rafmacalaba/datause-encoder-data huggingface dataset:
index for the restrictiveness of U.S. quota policy on imports from Hong Kong under the MFA. Our sample consists of exports of twenty seven categories of textiles and apparel from Hong Kong to the U.S. over the six years 1983 to 1988. The choice of coverage was determined by the availability of data on Hong Kong export quota licence prices, p; for these we used data collected by Carl Hamilton supplemented by World Bank estimates. Data on export prices and quantities and U.S. tariffs in each category were extracted from the World Bank's MFF data base; and changes in real income for the two countries were measured by the growth rates in real disposable income.

Estimates of the price elasticity of U.S. demand for Hong Kong imports were not available. If we assume that imports from Hong Kong are perfect substitutes for other textile imports,30 the elasticity we require, c, equals the elasticity of U.S. demand for all textile imports. E. divided by the Hong Kong import share. The latter fluctuated around .15 during the period considered; the exact values are given in Table 1. We present results for three values of c, -0.5, -1 and -2. with the unitary case being the 31 literature's consensus.

The results are presented in Table 2. For each year and each value of **w** e give the changes in the uncompensated and compensated TRI's from the U.S. and Hong Kong points of view (using the formulae in equations (6.8) and (6.9)).32 These are compared with the changes in the average tariff equivalent, calculated in the conventional manner as a trade-weighted average of the implicit tariffs, pi. (Table I gives the levels of this measure.) 30 Strictly speaking, this is inconsistent with our assumption of general equilibrium separability. Since the results turn out to be relatively insensitive to the elasticity value assumed, this is not perhaps a critical point. 31 See Trela and Whalley (1990).

32 The formulae for S are for local changes whereas the data refer to discrete intervals. To allow for this, the changes given are Divisia indices, calculated using the arithmetic averages of the parameters in two successive periods.

21
income by 28.6%. Their results show that electricity disruptions have clear economic implications for households. Similar results are found by Rao (2013) focusing on household enterprises in India. He uses a subset of 8,125 urban and rural households that manage a non-farm enterprise. While he primarily finds strong evidence for a positive income effect of connectivity to the grid, he also identifies suggestive evidence of a positive effect of better supply availability. He estimates that the benefit from the income effect of improving supply to 16 hours a day or more would reflect 0.1% of GDP on an aggregate level. In Pakistan, a study finds that the middle class pays the highest cost of electricity outages in relation to its income (Pasha & Saleem, 2013).

As explored in Zhang (2019), the benefits from electricity access are magnified if the access is reliable. In the three countries included in the study, Bangladesh, India, and Pakistan, access to electricity is associated with higher income and better social outcomes. But the impact is much stronger if the electricity is reliable (Figure 2). In some places and for some metrics, for instance the effect of electricity access on women employment in India and Bangladesh, benefits are two or three times larger if access is reliable.

**Figure 2: Impacts of access to electricity and network reliability on income and social outcomes in South Asia**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Increase in Increase in Increase in Increase in Increase in Increase in Increase in Increase in<br>per girl's women's per girl's women's per women's<br>capita income study time labor force capita income study time employment capita income labor force<br>participation participation<br>Bangladesh India Pakistan<br>Access alone Reliable access<br>Percent<br><!-- End of picture text --> _Source: Zhang (2019). Note: The effects of electrification on girls’ study time and the effects of power outages on women’s labor force participation in Pakistan are not estimated because data are not available._ The cost of outages is made up by different components whose importance depends on the context. In Pakistan, the total annual outage cost for households adds up to 6.7% of annual expenditure (Pasha & Saleem, 2013). The largest source of this cost is the cost of self-generation – making up 56% of total costs. Other costs include monetization of utility cost and foregone economic activity due to outages that account for 22% of the cost in the whole sample. Disaggregating the wealth dimension draws a slightly 6
The effectiveness of public interventions in the market for apprenticeships, and ultimately the rationale for reforms, depends on successfully addressing market failures faced by youths or firms. The creation of a formal apprenticeship system may induce youths to forgo other human capital investments, such as traditional apprenticeships, or employment opportunities. Beyond effects on youths, the cost-effectiveness of public apprenticeship schemes in part also depends on indirect effects, such as whether they benefit firms. For instance, it hinges on the absence of negative substitution effects on private traditional apprentices, meaning that there is unmet demand and absorptive capacity for apprentices in firms. However, impacts on firms, such as substitution effects on the hiring of other apprentices, are rarely quantified. The literature on the benefits and costs of apprenticeships is extremely thin, and mostly concentrated in a few high-income countries such as Germany or Switzerland (for a review, see Lerman (2014, 2017)). The lack of evidence in developing countries is particularly striking given the prevalence of apprenticeship as one of the most common types of training, its mostly private nature, and the importance it is given in skills development or youth employment strategies. Most existing studies focus on access and returns for youths, with little evidence on indirect effects on firms, and little distinction between traditional and formal apprenticeships.

# **2.2 The Côte d’Ivoire Formal Apprenticeship Program**

After steady economic development through the mid-1990s, Côte d’Ivoire entered a period of conflict, punctuated by a post-electoral crisis in 2010-11. Stability returned after the institution of a new government in 2011, and economic performance has improved since then. A range of public investments and programs were launched in 2011. They included an emergency youth employment and skills development project (PEJEDEC), which had an objective to improve access to temporary employment and skills development opportunities for young men and women in Côte d’Ivoire.<sup>6</sup> Among other interventions, the project included an apprenticeship component.<sup>7</sup> The PEJEDEC apprenticeship component is overseen by the office coordinating employ-

> 6PEJEDEC: Projet Emploi Jeune et Développement des Compétences (www.pejedec.org).

> 7See Bertrand et al. (2017) for evidence on the cost-effectiveness of a public works program supported by the same project.

8
|**Table 1: Base regr**|**ession results**|||
|---|---|---|---|
|Dependent variable: Quality<br>**(1)**|**(2)**|**(3)**|**(4)**|
|Certificate Y:1 N:0||||
|**Panel A: Log o**|**dds ratios**|||
|Informal Competition (CY avg.)<br>2.104**|3.056***|3.276***|3.361***|
|(0.819)|(0.780)|(0.708)|(0.737)|
|GDP per capita (logs)|-0.075|0.272|0.471|
||(0.924)|(0.892)|(1.268)|
|Labor Productivity (logs)|0.485***|0.438***|0.411***|
||(0.117)|(0.127)|(0.133)|
|Number of workers (logs)|1.182***|1.133***|1.007***|
||(0.186)|(0.185)|(0.185)|
|Age of firm (logs)|0.296**|0.325**|0.350**|
||(0.140)|(0.146)|(0.141)|
|Manager experience (logs)|-0.281|-0.252|-0.254|
||(0.176)|(0.186)|(0.184)|
|Herfindahl index||-0.287|-0.348|
|||(1.026)|(0.994)|
|Exports (proportion of sales)||0.864**|0.619|
|||(0.394)|(0.391)|
|Foreign ownership (%)||0.013***|0.013***|
|||(0.004)|(0.004)|
|Women top manager Y:1 N:0||-0.238|-0.279|
|||(0.329)|(0.307)|
|Annual employment growth (%)||-0.008|-0.008|
|||(0.007)|(0.006)|
|New Product Y:1 N:0|||0.062<br>(0.204)|
|Line of Credit Y:1 N:0|||0.001|
||||(0.002)|
|Number of power outages (per|||-0.017|
|month)|||(0.019)|
|Foreign Technology Y:1 N:0|||0.008***<br>(0.002)|
|Website Y:1 N:0|||0.006***|
||||(0.002)|
|Total population (logs)|||0.687|
||||(4.563)|
|Industry fixed effects<br>Yes|Yes|Yes|Yes|

39
<mark>each year to reflect key data that is relevant for civil society at large and are developed in partnership with domain experts, including organizations championing open data in their respective fields (see annex table A6 for most recent data categories). Each data set is evaluated using a set of questions in a survey that examines the openness of the data sets. Each survey question measures a crucial aspect of either the legal, technical, or practical ‘openness’ of data (see annex table A7 for detailed questions and scoring guidelines). As shown in annex table A7, GODI survey questions check different aspects of data access and usability. The scoring guidelines for GODI’s survey questions are such that fairly high scores may not always mean open data, but access-controlled data, or public data in poorly structured, or not machine-readable formats. Specifically, GODI does not add many filters, such as exclusively considering data that is machine-readable - even though it might give a more realistic image of open data. For example, budget data can be assessed in PDF form which may be in public domain, available online for free, but in a format making it practically unusable. This data is presented as 80% open. The score suggests a fairly high degree of openness, but in fact, the data is not open. Only 100% means that the data is open. GODI claims that this approach, seeks to demonstrate which data is already available and how it can be further improved.</mark> In comparison to the ODB and GODI, the ODIN has comprehensive country coverage and covers the most MENA countries. It <mark>is also easier to understand b</mark> ecause its measure of data accessibility credits data when it is machine readable as opposed to the GODI that assigns scores to data in formats like PDF which is not really open at least from the perspective of a researcher seeking machine readable data for analytical work. However, ODIN’s methodological guide mention that the terms “data,” “statistics,” and “indicators” are used interchangeably.<sup>27</sup> These three terms are clearly not synonyms, as data in the ODIN context does not include microdata. ODIN’s measure only captures access to generated statistics and indicators. Hence it is possible that ODIN suggests data accessibility where in fact there is no access to microdata. <mark>In the next section, we discuss recent ODIN scores for MENA vis-à-vis the results of microdata accessibility presented in section 3.</mark>

# 4.2 Performance of MENA countries on the ODIN

Countries in the MENA region perform rather well on the Open Data Access Indicator (ODIN). As shown in figure 2, in the 2018/2019 ODIN, <mark>MENA generally does better than Sub-Saharan Africa, and is on par with South Asia, Latin America and East Asia and the Pacific.</mark>

> 27 See https://docs.google.com/document/d/1MBK0hN6MoQrii7_E1bmRXmsUcE8Fbb-Q32nxm8d8qTw/edit Accessed 18 October 2021.

19
residency, which has data on bank cross-border claims in each instrument. This data only covers banks resident in BIS reporting countries, and so is more limited in terms of coverage

# **2.2 Coverage of the New Dataset**

We divide the countries into three groups by level of development: Advanced, Emerging, and Developing.<sup>31</sup> In our sample of annual capital inflows, we have 89 countries (25 advanced, 34 emerging, 30 developing). We exclude financial centers (e.g. Panama, Hong Kong, Bermuda) to avoid distorting the patterns in the data. At the quarterly frequency, our inflow sample drops to 85 countries, leaving off El Salvador, Mongolia, Montenegro, and Serbia. For the regression and correlation analysis below where we use quarterly GDP, our sample is further limited due to unavailability of quarterly GDP for many emerging/developing countries. The outflow sample consists of 31 countries (15 advanced, 16 emerging) at a quarterly frequency spanning 2004q1-2014q4. For the annual data, we have 31 countries (13 advanced and 18 emerging) spanning 2002-2014. Details on the samA.4. ples are in Appendix We are unable to make the outflow sample as large as the inflow sample because data on liabilities owed is more widely reported than data on assets owned, so we do not have many comparable filling series to replace missing outflows values in the BOP. Thus, while our efforts do improve our coverage of outflows, we focus on the contribution to inflow coverage in this section.

Table A5 illustrates the impact of our data filling exercise on sample coverage for in-

> 31We rely on the 2000 IMF WEO classification to define the group of advanced economies. Generally, the WEO does not divide emerging and developing countries into separate groups. We use the MSCI and IEO-IMF classifications to guide the definition of our EM group.

17
# **Table A3 Balance by Ebola-Related Disruption, Village Characteristics Means, clustered standard errors from OLS regressions in parentheses P-value of t-test of equality of means with relevant control group in braces**

||**Ebola**<br>**Disruption: Low**|**Ebola Disruption:**<br>**High**|**Difference**|
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
|**_A. Village Characteristics_**||||
|**Number of villages**|166|34||
|**Number of dwellings**|135|60|{.191}|
||[287]|[49.0]||
|**Number of sampled adolescent girls**|28.3|28.8|{.328}|
||[11.1]|[6.08]||
|**Poverty score (Mean across households)**|.345|.359|{.035}|
||[.053]|[.059]||
|**Number of pre-existing NGOs**|3.08|2.44|{.252}|
||[1.92]|[1.81]||
|**Christians as a share of village population**|.153|.075|{.133}|
||[.230]|[.095]||
|**Distance from Freetown (miles)**|56.6|33.5|{.271}|
||[24.6]|[11.8]||
|**Distance from Kailahun (miles)**|75.41|93.8|{.597}|
||[18.5]|[10.9]||
|**Distance from nearest PHU (miles)**|1.81|2.13|{.453}|
||[1.70]|[1.96]||
|**Distance from nearest secondary school (miles)**|4.32|2.77|{.578}|
||[5.00]|[2.72]||
|**_B. Village Leader Survey_**||||
|**"Girls who are visibly pregnant have a bad**||||
|**influence on their non-pregnant peers"**|.970|.941|{.413}|
|**[=1 if strongly agree]**||||
|**"Girls should be allowed to continue their**||||
|**education while pregnant"**<br>**[=1 if strongly agree]**|.096|.029|{.045}|
|**Pregnant girls allowed to sit exams in the nearest**<br>**secondary school**|28.3|37.9|{.938}|
|**_C. Policy Responses_**||||
|**Village was quarantined**|.030|.118|{.119}|
|**Village visited by contact tracing team**|.928|1|{.088}|
|**Received Relief from NGO**|.849|.853|{.103}|
|**Village received food aid**|.199|.353|{.986}|
|**Village received school supplies (excl. BRAC)**|.355|.500|{.690}|
|**Received Relief from Government**|.687|.706|{.140}|

**Notes:** Data sources are the Village Census (Panel A), and the Village Leader Surveys (distance measures in Panel A, all outcomes in Panels B and C). Column 3 reports p-values from a test of equality of means carried out by OLS regression of each characteristic on a dummy for assignment to treatment. All regressions include strata (district) dummies and calculate robust standard errors. The Poverty Score (PPI) is calculated through scorecards and its value, ranging from 0 to 100, represents the likelihood of a household being below the poverty line. The number of pre-existing NGOs includes all organizations apart from BRAC. Distance from Freetown and Kailahun are computed from GPS data.
# Introduction

Consumer subsidies in the Middle East and North Africa (MENA) region are widespread. All of the countries in the region administer energy subsidies, and most countries administer food subsidies on at least a few items. These subsidies are important for households in that they constitute a sizable part of household expenditure and represent an important share of governments’ expenditure or forgone revenues. Consumer subsidies are also larger in this part of the world compared to other regions (Clements _et al._ 2013; Sdralevich _et al._ 2014) and they are more heterogeneous in many respects. The initial origins, types, profile, administration, and cost and beneficiaries of subsidies vary significantly across the countries of the MENA region. This heterogeneity makes comparisons across countries more complex, but also provides an opportunity to derive lessons on subsidies and subsidy reforms.

This paper aims at comparing the distribution of subsidies and the impact of subsidy reforms on welfare across countries in the MENA region. Specifically, the paper does this offering a standardized analysis of consumer subsidies in 2014. We use household budget survey data for five selected case studies and standardize the key variables for the analysis, including expenditure per capita on individual products and a basic set of household characteristics. We also update all surveys to 2014 using information on production, prices, and population growth and transform all values in purchasing power parity (PPP) using the latest round of the PPP survey (2011). We then use a version of the microsimulation model “SUBSIM,” (www.subsim.org) which is designed to make comparisons across countries, to provide a comparative distributional analysis of subsidies and simulations of subsidy reforms. This version of the software is designed to compare individual products across countries and allows researchers to see how any two countries compare in the distribution of subsidies and in the outcomes of subsidy reforms. In this way, we are able to simulate the same subsidy reforms in different countries and compare the outcomes across countries in terms of household welfare and government revenues.

The countries considered are Libya, Morocco, and Tunisia for North Africa and Djibouti and the Islamic Republic of Iran for the Middle East. The combined populations of these countries is 130 million or about 34 percent of the population of the MENA region. The sample includes net oil exporters such as the Islamic Republic of Iran and Libya and net oil importers such as Morocco 2
## 2. Development implications of migration for the origin countries

Migration is a decision that impacts the welfare of the household, the home community, and in the end the whole economy in various ways (Azam and Gubert 2006). The welfare implications of migration on the origin country are most often, though not always, sizable and positive. The main channels through which migration alleviates poverty are increased incomes from remittances, ability to smooth consumption, access to finance for starting a new business, as well as tapping on to the knowledge and resources provided by the international community of the migrant diaspora. Besides pure monetary gains, migration and remittances allow for higher investment in health care and education. Yet, not all impacts are positive: exploitation of migrants by unscrupulous recruiters or employers is reportedly widespread; separation from family can be stressful for migrants; and large scale immigration can pose serious challenges to a nation’s identity and sovereignty.

# **_2.1 Economic implications for the country of origin_**

While migration has economic, social, and cultural implications for the sending and host societies, remittances the migrants send home are perhaps the most tangible and least controversial link between migration and development (Ratha 2007). According to the official estimates, migrants from developing countries sent over $315 billion to their origin countries in 2009, three times the size of official development assistance (Ratha _et al._ 2010) _._ The true size of remittances including unrecorded flows through formal and informal channels is likely to be even higher. While remittances to developing countries declined modestly in 2009 because of the global financial crisis, these flows have remained resilient compared to private capital flows, and have become even more important as a source of external financing in many developing countries.

Migration and remittances have both direct and indirect effects on the welfare of the population in the migrant sending countries.<sup>2</sup> A cross-country study of 71 developing countries found that a 10 percent increase in per capita official international remittances will lead to 3.5 percent decline in the share of people living in poverty (Adams & Page

> 2See World Bank (2005), UNDP (2009), Skeldon (2002), Kothari (2002), and De Haas (2005).

3
Table 5: Price of Japanese Rice, 1996

|Brand name|Price<br>(yen/60kg)|Index (standard=100)|
|---|---|---|
|Uonuma Koshihikari|31,779|194|
|Niigata Koshihikari|25,359|155|
|Standard Domestic|16,392|100|

Source: Japanese Ministry of Agriculture and Fisheries.

rice. Thus, Japanese consumers perceive a high degree of product differentiation in rice and are willing to pay a huge premium on a certain brand of rice.

The emergency rice import in 1993-94 is another example of how rice is differentiated in the eyes of the Japanese consumers. The summer of 1993 in Japan was unusually cool and the rice harvest that year was only 74 percent of the normal harvest. To cope with a possible food shortage, the Japanese government decided to import 2.59 million tons of rice from Australia, China, Thailand, and the United States. However, Japanese consumers did not like the idea to purchase foreign rice. Housewives waited patiently for several hours in line to obtain scarce domestic rice even though foreign rice was readily available at a cheaper price. Faced with the unpopularity of foreign rice, the government sold it as a package with domestic rice. In other words, consumers were forced to buy a certain amount of unwanted foreign rice in order to buy the domestic rice they really wanted. In spite of such a desperate effort by the government to sell foreign rice, about 1 million tons (or 38 percent) of the imported rice remained unsold, and the government was obliged either to ship it back to foreign countries as food aid to the poor under official development assistance (ODA), or to feed it to animals.

13