rafmacalaba/datause-encoder-data huggingface dataset:
capita by running a panel fixed effects model with region-year data. Changes in these revenues have no statistically significant effect on changes in income per capita (see Table S4.2 in the supplemental appendix).

Second, mining revenues may affect health status directly. Increased mining activities may generate enough additional pollution to worsen the general population’s health. However, changes in mining revenues do not have a statistically significant impact on changes in the probability of suffering a chronic illness, nor do they modify the predicted probability of suffering a self-reported life threatening illness<sup>12</sup> (see Tables S4.2 and S4.3 in the supplemental appendix). Finally, I verify that the level of mining revenues do not affect migration patterns and that changes in expenditure composition only explain a small fraction of the impact of total spending. These results and detailed explanations can be found in sections S4.1 and S4.2, respectively, of the supplemental appendix.

# _Specification_

My first stage equation is _Pjt_ is the level of public expenditure in period _t_ and region _j_ , _Zjt_ is the level of unanticipated mining revenues obtained by each region _j_ at time _t_ , δ _j_ are region fixed effects, and φ _t_ are year fixed effects. I controlled for other regional characteristics that vary over time ( _X_<sup>_R_</sup> _jt_<sup>),</sup>

> 11 I used data that varied at the region-year level and found no statistically significant effect of changes in mining revenues on changes with either of these consumer price indexes.

> 12 I tested the effect of mining revenues on the predicted probability of suffering a life-threatening illness for children in the Young Lives Study sample because I use this variable as a control in my main regressions. See the specification subsection for further explanation.

10
was raised by the industry to assist in rebuilding balance sheets and to establish new reinsurance companies and capacity.<sup>8</sup> Losses have, however, generally exceeded the additional capital subscribed and the success of capital issues is far from guaranteed despite the perceived positive price outlook.<sup>9</sup> Ratings agencies have responded with successive downgrades. For example, 12 of the top 25 reinsurance groups carried negative ratings outlooks at the end of 2002 and, out of the top 150 reinsurance companies, there had been 47 downgrades through 2002 and just 3 upgrades<sup>10</sup> (see Figure 6).

**Figure 6: Rating Migrations among the top 150 Reinsurance Companies* during 2002**

Source: Standard and Poors.

Of particular relevance in all markets, not simply those that have been affected by local falls in asset markets or catastrophic exposures, has been the influence of the response of major firms. Insurance groups have commenced an active program of corporate restructuring and, in some cases, market exit. Not surprisingly, a number of companies are focusing on their home markets and the

> 8 Source: Standard and Poors Reinsurance Outlook 2002.

> 9 The Economist, April 23, 2003 – “Aon, an insurance broker, estimates that reinsurers have lost $200 billion in capital since the terrorist attacks of September 11th 2001. Only about $30 billion in new capital has come in, much of it through new companies established in Bermuda. The value of their equity investments has shrivelled. And most of them have to deal with a legacy of sloppy underwriting when the market was soft.”

> 10 Source: Standard and Poors Global Reinsurance Outlook 2003.

13
# **_2.1 Trade Policy and the Booming_ Maquila** **_Sector_**

In 1990, Honduras began the implementation of pro-trade reforms by unilaterally reducing tariffs and in 1994 it joined the General Agreement on Tariffs and Trade (GATT). The multilateral agreement became the base for Honduras’ trade policy granting, at least, Most Favored Nation (MFN) treatment to all its trading partners. Honduras is an active member of the Central American Common Market. It has signed about a dozen bilateral investment treaties and free trade agreements with countries including Canada, Colombia, Chile, the Dominican Republic, Mexico, Panama, Switzerland, Taiwan (China), and the United States. Trade-oriented policies continue to be at the center of the development agenda in Honduras, which is participating in the negotiation of a trade agreement between the European Union and Central America.

**Figure 1 Trade Openness in Honduras, 1990–2006**

<!-- Start of picture text --> 1990 1995 2000 2005<br>year<br>Total Trade Trade Protection<br>1.2 20<br>1.1 15<br>1 10<br>(exports + imports)/GDP .9 5 average tariff (percent)<br>.8 0<br><!-- End of picture text --> _Source_ : Authors, based on data from the Banco Central de Honduras and Secretaría de Finanzas.

As a result of trade policy, the Honduran economy is developing into a more open and liberalized economy. Its tariff structure is low and more uniform than it used to be, and the application of nontariff measures is very limited (figure 1). The simple average of implicit tariff rates decreased from more than 16 percent in 1991–92 to about 3.3 percent in 2005–06.<sup>3</sup> In 2002, after the damaging effects of Hurricane Mitch, international trade supported a rapid recovery led by the exports of _maquila_ manufacturing and agroprocessing industries.

Between 1999 and 2006, exports and domestic markets became more diversified and employment and investment (domestic and foreign) grew, particularly in some nontraditional export activities. The United States remains the country’s principal trading partner, with Central America, particularly El Salvador and Guatemala, representing the second-most important market. The European Union is third, with exports to Germany particularly high. In recent years, exports to Mexico and Canada have also increased significantly.

> 3. The average implicit tariff was calculated as import tariff revenue/imports fob, excluding oil.

3
**Plc,Planning, and Reaercs**

**Eucallon and Empomn** X

Moock, Musgrove, and Stelcner used data from the 1985 Living Standards Survey in Peru to categorize 2,735 nonfarm family enterprises"informal" businesses that hire little or no labor

- and to explain eamings per hour of family labor.

The central question they addressed: Does formal schooling make a difference?

Regressiori analyses show that schooling affects eamings significantly, for all enterprises combined. This cannot reflect only "screening" but must .ndicate productivity (allowing for enterprise capital, location, and the age and sex of the workers).

Returns differ markedly among four subsectors - retail trade, textile manufacturing, other - manufacturing, and personal services and by gender and location (Lima, other cities, rural).

Postsecondary education has a fairly signiflcant payoff in urban areas, for both men and women. Retums for women are higher than for men, perhaps because education is still less frequent among women.

Primary education is especially valuable for women, who dominate the textile trades - for which only primary schooling pays off. Men dominate in the personal services subsector, for %hich post-primary education is valuable. Thus male-female differences are strongly associated w.th sectoral differences in the value of schooling.

In the retail trade sector, post-primary education appears to t: valuable in urban but not in rural areas.

In general, as might be expected, education pays off in jobs that require literacy, numeracy, or the ability to adjust to change. These results are consistent with earlier research indicating that education improves farmers' eamings very little so long as they follow tradition.l farming practices, where the necessary knov l, dge is transmitted informally. Education becomes especially valuable only when individuals take up new methods of production, because schooling enables them to apply these m.ethods more quickly and more profitably to their particular circumstances.

This paper is a product of the Education and Employment Division, Population and Human Ressources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433 Please contact Mary Fisher, room J7146, extension 34819 (40 pages with tables).

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
# **Community Treatment**

||Control mean|(1)|(2)|(3)|
|---|---|---|---|---|
|**Exposure**<sup>**~~+~~**</sup>|||||
|High exposure to the treatments|0.147|0.0750*|0.0491|0.0551|
|(HW message through three channels)||(0.042)|(0.037)|(0.037)|
|Medium exposure to the treatments|0.432|0.176***|0.138**|0.145***|
|(HW message through two channels)||(0.059)<br>[40%]|(0.054)<br>[32%]|(0.051)<br>[34%]|
|Low exposure to the treatments|0.674|0.127**|0.103**|0.106**|
|(HW message through one channel)||(0.050)<br>[19%]|(0.047)<br>[15%]|(0.045)<br>[16%]|
|Controls||NO|YES|YES|
|District dummies<br>Clustered standard errors in parentheses; *<br>+Channels: mass media (radio and/or printed|** p<0.01 ** p<0.05 *<br>materials), promotion|NO<br>p<0.1<br>al events and person|NO<br>al training/educatio|YES<br>nal sessions|

||Control mean|(1)|(2)|(3)|
|---|---|---|---|---|
|**Knowledge**|||||
|Events that require handwashing (summary index)|0.225|0.0152<br>(0.020)|0.007<br>(0.020)|0.009<br>(0.020)|
|Best method to wash hands is using water and soap (Percentage)|0.879|0.0402<br>(0.028)|0.0539*<br>(0.025)<br>[6.1%]|0.0512*<br>(0.021)<br>[5.8%]|
|Main cause of diarrhea is not washing hands with water and soap<br>(Percentage)|0.94|0.0272**<br>(0.012)<br>[2.8%]|0.0320**<br>(0.013)<br>[3.4%]|0.0313**<br>(0.012)<br>[3.2%]|
|Controls||NO|YES|YES|
|District dummies<br>Clustered standard errors in parentheses; *** p<0.01 ** p<0.05 * p<0.1||NO|NO|YES|

61
# **2.2 Constructing a hand-collected database of reported sectoral exposures**

Detailed information on banks’ loan composition is hard to obtain from publicly available or commercial databases. Typically, one can find a breakdown in real estate, consumer or business loans.<sup>5</sup> However, in general, there is no information on the sectoral composition of the business loan portfolio. Two exceptions are the credit registers maintained by some central banks and syndicated loan exposures. The former is, however, confidential, only available for few countries and does not allow cross-country comparisons. The latter is limited to very large loans by very large banks. Nevertheless, many banks provide information on their sectoral exposures in the notes to their financial statements.

The breakdown can be very detailed, but the level of detail can vary by bank and country as there is no required financial reporting format for these exposures. We hand-collect information on these exposures according to the following procedure. Starting from the universe of banks covered by Bankscope, we impose the following constraints: (i) banks need to be active in 2013, i.e. not have failed during the recent crisis; (ii) banks need to have publicly traded equity; (iii) banks need to have total assets in excess of 10 billion US$ in 2011; (iv) we only keep commercial banks, savings banks, cooperative banks and bank holding companies; and (v) information on basic characteristics, such as: common equity, total assets, the net interest margin, loan loss provisions as well as a liquidity ratio are non-missing for the period 2009, 2010, and 2011.

This selection results in a sample of 435 banks. We focus on large, listed banks as these are more likely to publish a detailed report on their website. However, this is not the case for all selected banks. The final database therefore consists only of banks for which the reports published on their website contain useful and detailed information on the sectoral exposures (317 from the 435 banks). To harmonize the heterogeneity in

> 5Liu (2011) investigates herding behaviour in bank lending by US commercial banks and looks at similarities in banks’ loan exposures to five categories (commercial real estate, residential real estate, consumer and industrial loans, individual loans and all remaining loans. He uses the Lakonishok et al. (1992) herding measure, which is initially developed to analyse herding by institutional investors their buy and sell signals.

10
Stephanou, C. and Rodriguez, C., 2008. Bank Financing to Small- and Medium-Sized Enterprises (SMEs) in Colombia. World Bank Policy Research Working Paper 4481.

World Bank, 2007a. Bank Financing to Small and Medium Enterprises: Survey Results from Argentina and Chile. Washington, D.C.

World Bank, 2007b. Bank Lending to Small and Medium Enterprises: The Republic of Serbia. Washington, D.C.

World Bank, 2007c. _Doing Business in Latin America 2008_ . Washington, D.C.

World Economic Forum, 2007. _Global Competitiveness Report 2007-2008_ . Geneva, Switzerland.

30
Studies also show the important contribution of social networks on the desire and decision to migrate (Boyd, 1989). Krissman (2005) calls for a larger conception of what constitute the migrant’s network, by including potential employers in the destination countries and labor smugglers, in order to understand international migration. Access to information about living abroad through siblings and friends increases intention to migrate (Cairns & Smyth, 2011) but social network from the home country decreases it, namely when there are strong links with family and local community. For instance, using data from Rwanda, Blumenstock et al. (2019) find that the probability of leaving home decreases proportional to the size of the home network. Bryan et al. (2014) point out that migration is a risky investment. Inasmuch as social networks could reduce the noise and perception of risks, their model suggests that firsthand, personal experience of migration is needed to overcome the information friction. They argue that this explains why there are fewer migrants than expected despite huge potential returns from migration.

Age is also an important determinant of migration, with younger people having a stronger incentive to migrate than older individuals (Cairns & Smyth, 2011; Kahanec & Fabo, 2013; Milasi, 2020). One of the reasons why age is such an important driver of migration is related to an individual’s familial/marital status which affects the likelihood of migration, namely, not having partner ties and childbearing (Smith & Floro, 2020; Van Mol, 2016). Milasi (2020) shows that women are less likely than men to desire to migrate, but that the probability to plan to migrate does not significantly depend on gender. He explains this by the fact that the actual planning of migration is guided by traditional drivers (e.g. income and education) that do not necessarily depend on gender whereas the desire to migrate in itself is more influenced by gender-related factors (Milasi, 2020; Ruyssen & Salomone, 2018).

Studies find that poverty and deprivation do not affect migration in the same way for women and men (Fleury, 2016). Smith and Floro (2020) find that among women from 84 low- and middle-income countries, food insecurity was a constraint rather than a push to migrate, while this relationship was not significant for men. Indeed, male migration increases with home or business ownership whereas these same factors tend to decrease female migration (Donato, 1993). Nevertheless, there is evidence of a positive education selection into migration for women (see for instance Richter and Taylor, 2008). Moreover, Heering et al. (2004) show that in Morocco, intention to migrate is higher when women have low job satisfaction and they suggest that this intention signals their modernity in comparison to more conservative Moroccan women. Studies also show that structural gender inequalities and discrimination can increase women’s migration (Fleury, 2016). Indeed, discriminatory practices (e.g., 6
economic assets in cities is leading a sharp rise of urban hazard risk and is a main driver for the increase in disaster losses. Growing exposure and delays in reducing vulnerabilities result in an increased number of natural hazards and greater levels of loss.

The impact of a disaster is dependent on the extent of the exposed elements that are in harm’s way, i.e. on the number of people and the amount and value of infrastructure that are affected by the disaster. The exposure module in this study is an inventory of assets at risk at the city level. We consider two asset classes: City population and city GDP. City population numbers are based on the “Henderson City Dataset” (Table 2).

**Table 2: Data sources for the exposure component**

|Dataset|Description|Unit|Source|
|---|---|---|---|
|Henderson<br>City Data|Data set of cities worldwide with more<br>than 100,000 inhabitants. The data<br>includes city names, countries, codes,<br>coordinates, and population numbers of the<br>years 1960, 1970, 1980, 1990, and 2000.|Inhabitants per<br>urban<br>agglomeration|Prof. J. Vernon<br>Henderson, Brown<br>University|
|GRUMP|Global urban footprint grid based largely<br>on NOAA’s night-time light satellite data<br>from 1994/5 coupled with settlement<br>information.|Urban population<br>distribution and the<br>global extents of<br>human settlements|Center for International<br>Earth Science<br>Information Network<br>(CIESIN), Columbia<br>University|
|GDP|Sub-national Gross Regional Product<br>(GRP) estimates and national Gross<br>Domestic Product (GDP) data are allocated<br>in proportion to the population residing in<br>that cell. The approach distinguishes<br>between rural and urban regions.|US$ per 1 km<sup>2</sup>grid<br>cell|World Bank|

All cities in less developed countries with more than 100,000 inhabitants in the year 2000 are selected from this database. This results in a city dataset with 1,943 cities. Cities in this context are entire urban agglomerations with suburban fringe and adjacent towns.

11
# **1** **<u>Introduction</u>**

Financial inclusion means that adults have access to and can effectively use a range of appropriate financial services. Such services must be provided responsibly and safely to the consumer and sustainably to the provider in a well regulated environment. At its most basic level, financial inclusion starts with having a deposit or transaction account at a bank or other financial institution or through a mobile money service provider, which can be used to make and receive payments and to store or save money. Yet 2 billion or 38 percent of adults reported not having an account in 2014 (Demirguc-Kunt et al., 2015). Financial inclusion also encompasses access to credit from formal financial institutions that allow adults to invest in educational and business opportunities, as well as the use of formal insurance products that allow people to better manage financial risks.

This paper provides a brief overview of financial inclusion around the world and discusses the benefits of financial inclusion and how they can contribute to inclusive growth and economic development, summarizing related empirical evidence.<sup>1</sup> It concludes by outlining some of the challenges to realizing the benefits of financial inclusion and directions for future research.

Financial inclusion can help reduce poverty and inequality by helping people invest in the future, smooth their consumption, and manage financial risks. Adults around the world and in all income groups use an array of different financial services. However, many low-income adults rely on informal financial services (Collins et al., 2009). Access to formal financial services allows people to make financial transactions more efficiently and safely and helps poor people climb out of poverty by making it possible to invest in education and business. By providing ways to manage income shocks like unemployment or the loss of a breadwinner, financial inclusion can also prevent people from falling into poverty in the first place. This is especially relevant for people living in the poorest households.

Financial inclusion also benefits society more broadly. Shifting payments from cash into accounts allows for more efficient and more transparent payments from governments or

> 1 See Klapper, et al. (2016) for a review of how financial inclusion can help achieve the Sustainable Development Goals (SDG’s). See Karlan and Morduch (2010) and Beck (2015) for surveys of the literature on access to finance and Cull et al. (2014) for a summary of the benefits of financial inclusion.

2
Aizenman, Joshua, and Yothin Jinjarak (2010) “De facto Fiscal Space and Fiscal Stimulus: Definition and Assessment.” NBER Working Paper No. 16539, November.

Baskaran, Thushayanthan, and Arne Bigsten (2013) “Fiscal capacity and the quality of government in Sub-Saharan Africa.” _World Development_ 45: 92-107.

Blanchard, Olivier J. (1993) “Suggestions for a new set of fiscal indicators.” In: Verbon, H.A.A., and F.A.A.M. van Winden, eds., _The Political Economy of Government Debt_ . New York: North Holland, pp. 307-325.

Blanchard, O.J., M. Das, and H. Faruqee (2010) “The Initial Impact of the Crisis on Emerging Market Countries.” Brookings Papers on Economic Activity, Spring, 1: 263-323.

Calderón, César, and Punam Chuhan-Pole (2017) What drives fiscal space in Sub-Saharan African countries? _Work in progress_ Calderon, C., and H. Nguyen (2016) “The Cyclical Nature of Fiscal Policy in Sub-Saharan Africa.” _Journal of African Economies_ 25(4): 548–579 Céspedes, L.F., and A. Velasco (2014) “Was this time different? Fiscal policy in commodity republics.” Journal of Development Economics 106: 92-106.

Cottarelli, Carlo, and Javier Escolano (2014) “Debt dynamics and fiscal sustainability.” In: Cottarelli, C., P.R. Gerson, and A.S. Senhadji, eds., _Post-crisis fiscal policy_ . Cambridge: The MIT Press, pp. 31-47.

Frankel, Jeffrey A., Carlos A. Végh, and Guillermo Vuletín (2013) “On graduation from fiscal procyclicality.” Journal of Development Economics 100: 32-47 Guerguil, M., P. Mandon, and R. Tapsoba (2017) “Flexible fiscal rules and countercyclical fiscal policy.” _Journal of Macroeconomics 52:_ 189-220.

Heller, Peter S. (2005) “Understanding Fiscal Space.” IMF Policy Discussion Paper PDP/05/4, March.

Kasekende, Louis, Zuzana Brixova, and Leonce Ndikumana (2010) “Africa: Africa’s CounterCyclical Policy Responses to the Crisis.” Journal of Globalization and Development, Volume 1, Issue 1, Article 16 Konuki, Tetsuya and Mauricio Villafuerte (2016) “Cyclical Behavior of Fiscal Policy among SubSaharan African Countries.” Washington, DC: International Monetary Fund, The African Department Paper Series.

Kose, M. Ayhan, Sergio Kurlat, Franziska Ohnsorge, and Naotaka Sugawara (2017) “A CrossCountry Database of Fiscal Space.” The World Bank Policy Research Working Paper 8157, August.

Lane, P.R., and G.M. Milesi-Ferretti (2011) “The Cross-Country Incidence of the Global Crisis.” IMF Economic Review 59(1): 77-110.

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<!-- Start of picture text --> 16 16<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>-2 -2<br>Estimates -4 -4<br>-6 Months to reform -6 Months to reform<br>-8 -8<br>RD<br>-10 -10<br>C. -12 -12<br>RD (Linear): 0.00 (0.02) -14 RD (Linear): 0.01 (0.02) -14<br>RD (Quadratic): 0.01 (0.02) RD (Quadratic): 0.01 (0.02)<br>-16 -16<br>-18 -18<br>-20 -20<br>2015m12 2015m12<br>Trends 2014m12 2014m12<br>VAT Liability<br>2013m12 2013m12<br>Year-Month Year-Month<br>VAT<br>2012m12 2012m12<br>I.Output Net<br>De-seaonalized 2011m12 2011m12<br>II.<br>B.<br> August 2014 2015m12 August 2014 2015m12<br>⇐ ⇐<br>VAT Rebates of up to4 ppt Enter Into Effect VAT Rebates of up to4 ppt Enter Into Effect<br>2014m12 2014m12<br>Trends 2013m12 2013m12<br>Year-Month Year-Month<br>Raw 2012m12 2012m12<br>A. 2011m12 2011m12<br>13.25 13 12.75 12.5 12 11.75 11.5<br>12.25 12 11.75 11.25 11 10.75 10.5 10.25<br>Log Output VAT Log Net VAT Liability<br>12.25 12 11.75 11.25 11 10.75 10.5 10.25<br>Log Output VAT Log Net VAT Liability<br><!-- End of picture text --> 58