rafmacalaba/data-use-mentions-extended huggingface dataset:
availability of schools) and is largely determined by nonmarket mechanisms (including government allocation of educationl services) imply that education levels are not necessarily highly correlated with abilities. That is, the limited role of the market in allocating education may imply large divergence in the value of the marginal products of education across individuals, beyond differences that could be explained by differences in ability.

The importance of these imperfections in the allocation of education and the consequences of such misallocations for aggregate income are examined in this section. We first set up a theoretical framework that integrates asset distribution in the derivation of an aggregate value added or GDP function yielding a structural empirical model. Next we estimate such a model for a group of middle-income developing countries using panel data for 1970-94.

# Consider the following production function of individual i,

## yi = Ak[Paih

(1.1) where yi is value added of individual i, _A_ is the coefficient of total factor productivity associated with the overall conditions of the economy, _ki_ is the share of total capital that corresponds to the individual, ai is a parameter reflecting the level of ability of individual i and hi is the level of education of individual i. The variable k represents the tradable factors that have equal marginal products across individual. Thus we assume that k can be aggregated without consideration of its distribution across individuals.

10
2018, Clemens, Lewis and Postel, 2018, Mobarak, Sharif and Shrestha, 2023). Information on country or region-specific bilateral or multilateral agreements is often more readily available whereas aggregate global analysis on the impact of labor agreements has been limited due to the lack of global datasets documenting these bilateral and multilateral treaties. This has changed recently with the emergence of large datasets on BLAs (Chilton and Woda, 2022, Chilton and Posner, 2018, Peters, 2019 _b_ ). Using these datasets, new and emerging research has examined the relationship between BLAs and trade flows between countries (Maximova and Paraschiv, 2022), the relationship between cross-border property rights and innovation (Bian, Meier and Xu, 2023) and emigration and democratization (Peters and Miller, 2022). However, to our knowledge, no paper has so far documented the impact of BLAs on migration flows between countries. Both Peters (2019 _b_ ) and Chilton and Woda (2022) find positive association but do not document the impact rigorously.

This paper relates to literature on how explicit policies related to migration affect migration. A few papers examine the role of policies such as border walls, minimum wage, passport and other legal hurdles in facilitating or hindering migration (Allen, de Castro Dobbin and Morten, 2018, Feigenberg, 2020, McKenzie, 2007, McKenzie, Theoharides and Yang, 2014). Yet, another set of papers study the impact of specific migration restriction programs (Clemens, Lewis and Postel, 2018, Di Iasio and Wahba, 2023, Mayda et al., 2018). In a similar vein, Ortega and Peri, 2013 examine the relative effects of income and a broader set of immigration policies on immigration to the OECD countries and find that restrictive policies can rapidly reduce migration. While the literature has largely examined the role of restrictive immigration policies, this paper examines the impact of a migration facilitation policy.

The paper also adds to the large literature on relieving constraints to migration more broadly. Studies have reconciled the high
|0.004**|-0.000**|-0.000|0.001|
||(0.003)|(0.000)|(0.002)|(0.000)|(0.000)|(0.002)|(0.000)|(0.000)|(0.002)|
|Eligible Child|0.016*|-0.001*|-0.007|-0.000|-0.001|0.001|-0.001|-0.001|-0.001|
||(0.008)|(0.001)|(0.005)|(0.000)|(0.001)|(0.006)|(0.001)|(0.001)|(0.005)|
|FDR q-value|0.738|0.738|0.738|0.738|0.738|0.738|0.738|0.738|0.738|
|Observations|121,718|121,718|121,718|121,718|121,718|121,718|121,718|121,718|121,718|
|Mean|0.194|0.001|0.095|0.001|0.004|0.188|0.002|0.005|0.124|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dummy for being employed in a certain industry category indicated in column heading, conditional on being employed, on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district,
>10</sup> Institutions such as the WBG concentrate their activity to particular airlines that rely on particular plane types and model (and thus not a random selection from the global vehicle fleet), where also average load factors may differ from overall global averages. A more accurate calculation of each institution’s footprint then requires that these numbers be calculated separately, for the actual air travel by the institution in a given year.

- 4) As we have already stressed, the climate forcing effect of emissions from airplanes is likely to differ from that resulting from ground-released carbon emissions due to the altitude at which they are released and to the fact that several types of emissions in addition to carbon are involved. Including such factors is still not standard in carbon footprint accounting, and the factor is at the moment very uncertain. We do not further pursue this issue in this paper, however.

> 9 Keen and Strand (2007), table 12, presents data on global averages by travel class in 2004, the last year for which we have good data.

> 10 The differences in fuel consumption by plane type and vintage can be substantial, and vary by a factor of 3 or more.

5
changes in the community, asking respondents whether the access to transportation became better or worse compared to five years ago and to describe the relevant events or programs happened in the community during the past five years. An example of the module of community needs, actions, and achievements is given in the Annex 7.

Even if most LSMS-tagged surveys do not have community questionnaires, several questions are inserted into some of these countries’ household questionnaires to collect the information on transport conditions in communities, for example, there are some questions on roads and public transport conditions in communities in the household questionnaires of Afghanistan, Cameroon, and Sierra Leone.

# _Identifying a set of recurrent questions_

We identify a set of common questions that are recurrent across surveys. There are five topics for which we could identify recurrent transport-related questions: roads, public transport, changes of transportation, governance, and the availability of needs and actions section.

The road topic includes the following questions:

1. Main type of road surface

2. Distance to the nearest tarred/asphalt road 13
change potential of black carbon. They note in particular that China and India alone account for 25-35% of global black carbon emissions. Providing energy efficient and smoke-free cookers, along with reducing soot emissions from coal combustion in small industries, could reduce warming from black carbon in East Asia by 70-80%.

To illustrate the role of developing countries for such off-grid technologies, Table 2 presents total patent counts based on inventor country for four types of cooking stoves whose primary applications are in developing country markets.<sup>15</sup> In all four cases, China is the leading source of patents, ranging from 46% to 89% of the total for each technology. Among developing countries, we also include data for India and South Africa, as these countries are important sources of scientific publications, to be discussed below. However, neither is an important source of patenting. This may be because these countries are doing less commercial work on these technologies, or because intellectual property rights are not seen as valuable in India and South Africa.

Because developed country research on indoor cooking stoves may be led by noncommercial entities such as universities or non-profit foundations, which are less likely to patent successful research outcomes, Table 2 also includes counts of scientific publications. Even here, a developing country is the leading source of articles for three of the four stove technologies.

> 15 These patents were identified using a combination of keyword and patent classification searches using the Delphion on-line patent database.  Countries are identified using the first inventor listed on the patent.  If no inventor country is limited, the first priority country (e.g. the country where the application was first filed) is taken as the source of the invention. Scientific publication data are collected using a keyword search of abstracts and titles in the Web of Knowledge database.  Here, the source country comes from author affiliations. For multiple authored papers, affiliations are counted for each country, so that the total number of affiliations may exceed the total number of articles.
seek to capture the existence of foundational laws, the robustness of these laws, and data governance administration. These additional details comprise the value this diagnostic provides compared with efforts like UNCTAD’s e-transaction database and DLA Piper’s data protection law tracker. Besides collecting information on the existence of any e-commerce and data protection laws, ATI, or open data legislation, the diagnostic also measures the robustness of such laws to assess whether they contain specific provisions or regulatory attributes. Moreover, in order to capture information on the implementation of those regulatory frameworks, the diagnostic also includes a few questions on administrative aspects of data governance, such as any digital ID systems for e-services, key institutions such as CSIRTs for cybersecurity and data protection authorities, common technical standards to ensure government system interoperability, mandatory data classifications used across government systems, and so on.

The diagnostic results show that although many countries have established various data regulations, those regulatory frameworks are not extensive enough or sufficiently aligned with good regulatory practices to ensure that the legal framework can create an enabling environment for the data economy (figure 10). Lowincome countries in particular are weak on technical infrastructure or institutional settings that enable robust data governance, such as the existence of digital ID system for e-services, mandatory use of common technical standards to ensure government system interoperability. In comparison, high-income countries show a high score on the administrative aspects of data governance even though some legislation is not robust enough with a full range of regulatory good practice incorporated. This reflects quite a pragmatic approach in exploring the most appropriate regulatory framework for their countries.

> 23 See art. 438 of the Digital Code Act, accessible at: https://sgg.gouv.bj/doc/loi-2017-20/

> 24 Additional information available at: https://www.canada.ca/en/revenue-agency/services/e-services/cra-login-services/sign- <u>partners-help-faqs/using-a-sign-partner.html</u>

24
53|55|0.1|1.5|6.8|
|Kyrgiz Republic|67|60|57|0.0|7.8|8.6|
|Tajikistan|60|54|48|0.3|2.6|2.5|
|Uzbekistan*|68|64|60|0.1|0.4|0.4|
|CIS-7 (average)|65|58|55|0|4|5|

Source: CIS-Stat (1998 and 2004). (*) The Unemployment Rate (UR) is neither registered nor open unemployment. It is calculated as a difference between labor resources and employment and inactivity (UR=LR-E-NA). Labor resources is a concept heritage of the Soviet Statistical system and still in use among CIS statistical agencies. It includes the labor force (employed and unemployed) and the economically non active in working age The economic decline deeply affected _state finances_ . Public spending shrunk in absolute terms and also relative to GDP and public debts surged qualifying five of these countries for the CIS-5 debt initiative (Armenia, Georgia, Kyrgyzstan, Moldova and Tajikistan). On average, the CIS-7 general government spending declined from 37% of GDP in 1992 to 23% in 2002 with the decline continuing during the more recent growth period. Only Moldova managed to maintain the relative level of spending (with large annual fluctuations) while all other republics saw a net decline. Government deficits have been higher during the 1992-1994 period, in the range of 20-30% of the budget (Unicef TransMONEE database 2002) and these have been financed for the most part with loans from multilateral organizations. In 2000, debt service alone - as % of government revenues - ranged from 15.5% in Armenia to 41.9% in Moldova and total debts averaged for the CIS-5 at 73.5% of GDP.<sup>11</sup> By the time the recession ended in 1996, the
Policy Research Working Paper 9142

# **Abstract**

This paper studies how crises prompted firms to switch borrowing across markets, impacting the amount borrowed, maturity, and currency denomination at the firm and aggregate levels. Using data on worldwide debt issuance from advanced and emerging economies, the paper shows that firms shifted their issuances between domestic and international syndicated loans and corporate bonds during financial crises. Firms reduced their borrowing in shockhit markets but increased it in other debt markets. Firms also moved toward longer-term markets, maintaining (or even increasing) their borrowing maturity. As they moved toward domestic markets during international crises, firms reduced the share of foreign currency debt. The opposite occurred during domestic crises. Large firms were the ones that switched between international and domestic markets, affecting aggregate capital raising activity. The analysis of four distinct markets generates patterns consistent with credit supply shocks that are different from those obtained when studying the dynamics of individual markets.

This paper is a joint product of the Development Research Group, Development Economics and the Office of the Global Director, Finance, Competitiveness and Innovation 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 jcortinalorente@worldbank.org, tdidier@worlbank.org, and sschmukler@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
Systematic Reviews 2016: 1.

Rand, J., & Torm, N. (2012). The Benefits of Formalization: Evidence from Vietnamese Manufacturing SMEs. World Development, Elsevier, 40(5), 983-998.

Rijkers, B., Laderchi, C. R., & Teal, F. (2010). Who benefits from promoting small enterprises? Some empirical evidence from Ethiopia. World Development, 38(4), 523–540.

Sanguinetti, P. (2005), Innovation and R&D Expenditures in Argentina: Evidence from a firm level survey. mimeo Sekkat, K. (2011). Firm Sponsored Training and Productivity in Morocco. Journal of Development Studies, 47 (9), 1391–1409.

34
TABLE 3 Baseline panel regression results

||All Sectors|Manufacturing<br>Sector|
|---|---|---|
|Liner Shipping<br>|-0.2299***|-0.2271**|
|Connectivity Index|(0.007)|(0.007)|
|Logistics Performance<br>|-0.5004***|-0.5156***|
|Index|(0.032)|(0.356)|
|Tariffs|0.3449***|0.4265***|
||(0.044)|(0.057)|
|trade agreement<br>|-0.0487***|-0.0567***|
|membership|(0.006)|(0.006)|
|Trade policy uncertainty|0.0907**|0.0902**|
||(0.004)|(0.005)|
||0.2605***|0.2687***|
||(0.007)|(0.007)|
|Common border|-0.4070***|-0.4125***|
||(0.033)|(0.035)|
|Common language|-0.1516***|-0.1369***|
||(0.013)|(0.141)|
|Observations|56,038|52,060|
||0.569|0.569|

Source: World Bank.

Note: * p<0.05, ** p<0.01, ***p<0.001. Robust standard errors are shown in parentheses. The table shows estimated coefficients from a gravity panel regression estimated for up to 95 countries using annual data for
these road data with the boundaries of Mexican allows us Intersecting municipalities to obtain municipality-level panel data of differences in road length for four periods between 1993 and 2012, namely 1993-1998, 1998-2003, 2003-2008, and 2008-2012.<sup>9</sup> For each period, the differenced data capture the flows of new roads in each municipality. We are able to track different road classes from the AAA typology: multi-lane divided, two-lanes, divided, pavement, gravel, and earth. To illustrate the evolution of the road data over time, we show in Figure 1 the expansion of the road network for the four periods. The changes between 1993 and 1998 (panels A and B) occurred exclusively during the PRI presidency (1929-2000), while the changes between 2003 and 2012 occurred exclusively under the PAN presidency (2000-2012), as shown in panels C and D.

We present in Table 1 the length of new roads in kilometers built in each period. To Table 1: New Physical Investment by Road Category, 1993–2012

||Multi-lanes|Two Lanes|Divided|Pavement|
|---|---|---|---|---|
|1993–1998|2,259|421|514|2,541|
|1998–2003|1,814|786|1,189|3,647|
|2003–2008|9,697|1,452|960|1,606|
|2008–2012|434|169|911|1,553|
|Total|14,204|2,828|3,574|9,347|

_Notes:_ Authors’ calculations based on DeLorme (2014) intersected with the AAA maps for the years 1993, 1998, 2003, 2008, and 2012.

calculate these changes, we first calculated changes in each road category in each municand summed them In so we in road ipality up. doing dropped negative changes length that in some cases might
microcredit programs over a long period fared better than intermittent participants in terms of income and consumption gains and poverty reduction (Khandker and Samad 2013). A macro study, using cross-country data, reinforces the positive impacts of microcredit (Imai et al. 2012).

The second strand of studies, using randomized control trials (RCTs), finds that microcredit has mixed effects on poverty reduction.<sup>3</sup> Various studies show the positive effects of microfinance (Coleman 1999, 2006; Karlan and Zinman 2009; McKenzie and Woodruff 2008; de Mel, McKenzie, and Woodruff 2008), while others find no evidence for income or consumption gains (Augsburg et al. 2011; Attanasio et al. 2012; Banerjee et al. 2010; Karlan and Zinman 2011; Crépon et al. 2011). Summarizing the findings of several RCT studies, a critic states that microfinance does not end poverty despite all the hype (Roodman 2012).<sup>4</sup> Microfinance is also under attack for charging “exorbitant” interest rates (e.g., the nominal on-lending rate of Grameen Bank in Bangladesh is 20 percent, 7 percent higher than the commercial bank rate), which goes against its stated mission of poverty alleviation. Using anecdotal evidence, many critics cite microcredit’s high interest rates and inadequate benefits relative to the cost of borrowing as reasons for rising indebtedness among borrowers. Their argument is simple: If microcredit programs were so helpful, indebtedness should not have increased over time relative to asset accumulation; thus, microcredit is not a “miracle,” as its proponents would have us believe. However, one study observes that, while the interest rates of microfinance institutions (MFIs) are

> 3 RCT studies have their own methodological weaknesses and thus are not always necessarily better than non-RCT studies (Deaton 2010; Rodrik 2008; Ravallion 2012).  Because microcredit operations are small in scale, the timeframe for realizing their impacts may exceed 1 or 2 years, which
where _a_ is an increasing function. This assumption captures human capital externalities as formalized, for instance, by Lucas (1988) in the context of endogenous growth. These human capital externalities may arise from learning spillover effects across workers, increased innovation activity in firms, and better institutional quality in a country, which may be associated with a more highly skilled domestic population. The empirical literature on human capital externalities is somewhat inconclusive but is mostly supportive. For instance, Acemoglu and Angrist (2000) find modest evidence in favor of human capital externalities from secondary schooling, whereas Ciccone and Peri (2006) find no evidence. Iranzo and Peri (2009) argue in favor of strong human capital externalities from college graduates in the United States but not from an increased share of high school graduates. In a recent study, Gennaioli et al. (2011) find strong empirical evidence of human capital externalities. They employ a new data set with 1569 subnational regions from 110 countries and argue that human capital is the primary driver of regional development. Moreover, they complement their finding with firm-level evidence on regional education levels for productivity and find large effects. The authors conclude that the previous empirical literature has underestimated the magnitude of human capital externalities. Similarly, Hunt (2011) employs a U.S. state panel data set for the period from 1940 to 2000 to show that an increase in the share of the immigrant college graduate population of one percentage point increases the number of patents per capita by 9–18 percent. This is strong evidence in favor of the hypothesis that skilled immigration increases TFP.

Each individual decides whether to become skilled and whether to migrate. Both skilled and unskilled individuals are internationally mobile, but they may differ in 6
traveler coming into the 5 pre-elimination districts that are the focus here, it would mean testing 6,956,197 travelers in 2013 based on the scaled mobile phone data. If each traveler were successfully tested so that all travel cases of malaria were found, treated, and secondary infections were prevented, it would mean 663 cases treated or prevented, representing around 44% of total cases in this area. Even just taking the cost of an RDT to test each person ($0.50), without adding any other costs 28