data from the US Census Bureau with tariff data published by the US International Trade Commission. The US database provides detailed information on tariffs and product-eligibility for trade preferences in the US each year, including the applied MFN tariff the country-product faces in the US, the unilateral preferences the country-product can benefit from in the US (e.g., GSP, AGOA), and the best preferential tariffs the country-product can benefit from in the US in the period 1997-2017.<sup>14</sup> Tariff measures are all expressed as ad-valorem.<sup>15</sup> The US database also includes value and quantity of US imports at country-HS 8-digit product-year level.

The US database offers important advantages relative to the widely-used trade and tariff data from the World Trade Integration Solution (WITS). First, it provides information at a more disaggregated 8-digit level. Second, it provides much better coverage in terms of years for all types of tariffs, MFN or preferential (under a large number of programs and regimes), whereas WITS has many tariffs missing. Imputation techniques used in the literature to correct those missing tariffs may yield inaccurate tariff rates. Third, preferential tariff rates are constructed based on

> 13 Cherkashin et al. (2015) show that EU’s EBA preferences that enabled firms in Bangladesh to start exporting apparel to the EU also allowed them to export to other markets as firms overcame fixed costs of production and exporting. The strict ROO requirements of EU’s EBA and its potential encouragement of greater local value added may have helped explain those benefits. 14 The US database is a companion to a comparable EU database that we mention later in this section. More details and the databases are available at: https://datacatalog.worldbank.org/node/144174/revisions/365082/view. 15 We compute ad-valorem equivalents for duty variables expressed as specific tariffs or combined tariffs (with an ad-valorem and a specific component) by dividing the specific tariff (or specific component) by the import unit price, itself computed as the median across the unit values of all US imports of a given HS 8-digit product in a given year across partner countries.

11
ing the merger of data from both waves and the exclusion of the Rohingya, the resulting dataset included 12,533 individuals in 2019 and 4,854 individuals in 2023.

2. Descriptive statistics for the variables employed in this study are in Table The analysis suggests that the population around the refugee camps is disproportionately young (on average 26 years of age) and mostly balanced in terms of gender composition (48 percent males). Additionally, household size is on average six individuals.

# **III.B.4 Individual-Level Outcomes**

Table 2, spanning Panels C to H, provides descriptive statistics for each of the individuallevel outcomes. These outcomes are grouped in several categories including: (i) adult and household welfare, (ii) labor market dynamics, (iii) health and education outcomes, (iv) safety perceptions, and (v) hosts’ attitudes toward the displaced population. A detailed B. definition of all the variables is included in Appendix _Adult and household welfare._ Adult’s welfare comprises measures of wealth, income, expenditures, consumption, and assistance. The impacts of camps are first assessed in a wealth index constructed as a composite measure of a household’s cumulative living standards the formula from the and Health The com- following Demographic Surveys.<sup>8</sup> analysis prehends the effects of camps on the total wealth index and on each subcomponent, including the proportion of service access, the proportion of adequate shelter materials, and the proportion of assets consumed. Second, the effects of refugee camps are assessed on total income and expenditures (including food and non-food). Third, the analysis evaluates outcomes related to food consumption, including: food consumption variety, share of self-produced, the share of food products for which aid was received, and indicator variables for bartering food. Fourth, since the Rohingya are completely dependent on humanitarian aid, the effects of camps are assessed on aid and assistance, including the following variables: receiving vouchers, food in-kind, assistance accessing services, and receiving household items. The descriptive statistics for aid-related outcomes (Panel C), underscore the vulnerability of the population near the camps, with a vast majority (at

> 8Details on the index construction can be found in the following link : https://dhsprogram.com/ topics/wealth-index/Wealth-Index-Construction.cfm.

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# **_4.2. Labour market institutions in the sampled countries_**

The sample statistics presented in Table 1 indicate that there exist substantial differences in the incidence of wage rigidity across the sampled countries. A natural candidate for such cross-country variation in wage rigidity is the differences in the national labour market institutions. We explore the impact of the institutional environment in the regression analysis that is carried out in the subsequent section of this paper, focusing on two aspects: collective bargaining and employment protection legislation. In the following, we will give an overview of the differences in these institutional measures across countries.

Our survey included three questions related to the collective bargaining of wages. Managers were asked if a collective wage agreement is applicable and if so, whether it is a firm-level agreement or a binding agreement that was negotiated at a level outside the firm (e.g. national, sector level, etc). In addition, the survey obtained data on the proportion of workers in the firms that is covered by any kind (inside or outside) of collective wage agreement. Table 2 summarises this information across countries, and complements it with aggregate data obtained from other sources, collected by Du Caju et al. (2008). Where comparisons are possible, this information is consistent at the aggregate level with existing institutional sources, such as an overview by the OECD (2004). We should note, however, that the measures of collective bargaining coverage presented in Table 2 refer to private sector enterprises only, whereas the measures from the above-mentioned sources are representative of the whole populations of workers in different countries.<sup>10</sup> Although union membership rates have been declining in Europe, collective bargaining coverage is still high in general. The percentage of firms that apply some kind of collective wage agreement is very high in the euro area countries under consideration, compared to noneuro area countries. Differences between euro area and non-euro area countries are also noticeable when one looks separately at collective agreements signed at different levels. Collective agreements signed outside the firm are the most common practice in the euro area countries, while firm-level agreements are more frequent in the non-euro area countries. In terms of the percentage of workers that are covered by some form of collective wage agreement, coverage is very high in the euro area countries. By contrast, non-euro area countries have low levels of coverage.

In addition to cross-country measures of bargaining coverage, Table 2 gives an overview of strictness of employment protection legislation (EPL). The measures of EPL for all countries

> 10 Appendix 1 gives an overview of the main characteristics of the national surveys.

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- 42 -

# (d) The Role of Irrigation

84. It is important to distinguish between an assured supply of water, which basically entails proper water management, from the narrow definition of irrigation. One of the lessons taught by the experience of agricultural developrent in Asia is that an assured supply of water is necessary to use full complements of modern inputs. Note, however, that the emphasis is on assured water supply, and not necessarily on large-scale flow irrigation. The record of the latter in SSA is highly discouraging. The role of irrigation (narrowly interpreted) depends on two important factors: the irrigation potential and the cost per unit of irrigated area. On both those counts SSA countries are at a disadvantage.

85. FAO has estimated the irrigation potential of SSA at 33.6 million hectares. Recent studies by the World Bank indicate that this may be an overestimate. In five detailed country studies, the total irrigation potential was estimated to be 20 percent smaller than the FAO estimate.Another factor that needs to be taken into account is that much of the land with irrigation potential lies in areas with sufficient rainfall to make the investment in irrigation unnecessary. A substantial part of 8 of the 10 countries with the largest areas with irrigation potential fall in this category. These countries are Angola, Zaire, Zambia, Mozambique, Tanzania, Nigeria, Central Africa Republic and Madagascar.

86. The revised estimate, allowing for the above adjustments, is that the potential for Irrigation in SSA Is in the range of 15-20 million
international (12 percent). Slightly over half the firms are in the service sector (53 percent), followed by manufacturing (27 percent) and retail (20 percent).

The ES COVID-19 Follow-up Surveys ask firms about changes in performance during the crisis, as well as expectations for the next months. Table 3 Panel B presents the summary statistics of firm performance by Round 3. Firms reported that their sales remained an average of 2 percent lower compared to the same month a year earlier. Round 3 surveys were typically conducted in mid-2021 (see Table A.1), meaning that firms reported their change in sales compared to mid-2020, a time when firms typically experienced steep declines in sales compared to pre-COVID-19 (Apedo-Amah and others 2020; Bruhn, Demirguc-Kunt, and Singer, forthcoming). Table A.1 also shows drops in sales reported in Round 1 and Round 2, which are all relative to the same month one year earlier. Taken together, the numbers suggest that, in mid-2021, sales of the average firm were still about 21 percent lower than before COVID-19. Unlike sales, employment outcomes in all rounds are measured compared to December 2019. The Round 3 data in Table 2 Panel B shows that the number of permanent full-time workers remained 15 percent lower compared to pre-COVID-19, with almost half of firms (47 percent) having decreased their number of workers. About 21 percent reported that they anticipated falling into arrears on outstanding liabilities in the next six months.

# **2.3 How are government support and firm characteristics correlated?**

Table 4 presents the correlation coefficients between firms that received government support and firm characteristics. Overall, not many firm characteristics are consistently or significantly correlated with having received government support across different rounds of government support. While many firm characteristics are significantly correlated with having received government support in Round 1 or 2, many of these correlations turn insignificant when we unbundle government support into Round 1 versus Round 2 support. Large firms, and those with foreign ownership are more likely to have received support in both rounds, possibly because these firms have stronger political connections. Also, having a credit or loan, and having a website are characteristics that are positively correlated with receiving government support in both rounds. This may be because firms with an existing financial relationship and online presence may be better placed to apply for and receive government support electronically where this option is available.<sup>8</sup>

> 8 We also find inconsistent or insignificant associations between government support and firm characteristics when we consider a multivariate OLS regression controlling for the same firm characteristics and country fixed effects. These results differ from our earlier findings reported in Demirguc-Kunt, Bruhn, and Singer (forthcoming) where we show in a different sample of government support data—corresponding to Round 1 in 11 countries and Round 2 in 13 countries—that more productive firms were less likely to receive any COVID-19 government support.

5
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Because a pandemic outbreak can also affect children’s socioemotional skills, the total impact of school closures on human capital accumulation is potentially larger than the effects captured by cognitive tests.<sup>31</sup> .

To give context to the school system’s challenges, we use a meta-analysis conducted by McEwan (2015) that summarizes the impacts of more than 70 randomized controlled trials of educational interventions in developing countries. According to the author, school interventions in primary education have, on average, positive effects that range from 0.05 to 0.15 of a standard deviation. The upper bound of the overall average effect of the education policies included in the metaanalysis (0.15 of an SD) is smaller than our lowest estimate. The expressive learning losses detected during a relatively small period of school shutdowns amid the H1N1 outbreak illustrate the challenges policymakers will face in designing sustainable learning acceleration policies to counterbalance the unprecedented skill losses caused by the long period of school shutdowns during the COVID-19 pandemic crisis, particularly in developing countries.

> 31For instance, the Global Education Monitoring Report (2019) points to the disruptive impact on learning as physical; emotional, with anxiety, fear, sadness, and lack of emotional control; and cognitive, expressed via difficulty paying attention, inability to process information and memory problems. As emotionally nurturing environments produce more capable learners, these factors will have more profound consequences for children’s development (Cunha and Heckman (2007))

28
37

# Table 7. **Government** Income and **Expenditure in** Two Townships in Wuxi and Shangrao Provinces, China

|Town<br>(Wux|shipA<br>i, 1985)|Town<br>(Shana|ship B<br>rao. 1986)|
|---|---|---|---|
|Income/<br>Yuan<br>Expenditures<br>(thousands|Percent of<br>) total|Yuan<br>(thousands|Percent of<br>) total|
|**Income**||||
|**Tax revenues**<br>13,428|56.4|193.5|46.4|
|**Less remitted**<br>**to**||||
|county<br>12,978|54.5|1.7|0.4|
|Retained<br>450|1.9|191.8|46.1|
|Refundfrom county|-|209.8|50.4|
|Remittances by township<br>enterprises<br>10,390|43.6|13.2&|<br>3.2|
|Less remitted to||||
|county<br>1,035|4.4|||
|Totalresources<br>9805|100.0|**i14.8**|i00.0|
|Expenditures||||
|Investment in township||||
|enterprises<br>6,720|68.5|19 <sup>.</sup>3<sup>b</sup>|4.6|
|Support to agriculture<br>900|9.2|26.0|6.3|
|Public and social services 1,050|10.7|285.7|68.9|
|Administrative||||
|expenditures<br>138|1.4|70.8|17.1|
|Unidentified (residual)' 1,097|11.2|13.0|3.1|
|Total expenditures'<br>9,805|100.0|414.8|100.0|

-- not available.

a. Township B's enterprises had net losses totaling Y13,759 in 1986. These remittances were financed by additional bank borrowing and by drawing from depreciation allowances.

b. It is assumed that all budgetary expenditure 'in support of production' other than items clearly specified as agricultural was for investment in township enterprises.

c. Total expenditures are assumed to be exactly equal to total township revenue resources, with the unidentified portion of expenditures derived as a residual.

Source: Fieldwork of World Bank/Institute of Economics of the Chinese Academy of Social Sciences research project.