rafmacalaba/datause-encoder-data huggingface dataset:
Islamic microfinance. Leading countries for Islamic microfinance in MENA include Lebanon and Yemen (CGAP, 2008). Yet IFC-commissioned market studies in the MENA region suggest a strong demand for Islamic microfinance products. Between 20 and 60 percent of those interviewed (microenterprises, low income individuals) indicated a preference for Shariahcompliant products. For some the lack of Shariah-compliant products is an absolute constraint to financial access, while for others this is a preference and they continue to use conventional financial services in the absence of competitive Islamic ones.

**The range of Islamic finance products offered is narrow** - over 70 percent of the Islamic finance products offered are _murabaha_ (cost plus), which is a lending product typically used for working capital. Most MFIs in Yemen offer a _murabaha_ loan product, for example. However, there are some signs that this limited selection may increase: in Egypt, Bank Misr plans to introduce Islamic microfinance activities to its 33 Islamic branches, and also develop a _mudaraba_ (profit sharing agreement) product in addition to the _murabaha_ product; in Yemen Tadhamon Islamic Bank opened a MSE division in late 2006; and in the UAE Noor Islamic Bank and Emirates Post Holding Group announced plans to establish a company to offer Shariacompliant financial services to low income clients.

**Constraints to Islamic microfinance** are mostly related to i) the capacity of microfinance providers to design authorized Islamic finance products that meet the needs of clients, and ii) the need to keep costs low for micro-level transactions. Islamic microfinance providers globally are still in the process of identifying innovations and adaptations to Islamic finance that lower costs and enable them to offer sufficiently attractive pricing to clients.  Islamic microfinance can be more reliant on collateral to secure loans, rather than on cash flow-based financing techniques, and as such is more affected by weak collateral registration and enforcement regimes.  There can also be a stronger tendency in Islamic jurisprudence to give borrowers more time and opportunity to repay, rather than executing collateral.

# _2.3 Delivery mechanisms for financial services_

# **Bank branch networks are correlated with access to loans and deposit accounts** .

As shown in Figures 4 and 5, the number of deposit and loan accounts relative to the population in MENA is closely linked to the size of branch networks.<sup>11</sup> The number of deposit accounts per population does not seem positively related to the proportion of the branch network held by state banks (Figure 7) though, despite a widespread assumption that state banks have deeper penetration in areas not covered by private banks and therefore can contribute to overall gains in access.

> 11 As shown in Annex 1.4, the relationship between number of deposits and branches holds even after controlling for per capita income, although it should be noted that the regression results are only suggestive, given the very small sample, and the endogeneity of the branch variables.

14
likelihood of finding a new market or product unprofitable and exiting. Thus, the failure rate associated with entry would fall.

To explore how important these channels are, we define an entry as an export observation in a product line that was not exported in the previous year or as a new market for an existing product. A failure is defined as entry that exists for just one year, i.e. a new market/product was tried in year _t_ , but it is unsuccessful and does not appear in year _t+1_ . In order to avoid entries as a result of countries that begin reporting imports during the period—recall, we use mirror data—we use a balanced sample of 43 importers that report trade continuously from 1980 to 2005. These countries account for on average 82 percent of total manufacturing trade of the 182 exporters over the period. Using a balanced sample implies that we are likely to underestimate the importance of new markets, since some destinations are left out, though the importance of these markets is likely to be small in most cases, as these are small countries.

Figure 9 shows the median number of entries and median misalignment for developing and developed countries (graphs using means look similar). The extent of undervaluation is positively correlated with an increasing number of entries. Figure 10 shows the median misalignment and the median failure rate in developing and developed countries. The failure rate appears to be declining in the misalignment. This is consistent with the hypothesis that undervaluation generates a significant amount of entry and a lower failure rate. It is noteworthy that the failure rate is quite high, well over 50 percent. This is consistent with work by Besedes and Prusa (2007) that shows that entry is frequent, it is the duration of new exports that separates successful exporters, such as the countries of East Asia, with unsuccessful exporters.

16
However, we focus on the 2006 round of the VHLSS for the outcome variables, since this round has the most detailed information on household investment in tutoring activities. We also supplement our analysis with data from another nationally representative survey (VHTS) focused on private tutoring that we fielded in 2008,<sup>15</sup> as well as data on teacher qualifications in the community from the primary school census (DFA) database.<sup>16</sup> Since most children start their first grade at 6 years old, we restrict our analysis to children who are between 6 and 18 years old.<sup>17</sup> To address concerns about grown-up children that have already moved away from home, we consider only children who are living at home and households where the total number of children born of the same mother is equal to the number of children living in the household. We define family size as consisting of children born of the same mother, but we also experiment with a more relaxed definition of family size that considers all children living together in the households, as well as other stricter definitions to be discussed later.

# **3.2. Overview of family planning in Vietnam**<sup>**18**</sup>

Vietnam’s family planning policy dates back to 1961, when the government encouraged families to have between two and three children, with the aim of decreasing the annual population growth rate from 3.5 percent to 2.5 percent. Due to the Vietnam War and a lack

> on both commune and district names (in addition to province and district codes) for matching. We can match 96 percent of all the communes in 2002 to those in 2006 and 2008 (i.e., we can match 2,808 communes out of 2,933 communes in 2002).

> 15 For details on this survey, see Dang and Glewwe (2009).  We collaborated on designing the survey with other researchers, including Paul Glewwe (University of Minnesota), Seema Jayachandran (Northwestern University), and Jeffrey Waite (World Bank). The survey was administered by Vietnam’s Government Statistics Office, using funding from the World Bank’s Research Support Budget and the Hewlett Foundation.

> 16 This database is initiated and maintained by World Bank-supported projects. For a brief description on the history and objectives for the primary school census database, see Attfield and Vu (2013).

> 17 We also experimented with other age ranges such as ages 10- 18 and 12- 18. Estimation results (available upon request) are qualitatively very similar and even more statistically significant than those for the age range 6- 18.

> 18 This section is mostly based on GDPFP (2011).

10
Kong SAR, China and Singapore (Figure 1, Panel A).<sup>13</sup> By 2014, China’s market capitalization had become the second largest in the world after that of the United States.

The expansion of market capitalization coincided with increases in equity prices and issuances. The price index in China rose rapidly since 2014, significantly diverging from the indices in Hong Kong SAR, China and Singapore, despite sharing similar trends up to 2013 (Figure 1, Panel B). Moreover, the aggregate amount of equity raised in mainland China doubled between 2007-13 and 2014-20 (Figure 1, Panel C). While mainland China and Hong Kong SAR, China shared similar equity issuance trends before 2014, a clear divergence has occurred since then. The pattern of equity issuances suggests a significant impact of internationalization on domestic equity financing that has yet to be explored in the literature. We fill this gap by using a rich dataset on equity issuance activity.

# **3. Data and Methodology**

# **3.1. Data**

We merge transaction-level data on equity issuances with balance sheet data of domestic and foreign listed Chinese firms with residence and major business operations in mainland China. The transaction-level data come from Refinitiv’s Securities Data Corporation (SDC) Platinum, which provides detailed transaction-level information on new equity issuances during 1990-2020. The balance sheet and income statement data come from Worldscope. Lastly, we augment our merged dataset with firm-level data on ownership structure from Wind.<sup>14</sup>

> 13 One of the key internationalization reforms– the Stock Connect program – also affected the capital market in Hong Kong SAR, China. Indeed, part of the growth in the market capitalization in Hong Kong SAR, China, since 2013 could be attributed to Southbound trading activities from the Connect program. Nonetheless, the market capitalization in mainland China rose substantially more after 2013 (Figure 1, Panel A).

> 14 All value variables in our sample are in 2011 U.S. dollars. See Appendix Table 4 for a detailed definition of the main variables used in the paper.

9
0 l l 3 4 5F (4) iitt 1tt tt 2tt iitt iitt iitt iitt ii tt iitt VV = ββ + ββ WW + ββ HH + ββ FFCCFF + ββ FFCCFF + ββ CC + ZZ′ δδ + vv To estimate this last equation, we need data for each variable. Unfortunately, there are no direct values for each of them; thus, we use predicting equations to substitute them, where X is a vector of predicting variables that includes Z, then we replace each of the equations (5) through (9) in (4). Now, the equation to estimate the indirect utility is the following: 0 4 5 (10) iitt 1tt 2tt ii 2tt 2tt ii 3tt 2tt ii 2tt ii 2tt ii iitt VV = ββ + ββ αα XX + ββ θθ XX + ββ θθ XX + ββ ππ XX + ββ ρρ XX + φφ Where 0 1 1 1 1 1 and the composed error term. iitt iitt iitt iitt iitt iitt ββ = αα + θθ + θθ + ππ + ρρ φφ = ττ + ωω + εε + σσ + εε Given the complex interpretation of the multinomial, we refer to the marginal effects of the independent variable in our model, which are defined as

> 26 Most of this section is based on Long & Freese (2014) and (Trivedi, 2005)

12
Benefits provided via public service provision are significant for poorer households. The magnitude of a transfer received (tax paid) compared to pre-transfer (pre-tax) income level is the incidence of the transfer (tax). The benefits received via public education system access are equivalent (in magnitude) to disposable income in the poorest decile. In contrast, benefits received by the richest decile are equivalent to just 4 percent of disposable income. Meanwhile, the incidence of public health care benefits received by the poorest decile is 30 percent (of disposable income) whereas the public health care benefits received by the richest decile are a negligible share of disposable income.

Figure 14 and Figure 15 show concentration curves for health and education. If benefits were distributed equally among the population, the concentration curve would coincide with the 45-degree line (in blue). For instance, 50 percent of the benefits provided through health expenditures would accrue to 50 percent of the population if the concentration curve for health coincided with the 45-degree line. If the concentration curve lies above the 45-degree line, poorer households benefit more from the distribution of benefits. In contrast, if the concentration curve lies below the 45-degree line, poorer households capture smaller shares of the available benefits than their shares of the population.

Public health care expenditure concentration shares are less than population shares for poorer households. It is commendable that poor and non-poor households alike can access public health care services, and that poor households manage conditional access to the public health care system at the clinic level as or more frequently than do non-poor households. However, per-capita benefits overall are greater in richer households because of a higher likelihood of a higher-valued hospital visits among individuals in non-poor households (Figure 14).

**Figure 14. Health Benefits: Cumulative Figure 15. Education Benefits: Cumulative** **<u>Concentration Shares Concentration Shares</u>**

_Source_ : Authors’ calculations based on LCMS 2015.

Public education expenditure is pro-poor only at the primary levels. The distributional pattern is very prorich for early childhood education/pre-school, secondary school, and especially for colleges and universities (Figure 15). Households in the poorest 50 percent of the population receive almost none of the public tertiary education benefits available because these households contribute very few of the students enrolled in public tertiary education. Public education expenditures might look _more_ regressively distributed if poorer households contributed primary school students at the same lower rate as do non-poor households.

Cash Transfers Have Limited Coverage and Are Small The Social Cash Transfer Scheme (SCTS) is progressive but provides low coverage. Figure 16 shows that the bottom 40 percent of the population (ranked by Market Income + Pensions) receives nearly 70 percent 29
Policy Research Working Paper 10359

# **Abstract**

Online surveys are convenient, cost-effective, speedy, and increasingly popular instruments for data collection. This study investigates whether online surveys that used Random Domain Intercept Technology to recruit respondents were accurately measured labor market outcomes in six middle-income countries in the aftermath of the COVID-19 pandemic. Compared with the national average, online surveys oversampled males, youth, those with higher levels of education, and those in smaller households. Reweighting using propensity score estimates fails to equalize the means of variables excluded from the model. When comparing the employment-to-population ratio from the internet surveys to the most recent relevant nationally representative surveys, the average deviation is 30 percent. Reweighting using propensity scores in that case worsened the bias. Internet survey estimates of informal and self-employment rates also tend to be inconsistent with benchmark data, although the latter are available for fewer countries. Overall, the results suggest that despite the advantages and convenience of recruiting internet survey participants through Random Domain Intercept Technology, the resulting sample is not representative and even after propensity score reweighting, it can yield estimates that are at odds with nationally representative surveys.

This paper is a product of the Poverty and Equity Global Practice. 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 dnewhouse@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
<mark>that agricultural production per capita rises with income; that the shape of the production curve is concave, but the curvature is much flatter than that of the consumption curve, revealing close to a linear relationship between income and production; and that countries with higher land endowments tend to produce more food given the same level of income. The figure also suggests that land abundant countries such as the United States tend to produce more food than they consume and to be exporters of food at almost all income levels. In contrast, land-scarce countries such as Japan are likely to consume more food than they produce, being food importers throughout their income levels. For relatively land scarce countries such as China, the concavity of the consumption curve implies that their consumption growth may be faster than production growth for a wide range of income, which in turn may contribute to rising net imports.</mark> <mark>Some scholars view the role of agricultural trade as a way of</mark> shifting agricultural resources such as land and water from resource-abundant to resource-scarce areas (e.g., Chapagain, Hoekstra and Savenije 2006; <mark>Fader, Gerten, Krause, Lucht and Cramer 2013; Q</mark> iang, Cheng, Kastner and Xie 2013 for China; and <mark>Wichelns 2001 for the Arab Republic of Egypt</mark> ). For instance, calculating the ‘virtual’ land use (i.e., resource flows hidden in traded products) embodied in China’s agricultural trade, Qiang _et al._ (2013) find that China’s massive imports of land-intensive soybeans from land-abundant countries such as the United States and Brazil made China’s domestic cropland areas available for staple foods. Headey (2016) reports that the global agricultural land distribution has shifted towards land-abundant developing countries between 1961 and 2012 (Figures 1-2) and suggests that there may be a continued shift in agricultural production towards land-abundant countries with a comparative advantage in land-intensive goods.

# Projections to 2050

Using the intermediate PPP GDP<sup>16</sup> and population projections for 2030 and 2050 from the SSP database (SSP2) (Leimbach _et al_ . 2017), we perform simulation exercises to estimate how CE consumption and production might evolve in the future. We use projections of arable land kindly provided by Jelle Bruinisma, which reflect the detailed estimates underlying Alexandratos and

> 16 One caveat in our analyses is that we rely our GDP projections on the SSP database and do not consider the potential growth slowdown identified by recent research (e.g., Laborde and Martin 2016; World Bank October 2016 for SSA).

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Among all road links that form the Trans-African network, median traffic growth after upgrading is 182% at the median, 447% at the 75<sup>th</sup> percentile, and 1,295% at the 90<sup>th</sup> percentile.

# **5.  Network Upgrading Cost**

We have used the World Bank’s Road Cost Knowledge System (ROCKS) to estimate the costs of upgrading and maintaining the 83-city road network described in this paper.<sup>32</sup> Table 8 provides detailed information from ROCKS for 470 upgrading and maintenance projects in Sub-Saharan Africa.<sup>33</sup> Estimated unit costs ($US/kilometer) vary widely by type, both within and across categories. For 170 upgrading projects, the median unit cost is approximately $127,800 per kilometer (Table 9), but median cost by work type varies from $55,000/km for asphalt mix resurfacing to over $1 million/km for new highway construction. For 300 maintenance projects, with median unit cost of $5,600 per kilometer, work types vary from $500/km for grading to over $20,000/km for surface treatment resurfacing. Table 10 provides information on the scale of the network to be upgraded and maintained. Its total length, calculated from the geographic database of road links, is 108,820 kilometers, with well-maintained, paved highways in South Africa accounting for 9,802 kilometers. West Africa has 32,248 km in the network, Central Africa 29,274, East Africa 24,146 and Southern Africa 13,350.

Sub-Saharan countries exhibit wide variations in institutional, economic, topographic and climatic factors that may have significant effects on the cost of road

> 32  The ROCKS website address is <u>http://www.worldbank.org/transport/roads/rd_tools/rocks_main.htm</u> 33   African countries in the ROCKS road project cost database include:

> West Africa: Chad, Ghana, Guinea, Mali, Nigeria, Sierra Leone Central Africa: Zambia East Africa: Madagascar, Malawi, Tanzania, Uganda Southern Africa:  Botswana, Zimbabwe

26
from IHS3. After restricting to individuals for which all variables are non-missing, our analysis is carried out on a balanced sample of 11,410 individuals.

We construct three measures of food insecurity that are typically interpreted as being measures of food access: daily per capita caloric intake, undernourishment, and the food consumption score (FCS),<sup>9</sup> which captures dietary diversity.<sup>10</sup> The data on caloric intake come from a 7-day recall module on household-level food consumption; undernourishment is a binary outcome indicating whether an individual consumes less than 2,100 calories/day. In addition to standard survey modules on household characteristics, assets, income, and consumption, the survey contains a module on experiential assessment of food security. This module includes several questions which ask whether households have worried about having sufficient food (e.g. _In the past 7 days, did you worry that your household would not have enough food?_ ), and whether they have experienced food insecurity (e.g. _In the last 12 months, have you been faced with a situation when you did not have enough food to feed the household?_ ).

To create our core panel data, we merged in an external food price data set that forms part of the Agricultural Market Information System (AMIS) collected by the Planning Department of Malawi’s Ministry of Agriculture and Food Security (MoAFS). Price data were collected weekly from 72 markets located in Malawi’s 26 districts and aggregated to the

> 9 The FCS considers dietary diversity, frequency of food groups consumed, and relative nutritional value of each food group consumed in the last 7 days (FAO 2008). The potential score range is 140. The higher the FCS, the better the diversity of the household’s food intake and, subsequently, the better the quality of the members’ diets. In categorizing households, those with scores less than 21 are determined to have “poor” dietary diversity; those falling between 21 and 35 are said to be in a borderline range; and those with scores above 35 are considered to have acceptable levels of dietary diversity (Weismann and others 2009).

> 10 Dietary diversity is a concept that captures the degree to which households or individuals consume a variety of foods. Along with caloric intake and undernourishment, dietary diversity can help generate a greater understanding of a population’s food insecurity (Ruel 2003). Research also suggests that dietary diversity can be used as a proxy for household income, household-level access to food, macronutrient, and micronutrient intake (see Hatloy et al. 2000; Anzid et al. 2009; Hoddinott & Yohannes, 2002; Rah et al. 2010).

5