rafmacalaba/datause-encoder-data huggingface dataset:
## 2.3 Schooling externalities

We also consider the possibility of a positive externality from highly educated workers, in the spirit of the recent literature (Acemoglu and Angrist 2000, Ciccone and Peri 2006, Moretti 2004a, 2004b and Iranzo and Peri 2009). There is a large body of growth literature (beginning with Lucas 1988, and extending to Azariadis and Drazen 1990, Benhabib and Spiegel 2005, Cohen and Soto 2007 and Vandennbussche et al 2009) that emphasizes the role of human capital (schooling) on technological progress, innovation and growth of GDP per capita. More recently, however, the empirical literature has pointed out that while it is sometimes hard to ...nd an e¤ect of human capital on growth of income per capita (Benhabib and Spiegel 2005), there seems to be evidence that human capital contributes to the level of income per person beyond its private returns. This implies that TFP is an increasing function of the schooling intensity in the domestic labor force. Such formulation is particularly appropriate to be included in our model and, based on the expressions used in Moretti (2004a, 2004b), the TFP can be expressed as follows , where A0 captures the part of TFP independent of the human capital externality, and � is the semi-elasticity of the modi...ed TFP to the share of highly skilled in the economy, <u>Qh;t</u> 5 Qt . Acemoglu and Angrist (2000) and Iranzo and Peri (2009) use a similar formulation to express schooling externalities and we use their estimates of the parameter �. Relying on this structure and using migration data from national Censuses, we can simulate the e¤ects of immigration and emigration on wages of non-migrants in a range of countries for a range of values obtained from the literature for the three key parameters �q; �I and �.

# 3 Data description

Assessing the national wage e¤ects of immigration to and emigration from diverse set countries across the globe requires country-level international migration and labor force data by skill level. The detailed description of the migration data is in the Data Appendix. Here we describe brie‡y the main sources and features of the migration and labor force data used.

## 3.1 International migration data

The relevant migration ‡ows to be used in our exercise are immigration and emigration ‡ows (namely gross ‡ows of immigrants and emigrants net of returnees and re-migrants). They capture the change in actual supply of migrants in a country.

There are several sources for migration ‡ows by receiving country (e.g. OECD International Migration database, UN migration statistics) but those only include gross in‡ow of people in a country and they almost never correct for migrants who leave or go back to their country of origin. Moreover they never record undocumented migrants and they often

> 5The externality is expressed in terms of the ratio between labor composite. Such a ratio, however, is almost identical to the share of workers with high schooling in the labor force.

8
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Evaluating Aid for Trade: A Survey of Recent Studies<sup>1</sup> _Olivier Cadot, Ana Fernandes, Julien Gourdon, Aaditya Mattoo and Jaime de Melo_<sup>_2_</sup> JEL classification: F15, F35.

Keywords: Aid for trade (AFT), trade performance, gravity, impact evaluation <mark>Sector board: EPOL.</mark> 1 An earlier version of this paper entitled “Evaluation in Aid-for-Trade: From Case-study Counting to Measuring” was presented at the FERDI-ITC- World Bank workshop «Aid for Trade: What Do We Know ? Which Way Ahead?», Geneva, December 6, 2012. We thank Céline Carrère, Bernard Hoekman and seminar participants for comments on the earlier draft. Cadot and de Melo gratefully acknowledge support from France’s Agence Nationale de la Recherche under “Investissement d'Avenir” grant ANR-10-LABX-14-01 and from Switzerland under NCCR work package 6, “Impact assessment”.

2 CADOT (University of Lausanne, FERDI and CEPR); FERNANDES (World Bank); GOURDON (CEPII); MATTOO (World Bank); DE MELO ( FERDI and CEPR).

1
- Silva, J. M. C. S. and Tenreyro, S. (2006). The log of gravity. _Review of Economics and Statistics_ , 88(4):641–658.

- Stone, S. and Casalini, F. (2020). Sanitary and phytosanitary measures. In _Handbook of Deep Trade Agreements_ , pages 367–390. The World Bank.

- Teti, F. (2020). 30 Years of Trade Policy: Evidence from 5.7 Billion Tariffs. ifo Working Paper Series 334, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

- UNCTAD (2012). Non-Tariff Measures To Trade: Economic And Policy Issues For Developing Countries Developing Countries In International Trade Studies.World Trade Organization. Technical report, United Nations Conference On Trade And Development.

- WTO (2012). World trade report 2012: Trade and public policies: A closer look at non-tariff measures in the 21st century. Technical report, World Trade Organization.

28
The clearest characteristic of net lifetime beneficiaries from this PAYG pension system is gender; women are more than twice as likely as men to gain from the system, with 81% of surviving womnen but only 37% of surviving men being net beneficiaries. The disproportionate gains of women are a consequence of lower lifetime tax payments due to lower lifetime earnings, and higher lifetime benefits because of higher life expectancy. The average age of death among net beneficiaries is 82.5 years, compared with 73.2 years for net taxpayers. Table 2 shows, for all people who survive to age 65 and for males and females separately, the socio-demographic characteristics of net beneficiaries and net losers from this simple PAYG scheme.

**Table 2**

**Socio-economic characteristics from LIFEMOD of lifetime net beneficiaries and net taxpayers in a simple PAYG pension scheme**

# ALL SURVIVORS

|Characteristics|Net<br>Ben|Net<br>Tax|
|---|---|---|
|Age of death|82.5|73.2|
|Ever lone parent(%)|27.7|10.1|
|Ever divorced (%)|36.2|37.2|
|Years divorced|8.8|5.6|
|Years tertiary educ.|0.9|1.4|
|Years of unemployment|3.8|4.1|
|Years of employment|30.9|37.0|

17
# **3 Intra- and inter-provincial labor migration**

Worldwide, labor migration is a powerful tool to reduce distance to economic opportunity. Eight million Americans change states every year (World Bank, 2008). In China, the gradual reform of the Household Registration System (Hukou) reduced the constraint on domestic labor mobility (Chan and Li, 1999; Chen and Wu, 2005; Fan, 2005a; 2005b; He and Pooler, 2002; Li, 2004; Liang, 2001; Shen, 1999; Zhang and Song, 2003; Wu and Yao, 2003; Zhao, 2005). The significant inflow of capital and the development of an export-oriented economy in the coastal provinces created strong demand for labor (Li, 1997; Wong et al., 2003; Zhu, 2000). The large-scale migration from rural areas and inland regions to urban areas and coastal regions provided coastal and urban areas with a flexible labor supply at reasonable costs and contributed to the rapid development of their labor intensive industries (Luo and Zhu, 2008). This in turn accelerated the formation and expansion of the small and medium size cities in the coastal provinces and increased the potential for the exploitation of economies of scale (Sit and Yang, 1997; Li, 1997).

A similar story applies for Guangdong, with PRD (especially Guangzhou, Shenzhen, and Zhuhai) as the hubs. The inflow of migrant workers largely contributed to the rapid development of Guangdong. Guangdong officially became the most populous province in 2005. Its total population hit 110 million then, with 79 million registered permanent residents and 31 million migrants who have lived in Guangdong for more than six months.<sup>3</sup> The PRD, the Yangtze River Delta, and Bohai Bay area, are the three largest regions with significant migrant concentrations in China (Trudel, 2008).

The Chinese fifth census data shows, in 2005, that of the 93 million working age cross-country migrants, some two-thirds are urban labor and some one-third rural labor. Urban areas are the major destination of migration: roughly 67 million moved to urban areas and 26 million to rural areas. The scale of intra-provincial migration was higher than that of inter-provincial migration. At the national level, the average intra-provincial immigration rate is about 11.8 %, and interprovincial immigration rate 6.4 %. In Guangdong, in 1978-2013, the average total migration rate is 2.8 %, and the average net migration rate is 0.3 %. The net migration rate is positive in most of the years. It gradually increased since 1978, peaked around 2006, and fluctuated downward in the recent years. The average gross inflow of migrants amounted to some 1.06 million per year. With an average outflow of migrants of some 0.84 million, this results in a net inflow of migrants of 0.22 million (see figure 4). This “demographic dividend” has played a significant role in contributing to the economic development in Guangdong.

> 3 <u>http://www.chinadaily.com.cn/english/doc/2005-01/29/content_413299.htm.</u>

6
## 3. Macroeconomic evidence of the response of remittances to natural disasters

In this section, we empirically investigate the following question for a large sample of developing countries and across income groups and geographical regions: Do remittances respond in a countercyclical or compensatory manner to natural disasters in the recipient economies?

The empirical exercise is undertaken primarily to understand whether remittances respond to natural disaster events in home countries.

# **_3.1 Data_**

The outcome variables of interest are migrant remittances to a country _i_ in a year _t_ . The econometric analysis is based on estimates of remittance flows to developing countries from the World Bank’s World Development Indicators (WDI). Data on GDP per capita and population comes primarily from the same source. Summary statistics of the different flows and other variables of interest are presented in table 1.

Natural disaster data on the occurrence and effects of natural disasters are from Center for Research on the Epidemiology of Diseases (CRED), International Emergency Disasters Database (EM-DAT).<sup>10</sup> CRED defines a disaster as a natural situation or event which overwhelms local capacity, necessitating a request for external assistance (Noy, 2008, EM-DAT Glossary of terms). These disasters can be grouped into several categories, of which meteorological disasters (floods, wave surges, storms, droughts, land slides and avalanches), climatological disasters (disasters caused due to long run or seasonal climatic variability such as drought, extreme temperatures and wild fire) and geophysical disasters (earthquakes, tsunamis and volcanic eruptions).

Each of these categories mentioned above are not mutually exclusive and should be considered more as a typological classification. In our analysis, we focus primarily on all disaster events taken together within a country in a year rather than each of them examined separately. A reason for the focus on the total impact of all disasters in this paper is the possibility that different regions in a country can be affected by different types of disasters in a given year and since remittances data is available only at annual

> 10 The Center for Research on the Epidemiology of Diseases (CRED) has collected and made publically available data on the occurrence and effects of natural disasters from 1900 to the present with a worldwide coverage. The database is compiled from various sources, including UN agencies, non-governmental organizations, insurance companies, research institutions and press agencies. The EM-DAT data is publicly available on CRED's web site at: www.cred.be.

7
# **3.1 The Data Set**

The data set we use is the World-Bank-NBS Survey on 120 Chinese cities of 12,000 firms between 2002 and 2004. In the data, all provinces in China are covered except Tibet.<sup>4</sup> For each province, the capital city is selected, and if the province is not too small (by China‟s standard), other cities are also selected. Typically, provinces with high total GDP are allowed to survey more cities. In each province, top cities (as measured by either total GDP or total industrial output) are selected. For all but four cities, we sample 100 firms; for the four mega cities (Beijing, Tianjin, Shanghai, and Chongqing), we sample 200 firms. Thus, we have 12,400 sample firms in total. See Table A1 for a complete list of provinces and cities. All firms are from manufacturing. For each city, the top 10 manufacturing industries in terms of sales revenue are drawn. For each industry, all firms in the sample universe are divided into large, middle and small firms, each accounting for 1/3 of total industry revenue. Then from each of three types of firms, an equal number of firms are drawn. Firms are required to have a minimum of 10 employees. The distribution of our sample industries is displayed in Table A.2.

The survey has three main parts. The first part was sent to the senior managers of a firm, covering topics related to basic firm characteristics, bottlenecks to firms' growth, relationship with clients and suppliers, labor, infrastructure, trade, finance, corporate governance and relationship with the government. The second part was sent to the accounts and personnel officers, concerning topics on ownership composition, financial statements and labor statistics. The last part is answered at the city level, covering basic characteristics of the city that a firm is located.

> 4 Tibet is not selected because there are insufficient number of firms.  In conventional firm surveys conducted by NBS, Tibet is often not selected due to high survey costs.

10
Table 4: Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference

||||Sector of W<br>|ork<br>||
|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service /<br>Sales|Agriculture /<br>Unskilled Manual|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|
|�<br>Years of Schoolingizy|0.093|0.059|0.064|-0.048|-0.786|
||(0.058)|(0.028)|(0.047)|(0.031)|(0.468)|
||[0.107]|[0.033]|[0.169]|[0.116]|[0.093]|
|Mean of Dependent|0.321|0.082|0.100|0.312|7.704|
|(Pre-Reform Cohorts)||||||
|First Stage F-Statistic|6.06|6.63|6.63|6.63|6.26|
|N|13,909|13,755|13,755|13,755|13,789|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : The dependent variable is described at the top of each of the five columns. In columns 1–4 it is an indicator that equals 1 if true, and in column 5 it is the ideal number of children. Skilled/Professional jobs include professional, clerical, and skilled manual job groups; the other categories exactly describe the occupation groups included. Ideal number of children is censored at 20; no women in the Demographic and Health Survey report having more than 18 children, and non-numerical � responses are assigned the maximum value. Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure, _Izy_<sup>FPE</sup> . All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. Each estimate is from a unique regression, and the second-stage estimate in column 5 is generated using a tobit model. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

28
equilibrium size, allowing them to choose potentially more efficient organizational forms (Demirgüç-Kunt, Love, and Maksimovic, 2007).

Though SMEs make up a large part of the emerging private sector in most countries, they are also more constrained in their access to financial services than large firms (Ayyagari, Beck, & Demirgüç-Kunt, 2007; Beck, Demirgüç-Kunt, and Maksimovic, 2005). Thus, one reason for the mixed evidence on SMEs and growth may be that weak legal and financial institutions in developing countries prevent SMEs from growing into large firms. A large SME sector might therefore actually reflect inefficiencies in the system that do not allow for firm growth or make it optimal for firms to stay small (Beck, Demirgüç-Kunt, and Maksimovic, 2005).

In this paper, we compile and assess the current knowledge on the role of SME finance in developing countries. In Section II, we take a close look at available data to understand the extent to which access to finance is an obstacle for SMEs. We use data from both firm-level surveys as well as surveys of bankers to gain a comprehensive understanding of both the supply and demand side issues in SMEs accessing bank finance. Next, we look at how prevalent non-bank sources of financing are for SMEs.

In Section III, we focus on the institutional constraints that affect access to finance. We begin by focusing on different market failures that impede SME access to finance. These include the lack of reliable credit information, lack of suitable collateral, and weak legal institutions. In each case we survey the latest research showing how improvement in each of these areas leads to tangible benefits for small firms. We end this section by exploring the role that large banks and foreign banks 4
# **1. Introduction**

Irregular migration from Africa to Europe has become increasingly visible in recent years. Images of young African men on crowded rubber boats in the Mediterranean Sea cast a spotlight on the strong desire of many youth to migrate, and on the tragic consequences that can occur when these journeys through the “backway” go awry.

The COVID-19 global pandemic has resulted in travel bans, the shutting of consular services, and border closures in many countries (OECD, 2020). The pandemic has in this way disrupted legal pathways to international migration, and raised concerns that more people may turn to irregular or backway migration as a substitute (Yayboke, 2020; Sanchez and Achilli, 2020). Indeed, while European border agencies reported an initial dip in Mediterranean crossing, this seemed to pick up later in the year.<sup>1</sup> Despite further restrictions introduced by Maltese and Italian governments on disembarkation in their ports, migrants have continued to leave Libyan shores since the pandemic outbreak, and the number of people attempting the crossing from North Africa to Italy reached 34,000 in 2020, almost three times the number of irregular sea arrivals recorded in 2019.<sup>2</sup> Overall arrivals to Spain in 2020 also increased by 29% to 41,681 arrivals, although there was a diversion in the migration routes used. Arrivals to mainland Spain, Ceuta and Melilla via the Western Mediterranean route dropped to 18,656 arrivals, who were predominantly citizens from Algeria and Morocco, whereas arrivals to the Canary Islands kept accelerating during 2020, reaching 23,025 arrivals (two-thirds of which in the last quarter of 2020), mostly from Morocco, but also Mauritania, Senegal and The Gambia.<sup>3</sup> These facts alone do not provide a full picture of the COVID-19 impact on irregular migration flows, but they do suggest irregular migration attempts from Northern Africa to Europe through the Mediterranean Sea continue to be an issue, despite the border closures and travel bans.

> 1 See <u>https://www.iom.int/news/covid-19-control-measures-gap-sar-capacity-increases-concern-about-invisibleshipwrecks</u> [accessed 29 January, 2021].

> 2 Mixed Migration Centre “Quarterly Mixed Migration Update Q4 2020: West Africa”, January 2021. <u>http://www.mixedmigration.org/resource/quarterly-mixed-migration-update-wa-q4-2020/</u> [accessed 29 January, 2021].

> 3 See UNHCR data available at <u>https://data2.unhcr.org/en/documents/download/84312</u> and FRONTEX data available at <u>https://frontex.europa.eu/along-eu-borders/migratory-map/</u> [accessed 29 January, 2021].

2
16 significant since the number of potential buyers<sup>and sellers are small in the</sup> secondary market. In order to test this hypothesis,<sup>we examine the effects of</sup> the increase in commercial bank loan reserves<sup>in mid-1987 and the announcement</sup> of the Brady Plan in March 1989. Third, since<sup>the economic conditions existing</sup> in the creditor country can influence<sup>the bargaining process between the</sup> debtor and the creditors, we estimated<sup>the impact of changes in US industrial</sup> production index on the willingness to pay<sup>of the debtor country.</sup> The impact of the above factors on the<sup>willingness to pay was estimated</sup> using pooled cross-section time-series<sup>data for 6 countries described in</sup> Section 2 over the 1986:4 - 1990:4 period.<sup>We used the typical random-effect</sup> error components equations since simply pooling<sup>the data and estimating by an</sup> ordinary least squares procedure can<sup>result in inefficiency as well as several</sup> sorts of biases due to heterogeneity of coefficients<sup>across countries and time</sup> 15 The data was obtained from the IMF's<sup>International Financial Statistics</sup> and from various national sources. For some countries,<sup>because quarterly data</sup> on GDP was not available, we used the Industrial<sup>Index.</sup> The estimated results are summarized<sup>in Table 2. On the whole, the</sup> results appear quite robust. The most interesting<sup>aspect of these results</sup> concerns the positive impact of changes<sup>in the debtor country's capacity of</sup> payment on the willingness to pay.<sup>Indeed, an increase in GDP or GDP growth</sup> appear to influence positively the dependant<sup>variable. This result seems to</sup> indicate that GDP and GDP growth are good<sup>indicators of the willingness to</sup> pay. Similarly, an increase in the change<sup>of international reserves and in</sup> exports leads to an improvement of the debtor<sup>country willingness to pay.</sup> Note, however, that the impact of a<sup>change in reserves is positive but not</sup> statistically significant in most regressions.<sup>As suggested by several</sup> authors (e.g. Divan (1990)), a higher<sup>degree of openness of the debtor</sup> country (measured as the ratio of<sup>imports and exports over GDP) leads to an</sup> 15 The fixed-effect error component model and<sup>regressions for individual</sup> countries were also tested. Overall,<sup>the results were not qualitively very</sup> different from those presented in<sup>Table 2. Note, however, that the degree of</sup> freedom is low for individual regressions.<sup>These results can be obtained from</sup> the authors upon request.