rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Polky anning, and Research<br><!-- End of picture text -->

# **International Trade**

Using two independent sources of data on international transport and insurance costs, Yeats investigated the charge that Caribbean exporters are at a competitive disadvantage because of dis- criminatory freight rates particularly liner conference pricing practices. He concluded that: Air cargo service costs are lower for most of the Caribbean rountries studied than for their competitors. (Dominica and Guyana are exceptions, due to unusual air freight costs for several major products.) Over one-third of all shipments to the United States (excluding petroleum and sugar) involve air transport - the use of which has been rising rapidly in the 1980s.

Most Caribbean countries pay about the same ocean freight rates as their competitors, although Guyana, St. Kitts, St. Lucia, and Suriname pay more on many major exports.

relatively high percentage of which are shipped by air).

Although they do not pay higher average freight costs than their competitors, Caribbean exporters could reduce their freight costs by improving ports, bulking cargoes, forning shippers' unions, and adopting uniform transport modes.

Several lines of analysis warrant further study: How can the Caribbean countries best exploit the cost advantage they have for products exported by air?

Why is ocean transport so much less cost efficient than air freight for Caribbean countries?

Caribbean exporters are more likely to pay a premium on transport costs for food and agricultural raw materials (which rely heavily on ocean transpon) than on manufactured goods (a Why do certain product groups, such as food and agricultural raw materials, cost proportionately so much more to transport?

This paper is a product of the Intemational Trade Division, Intemational Economics Departnent. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Jean Epps, room S8-035, extension 33710 (164 pages with charts and tables).

The PPR Working Paper SePies disseminutes the findings of work under way in the Bank's Policy, PiPRing, and Research Complex. An objective of the series is to get these fundings out quickly, even if presentations are less than fully polished. T'he findings, interpretations, and conclusions in thesc papets do not necessarily represent of ficial policy of the Bank.

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and historical trends suggest that Malaysia is expected to pass the threshold in the mid-2020s. However, this projection might be delayed due to the effect of COVID-19, causing a reduction in the forecasted growth in 2020 and possibly in the early 2020s (Figure 1.1).<sup>3</sup> Aggregate investment-to-GDP has averaged 24% over the last two decades (Figure 1.3), with public investment trending down and private investment trending up (Figure 1.4). During the late 1980s and 1990s **,** investment rates in Malaysia increased rapidly, reaching over 40% of GDP just before the 1997 Asian financial crisis. High private investment-to-GDP rates were buoyed by the First Industrial Master Plan (1986-1995), and liberalization and deregulation in the economy. In the 1990s, excessive investments also occurred in certain sectors, especially the property sector. After the Asian financial crisis, the investment-to-GDP ratio declined, with the fall mostly due to lower levels of private investment (not shown). In the last 10 years, investment-to-GDP has averaged 24.6%. A closer look at the split between public and private investment shows that public investment has been declining since 2012, falling from around 11% to 7% of GDP in 2018. This is reflective of the government’s fiscal consolidation plan. At the same time, some rebalancing has been observed with private investment rising from 15% to 17% of GDP.

The median TFP growth over the past 30 years (1985-2014) is 0.9% (Figure 1.5). Since TFP is calculated as a residual – growth less factor accumulation – it is volatile and oscillates with the economic cycle.

The human capital growth rate has experienced a downward trend since the early 1990s and it has averaged roughly 0.6-0.7% in the 2010-2014 period (Figure 1.6). Human capital is a commonly measured using the average years of schooling, though in our forward-looking simulations we use a broader measure based on the World Bank Human Capital Index that includes schooling quality and population health. In the 1980s and early 1990s, human capital grew at around 2% but now has slowed to 0.6%. As it is harder to increase the average years of education when people are already well educated, countries often experience a slowing growth rate of human capital over time. We expect this trend will continue for Malaysia as it moves to high income status.

Total population growth was close to 1.3% over the past 5 years, after experiencing a downward trend since 1990 (Figure 1.7). Before 1990, population growth averaged 2.5-3%. Slower population growth is typical of developing economies that transition into high-income economies and is expected to continue. Slowing population growth also affects the share of population of working age (15-64), which in turn affects the size of the labor force (Figure 1.8). The share of the population between the ages 0 to 14 has 3 World Bank (2020) projects a growth rate of -0.1% under the baseline and -4.6% under a low-case scenario for 2020. 5
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Naudeau, S., R. Hasan, and A., Bakilana (2015): “Adolescent Girls in Zambia: Introduction and Overview,” Policy Brief: Zambia.

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I2D2 database and provides some descriptive statistics regarding wage dispersion across region, sectors and age groups. Section 4 presents the estimates of regional and sector mobility costs, as well as a description of their correlation with variables such as income per capita, wage inequality and the geographic and sectoral concentration of employment. Section 5 explores the extent to which the estimates of mobility costs are driven by information costs and analyzes how access to the Internet is associated with reduction in mobility costs. Section 6 concludes.

# **2 Methodology**

Consider a worker of type _l_ (a type being given by characteristics such as age, gender, education, occupation, etc.) working and living in sector-region _k_ .<sup>5</sup> Making this distinction right now would uselessly flood the text with subscript and indices. Her utility _Ul,k_ is given by: where w _l,k_ is the log of real wage received by the worker; _γl_ represents worker’s characteristics (orthogonal to wages) that are common across the different region/industries. We assume that (i) workers are rational and maximize their intertemporal utility, and (ii) what we observe in the data is an equilibrium. Workers of type _l_ will decide to move from sector-region _k_ to sector-region _k_<sup>_′_</sup> until their intertemporal utility V _l,k_ is equal to the intertemporal utility in sector-region _k_<sup>_′_</sup> (V _l,k′_ ) net of mobility costs (C _k,k′_ ). Workers of different age maximize over different time horizons. We assume they maximize over (T _l_ -t0) years, where T _l_ is the retirement age and t0 their current age. This is an important feature of our model since our empirical strategy will strongly rely on the time horizon of workers until retirement (therefore on their age) for identification. This implies that for all V _l,k′>_ V _l,k_ we should observe in equilibrium: where _β_ is the intertemporal discount factor. Substituting (1) into (2) and solving for the difference in gross intertemporal utilities yields:

> 5We consider jointly the sector in which workers are employed and the place where they live. In the empirical analysis we distinguish between industry and region.

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Policy Research Working Paper 9252

# **Abstract**

The COVID-19 pandemic has closed schools around the world, forcing school systems and students to quickly attempt remote learning. A rapid response phone survey of over 1,500 high school students aged 14 to 18 in Ecuador was conducted to learn how students spend their time during the period of quarantine, examine their access to remote learning, and measure their mental health status. The data show that 59 percent of students have both an internet connection at home and a computer or tablet, 74 percent are engaging in some online or telelearning, and 86 percent have done some schoolwork on the last weekday. Detailed time-use data show most students have established similar daily routines around education, although gender and wealth differences emerge in time spent working and on household tasks. Closure of schools and social isolation are the two main problems students say they face, and while the majority are mostly happy, 16 percent have mental health scores that indicate depression.

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 dmckenzie@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 and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
classifications is much too detailed a breakdown for the purpose at hand, namely to understand the influences on the broad allocation of different sources of funds to different 1 **nereioret wu** nave **simpliieu** anu ConsoluateU Lthe Ua-w **i-lou** a s1implifu **IUFIIItIL,** the same for all countries, distinguishing between just six broad categories with convenient notation as follows:

|_Assets_|_L_|
|---|---|
|aloansandadvances|c<br>capital|
|_b_billsandotherliquidinvestments(net)|ddepositsanddeposit-typeinstruments<br>_e_netotherliabilities|
||f<br>netforeignliabilities|

Here a ("advances" - though we use the term interchangeably with loans) equals total domestic credit, including claims on central, state and local governments (these expressed net of deposits), public enterprises, nonmonetary financial institutions and the private sector. The remaining asset-side item b ("bills") includes bank reserves net of credit to banks from the monetary authorities. The item c "capital" is the entry under **canitl acinnte** in **_IR.^_** it doens **nnt** in **a!i** nnces mrreqnnnd **tn** recmIntnrv caniti **lintder** the B 1asel conveifions. X L'e I*uJmjor iteML **UI UVIL LiaUbL1L1 1d** Upsits," "e **whrn,.IcIudIesIIU nUoL** only demand and time deposits (other than the public sector deposits aiready netted out of a) but also money market and other liquid liabilities. The residual item _e_ also includes bonds issued by banks. Net foreign liabilitiesf is self-explanatory. Detailed definitions are included in the Annex: "Consolidating the monetary survey".

Over time, each of the elements of the balance sheet evolve, but at any given moment, the balance sheet identity is satisfied by the data for each country: _a+h=e+d+e+,f_
23 Table 9

|Examples of the Costs of Regulation in A<br>(in**Millions**of 1991 US**Dollars)**|rgentinaa<br>|l<br>|
|---|---|---|
||_Period_|_Average Annual_<br>_Cost_|
|Financial System<br>$ High reserve requirements and subsidized credit by the central<br>bank<br>$ Inflation taxes on checking accounts|1987<br>1983-1987|1,000<br>670|
|Fuel price controls|1977-1987|350|
|Health Services<br>$ Extra costs from double affiliation<br>$Idle capacity in public hospitals|1986<br>1987|150<br>172<br>l|
|Fishing<br>export subsidies|1986-1987|12<br>l|
|Efficiency costs from domestic consumption restrictions in cattle<br>markets|1984|104|
|Efficiency costs of the special fund for tobacco|1987|30|
|-Air transport regulations|1988|75|
|Restrictions on rail transport of cement, wine and grain|1987|95|
|Truck transport|1987|100|
|$ Costs of road deterioration<br>$ Costs of provincial regulations in the transport of grains|1987|30|
|Port restrictions on price and entry|1987|90|
|Regulations imposed on business|1965-1987|1,200|
|Regulations on employment in the public sector|1987|120|

a The costs of regulation presented in this table measure different concepts, such as efficiency losses in the economy, cost premiums to consumers, tax reductions and subsidies. Thus, it might not be technically correct to total them.

Source: Fundacion de Investigaciones Economicas Latinoamericanas (1991)
# Figure 3.2 RATE OF CHANGE OF RER IN SUDAN (Actual, Fitted and Forecast)

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job. We have dealt with this as follows: respondents interviewed between September and December (respectively, between January and August) have been classified as ̳recently employed informally‘ if they last worked (informally) within the same year (respectively, within the same or the previous year). In most countries, the core period of field work was between September and March, so that in 21 out of 30 countries the average reference period deviated from 12 by no more than 2 months; in 6 countries it was about 15 months, and only in Latvia and the Czech R. it was close to 18 months. Moreover, for each of the four European regions the average is close to 12 months: 11 months for the North, 11.6 months for the West, 12.6 months for the South, and 14.6 months for the East.

Based on these reference periods, the estimated size of currently non-employed population engaged in informal employment during the 12 months preceding the 2008-2009 round of the ESS is 3.5% of current extended labor force, ranging from 1.5% in the Nordic countries to 3% in the West to 4% in the East; for Southern Europe, the population-weighted estimate is 3.6%, but the equally-weighted one reaches 5.5% (Table 2, panel B, left). Note that the vast majority of these cases concerns dependent employment without a contract (rather than self-employment). When these estimates are added to the estimates of informal employment during the survey, the overall population-weighted (respectively, un-weighted) average estimate of population recently engaged in informal employment for the 30 countries covered is 19.2% (respectively, 21.3%) of the current extended labor force, or 11.4% (respectively, 12.8%) of the population aged 15+. The informality ranking of the four European regions remains unchanged: the highest prevalence of informal employment is found in the South (more than one quarter of extended labor force); in the West and in the East this proportion is one sixth, whilst in the Nordic countries it is between one ninth and one eighths (Table 2, panel B, right). Note these are _lower bound estimates_ , because respondents employed during the survey were not asked about their past activities.

Figure A1 (in Annex), based on results of ESS rounds 2, 3 and 4, summarizes main findings on the prevalence of informal employment in the four European regions for the whole period between 2004 and 2009.<sup>12</sup> Overall size of informal employment decreases from the South to West to East to North, but the median prevalence of dependent informal employment is higher in the East than in the West. For each of these country groups, the median (across space and time) level of informal self-employment is higher than that of informal dependent employment. The East and the North are much more homogeneous in terms of informal employment than the West and the South.

> 12 Like in all Figures hereafter, countries in Figure A1 are not weighted by population size.

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of the phone survey sample comparable to the full sample. These weights were applied in all our analyses to address potential biases due to systematic sample attrition and non-response (Wooldridge, 2007; Korinek et al., 2007). Nonetheless, to the extent that our results fail to control for this bias, our results may be taken as lower bounds of the actual impacts of the conflict.

# **2.3 Measuring Key Outcomes**

The paper focuses on three broad categories of outcomes that are observed both in the pre-war and post-war-onset phone surveys. These are: (i) the household food insecurity experience, (ii) Households’ access to food markets, and (iii) participation in labor market and livelihood activities. The 2019 face-to-face survey collected a wealth of information on household food security, employment and labor market participation, consumption, and other socio-economic characteristics that serve as baseline for the phone surveys. The phone survey also covered these topics which are of interest in this study, particularly household food security and participation in various livelihood activities. Because phone surveys cover a few modules, the surveys implemented similar questionnaires across rounds but followed a modular approach – some modules were dropped, and others kept or added in different rounds. Thus, we observe food security and labor market outcome indicators across multiple pre-war and post-war-onset rounds but not necessarily across all twelve rounds.

# **_Food Insecurity_**

Food insecurity is measured using the Food Insecurity Experience Scale (FIES), an experiencebased food insecurity metric developed by the Food and Agriculture Organization (FAO) of the United Nations that is widely applied to measure prevalence of food insecurity (FAO, 2014a).<sup>15</sup> The FIES relies on respondent’s direct responses to an eight-question survey module, referring to experiences of difficulties to access sufficient and nutritious food in the last 30 days. The FIES elicits responses based on whether the respondent or another household member (1) was _worried_ about having enough food to eat, (2) ate only a _few kinds_ of foods, (3) unable to eat _healthy_ and nutritious foods, (4) _ate less_ than should have eaten, (5) had _skipped_ a meal, (6) _run out_ of food, (7) was _hungry_ but did not eat, and (8) went without eating for a _whole day_ . A binary variable is coded for each question that takes the value of 1 if the answer is “yes” and zero if otherwise, and

> 15 <u>https://www.fao.org/in-action/voices-of-the-hungry/background/en/</u>

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