rafmacalaba/data-use-mentions-extended huggingface dataset:
for the period 2012-2017 between 25 and 30 percent of households are HtM. For Japan, we ...nd that 18 percent of individuals are HtM and Hara et al. (2016) ...nd, following Kaplan et al.’s (2014) approach, that the share of HtM is about 13 percent for the years 1989-2009.

Appendix 2 provides further evidence regarding key characteristics about the nature and main characteristics of HtM and non-HtM. First, it shows evidence that while a vast majority of nonHtM individuals in the developed world would rely on savings to cope with a ...nancial shock, the top source of funding of individuals living in emerging markets would be aid from family, relatives, and friends. Furthermore, in both set of countries, few individuals would rely on selling assets for providing support, so illiquid wealth (such as real estate properties) does not seem to be important for dealing with a ...nancial shock. Second, and using information on whether individuals have a standing property loan, there is strong evidence that wealthy HtM individuals are also present in emerging markets (and not solely in developed countries). Interestingly, yet not surprisingly, while wealthy HtM represent an important share of all HtM individuals in developed countries, it is less the case in emerging markets where the HtM phenomenon is largely driven by poor HtM individuals. Lastly, this appendix analyzes whether the capacity to deal with a ...nancial shock varies across several plausible relevant economic and demographic, individual and household, characteristics. As in previous studies relying on data from developed countries, individuals living in households with higher income, greater educational attainment, male, and employed show a higher capacity to deal with a ...nancial shock, both in developed and in emerging countries (with even similar predicting capacity values among these groups of countries).

# 4.3 Evidence on allocation of social transfers

Figure 3 shows the share of social transfers reaching HtM individuals, �. This is the
# **I. Introduction**

The purpose of this paper is to empirically evaluate the main determinants of tourist arrivals and expenditures that have been found in the literature, incorporating new explanatory variables, and then assess the tourism performance of São Tomé and Príncipe (STP). The economic literature has established that international trade is one of the causes of economic growth.<sup>1</sup> This relationship can also be extended to the tourism sector, which is a service export.<sup>2</sup> STP is a small-island African country in the Gulf of Guinea, in which tourism is a comparative advantage, but it is far from the characterization of a tourism-dependent small economy. The empirical examination of the determinants of tourism aims to help STP and other small islands to understand what can be done to spur tourism growth.

The economic literature shows that tourism demand is affected by not only price and income, but also a host of other factors, such as air connectivity, language, and culture, among others. First, there is an extensive research agenda on measuring price and income elasticities of tourism, which is specific for different types of tourism destinations. Second, there is a myriad of characteristics that are found to be important to the tourism industry, such as remoteness, language, culture, air connectivity, bilateral trade, etc. More recently, there have been many studies emphasizing the role of digital media and digital presence<sup>3</sup> as a key determinant of tourist decisions. Finally, the models were expanded to investigate the link between acceptance of lesbian, gay, bisexual, and transgender (LGBT) people and its impacts on tourism.

Following the literature on determinants of tourism demand, several empirical approaches were applied and yielded general findings applicable to all countries and some specific to STP. This paper constructs a database from different sources to provide a complete picture of tourism statistics and explanatory variables. Different statistical models were used to establish correlations and,
174<br>10,000 9,769<br>9,378<br>9,000<br>8,000<br>Kathmandu Oth urban R‐W Hill R‐E Hill R‐W Tera R‐E Tera National<br>NLSS 1995/96 Old Poverty Trend NLSS 2010/11 Old Poverty Line<br><!-- End of picture text --> Source: Nepal Living Standards Surveys 1995 and 2010 A vocal, dynamic and assertive middle class can be the foundation for a social contract that can catalyze reforms to both generate and share growth. Postwar Western Europe, post-revolution China, post land reform Republic of Korea, and the United States under the New Deal all bear

> 23 This is also a topic of an in depth exploration in the forthcoming report, “Moving up the ladder: poverty reduction and social mobility in Nepal” (World Bank, 2016).

22
. 2006. “Ethnic Persistence, Assimilation and Risk Proclivity.” _IZA_ Discussion Paper No. 2537.

Bonin, Holger, Amelie Constant, Konstantinos Tatsiramos, and Klaus F. Zimmermann. 2009. “Native-Migrant Differences in Risk Attitudes.” _Applied Economics Letters_ , 16: 1581-1586.

Borjas, George J. 1985. “Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants,” _Journal of Labor Economics_ , 3 (October): 463–89.

______ 2015. “The Slowdown in the Economic Assimilation of Immigrants: Aging and Cohort Effects Revisited Again,” _Journal of Human Capital_ , 9 (4): 483-517.

Borjas, George J. and Hugh Cassidy. 2020. “The Adverse Effect of the COVID-19 Labor Market Shock on Immigrant Employment,” _National Bureau of Economic Research_ Working Paper 27243.

Brinca, Pedro, Joao B. Duarte, and Miguel Faria-e-Castro. 2020. “Measuring Sectoral Supply and Demand Shocks during COVID-19,” Working Paper 2020-011B, _Federal Reserve Bank of St. Louis_ .

Cadena, Brian C. and Brian K. Kovak. 2016. “Immigrants Equilibrate Local Labor Markets: Evidence from the Great Recession,” _American Economic Journal: Applied Economics_ , 8(1): 257– 290.

Carlsson, Magnus, and Dan-Olof Rooth. 2007. “Evidence of ethnic discrimination in the Swedish labor market using experimental data,” _Labour Economics_ , 14 (4): 716-729.

27
show improvements on participant social skills. In such assessments, treated students report greater tolerance of others, a reduction in interactions with bad peers, and an improvement in the overall classroom environment. In addition, some students report the program reinforces their academic experience in a more enjoyable way (Glasswing International, 2012b).

# **2.2 Recruitment and Enrollment Process**

During 2016, the NGO offered and implemented this version of the ASP in five public schools in El Salvador. Using data from the country’s 2015 Educational Census, we found that these schools were similar to the underlying population of public educational centers in El Salvador. Tests for differences between participant and nonparticipant schools are shown in Table A1 in the Appendix section.<sup>15</sup> As part of the recruitment process, the NGO visited the schools to advertise the program and provided informational brochures and videos. Additionally, NGO staff invited participants from other schools to join them for these visits and share their experiences with prospective attendees. During these visits, the team gave a parental authorization form to children interested in participating in both the intervention and study, asking them to return it with a signature from a parent or tutor. They also gave teachers extra forms for students who were absent during the visit but might want to participate.

Some weeks later, the NGO and research team returned to schools to register and enroll children. Out of a total of 2,420 students from the five schools, we recruited and enrolled 1,056 students between 10 and 16 years of age. Children were allowed to self-enroll as long as they had an authorization signed by an appropriate adult.

During the registration process, students completed a form with personal and family information and their application to participate in a club. They were next assigned to a group, taking into consideration their preferences and the aggregated demand for the club category. Originally, club enrollments varied according to the number of participants interested in each category. However, for methodological reasons
of market timing considerations in issuance activity.<sup>3</sup> Gozzi et al. (2010) show that firms continue issuing securities at home after accessing international bond and equity markets.<sup>4</sup> In the present paper, we contribute to this literature by analyzing the characteristics of corporate bond issues in domestic and international markets, showing that firms issue bonds with different characteristics in different markets.

A second strand of the research examines market segmentation and its implications for capital market functioning and portfolio allocation.<sup>5</sup> In a frictionless world, the location of where firms issue securities is irrelevant. In practice, however, frictions might fully or partially segment domestic markets from international ones (Japelli and Pagano, 2010). For example, regulations and taxes might hinder the ability of investors to purchase securities outside their home market (Lewis, 1999; Karolyi and Stulz, 2003; Cameron et al., 2007), and information asymmetries between foreign and domestic investors might induce them to price similar assets differently (Bae et al., 2008). In this context, investors with different preferences, investment horizons, and abilities to diversify risk could dominate particular markets, so that securities with distinct traits are offered in different locations (Kim and Stulz, 1988). Securities might also differ across markets if market makers in different locations specialize in securities with particular characteristics (Pagano and von Thadden, 2004). Consequently, bond attributes might be specific to the geographic location and bond prices might not fully converge across markets. We

> 3 The value of debt issues is not directly comparable to that of equity issues because equity issues have no maturity, while debt issues must be repaid. Part of the proceeds from debt issues is typically used to repay maturing debt and, therefore, only a fraction of debt issues can be considered new financing. Henderson et al. (2006) try to adjust the data on debt issues to take this fact into account
_ This table provides estimates of the effects of logarithm of future refugee intensity on logarithm of total household consumption expenditure per capita (column (1)), a binary measure of household consumption poverty status (column (2)), “started log” of household wealth score (column (3)), and a binary measure of household wealth poverty status (column (4)). Household sample weights from ESS2 applied. All columns control for the logarithm of (lagged) refugee intensity, household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for the 12 characteristics are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

56
# **6. BIBLIOGRAPHY**

Akresh, Ilana Redstone. 2006a. “Immigrant’s US labor market adjustment: Disaggregating the occupational transitions.” Demography, 44(4): 865-881.

Akresh, Ilana Redstone. 2006b. “Occupational Mobility among legal immigrants to the United States.” International Migration Review, 40(4): 854-885.

Akresh, Ilana Redstone. 2008. “Occupational Trajectories of Legal US Immigrants: Downgrading and Recovery.” Population and Development Review, 34(3): 435-456.

Baker, Michael and Dwayne Benjamin. 1994. “The Performance of Immigrants in the Canadian Labor Market.” Journal of Labor Economics, 12(3): 369-405.

Baltagi, Badi H. 2001. “Econometric Analysis of Panel Data.” Second Edition. John Wiley & Sons, Ltd. West Sussex England.

Barro, Robert J. 1991. “Economic Growth in a Cross-Section of Countries.” Quarterly Journal of Economics 106: 407-433.

Barro, Robert J. 1997. “Determinants of Economic Growth: A Cross-Country Empirical Study.” Cambridge, MA: MIT Press.

Ben-Porath, Yoram. 1967. “The Production of Human Capital and the Life Cycle of Earnings.” Journal of Political Economy 75: 352-365.

Bloom, David E. and Morley Gunderson. 1991. “An Analysis of the Earnings of Canadian Immigrants.” In J.M. Abowd and R.B. Freeman (eds.): _Immigration, Trade and the Labor Market_ , 321-42. Chicago: University of Chicago Press.

Borjas, George. 1982. “The Earnings of Male Hispanic Immigrants in the United States.” Industrial and Labor Relations Review, 35(3): 343-353.

Borjas, George. 1985. “Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants.” Journal of Labor Economics 3(4): 463-489.

Borjas, George. 1987. “Self-Selection and Earnings of Immigrants.” American Economic Review 77(4):
regions of a country. We use a cross-section of more than 40,000 manufacturing and services firms in 79 developing countries. We hypothesize that traditional firm-level characteristics matter less for firms located in core regions, since a firm’s business environment will explain a larger share of a firm’s export behavior.

This paper is structured as followed. In the next section, we compare firms located in the core with firms located in non-core regions to detect significant differences in export performance. We also analyze other performance indicators related to a firm’s business and business environment. In section three, we perform an econometric exercise where we first study the impact of firm-level and regional determinants as well as agglomeration economies on exporting. We also examine the role of location, assessing whether the determinants of exporting are different if firms are located in core versus non-core regions. Based on the results of the empirical analysis, we derive some policy conclusions in section four.

# **2 Descriptive Analysis: Core versus Non-core Regions**

## **2.1 Data**

The World Bank Enterprise Analysis Unit recently published the Enterprise Surveys Indicator Database.<sup>4</sup> This publication is based on enterprise surveys for over 100 countries, over the period 2002 to 2010. Enterprise surveys represent a comprehensive source of firm-level data in emerging markets and developing economies. One major advantage of the enterprise surveys is that the survey questions are the same across all countries. Moreover, the Enterprise Surveys represent a stratified random sample of firms using three levels of stratification: sector, firm size, and region.

The Enterprise Surveys Indicator Database covers a wide range of indicators on firm characteristics and indicators of business environment, innovation and technology, workforce and skills, permits and licenses, infrastructure, finance, and corruption among others. In making use of this dataset, we apply the following rules: (i) We included only the most recent Enterprise Surveys for each country into the analysis. (ii) We dropped high-income
2003 and 2012 was 7.5% per annum, the growth in manufacturing GDP was around 3.3%. These trends meant that manufacturing’s share of total GDP declined from 9.8% in 1990 to around 6.9% in 2012 (Davies and Kerr, 2018).

Ghana has several sources of manufacturing firm‐level data, which have led to a substantive body of research using firm‐level data. Firm‐level analysis using Ghanaian data has been made possible by the publicly available RPED firm panel surveys conducted by the World Bank and the Centre for the Study of African Economies at the University of Oxford between 1991 and 2002. More recently a Project for Enterprise Development in Low Income Countries (PEDL) funded project has resulted in the 1987 and 2003 firm censuses being put into the public domain, whilst the authors are working on a follow‐up PEDL‐funded project to put the 2014 firm census into the public domain and document the differences and similarities between the 1987, 2003 and 2014 censuses, as well as the 1962 census for which microdata does not exist. We discuss this further in section 3.

The paper closest to ours is Lall et al (2009), who investigate the effects of infrastructure, human capital, existing agglomeration and other factors on the location decisions and productivity of Ghanaian manufacturing firms, using the 1987 and 2003 industrial censuses and changes across regions between the two censuses. Lall et al (2009) find that there was a pattern of de‐concentration of manufacturing away from the dominant districts (Accra and Ashanti, which contains the 2<sup>nd</sup> largest city Kumasi) between 1987 and 2003. In doing so the authors take the firm censuses at face value. We discuss below that these censuses were actually not comparable in a number of important ways. The main concern about the results from the Lall et al (2009) paper is that the de‐ 3
to sleep in at home; having electricity at home; having running/tap water at home; living in a dwelling with concrete/cement/stone walls; living in a dwelling with a metal roof; living in a dwelling with a toilet; household ownership of various assets—bed, mosquito net, books, mobile phone, computer).<sup>39</sup> Teacher characteristics include variables reflecting gender, age, education, and training. School characteristics include indicators for whether the school has a way of “recognizing” teacher performance, availability of piped water at the school, accessibility to a road, the ratio of students to teachers, and the location of the school (urban/rural). We also control for the subject being taught during the observation.

> 34 Results disaggregated by subject are qualitatively similar (these are available online via a link on - <u>https://sites.google.com/site/decrgdeonfilmer/working papers).</u>

> 35 We also estimated results using a Rasch model, as well as simply the percent correct and results are qualitatively similar (these are available online via a link on <u>https://sites.google.com/site/decrgdeonfilmer/working-papers).</u>

> 36 This is calculated as Share of Time Teaching minus Share of Time Teaching and Learning.

> 37 See Bold et al. (2019) for further description of the teacher test.

> 38 The specific measure we use comes from an analysis of pooled data from a number of Sub-Saharan African countries.

> 39 In a number of cases, some of these variables are missing.  In such cases we replace the value by the mean across the sample, and include a dummy variable in the model that is equal to 1 if the value was originally missing.

25
to five percent a year. Setting the sum of technological progress and depreciation rate at 5 percent is in line with other studies, such as Mankiw, Romer, and Weil (1992) and King and Rebelo (1990). The data on human capital is based on the educational attainment level calculated by Barro and Lee (1993). Of the data they have compiled we use the mean for three years --1975, 1980, and 1985-- of the average number of years of education of the total population.

We assume that output in any country i is produced according to the following production

# function

Yi = Ki' Hip (A N)'a .

[1] In this equation Y is output (GDP), K represents physical capital, H is human capital, A denotes 6 the country's level of technology, and N is the number of persons in the labor force . Both ax and 6 The introduction of human capital into a production function with constant returns to scale is similar to viewing capital as a broadly defined input (i.e., one which includes both human as well as physical capital). This has also been referred by many authors as a Solow-augmented model (see, among others, Barro and Sala-i-Martin, 1995, and Mankiw, Romer, and Weil, 1992). While a model based on this production function would still imply that long-run economic growth is exogenously determined, our aim is to analyze (conditional) convergence. Endogenous growth models have little to say on this topic since their main goal is to provide an interpretation as to why countries continue to grow indefinitely without relying on the exogenously given technological progress explanation. In this sense, convergence is not the usual focus of these models.
including differences in education and experience, hours worked, occupational segregation, and gender discrimination. As transition economies move towards contribution-based systems, these tendencies should result in lower pensions for women, on average, as their total lifetime income will be lower than men's. Finally, women have higher life expectancy at retirement age than men, so they receive old-age income in average over a longer period.

**3.8** Table 2 shows the current differences in labor market characteristics in the countries in our study. While male and female labor force participation rates were high and very close dring the previous regime, women's rates have fallen in most of the ex-communist countries of East-Europe and Central Asia since the beginning of the transition. In general, female rates fell even more than men did, and the fall has been heavily concentrated in the younger and older categories. (Barry and Reilly **1999).** Recorded unemployment, however, is not significantly higher for women, which may reflect the lower participation rates at the ages most likely to suffer unemployment.

|**Table2.L **|**aborMarketC**|**haracteristics**|||
|---|---|---|---|---|
||**Kazakhstan**|**KyrgyzRepublic**|**Latvia**|**Moldova**|
|**Laborforceparticipation**<br>**(formalsector)**|||||
|**_%_**_ofworking-agepopulation_<br>Female|42.8|**38.2**|**50.2**|45.3|
|Male|**_52.5_**|_45.6_|**_59.0_**|**_52.6_**|
|**Unemploymentrates**<br>**_%_**_oflaborforce_
<mark>we should not expect any impact or a much lower impact on HAZ or stunting and we indeed find that there is no impact (Test 1, Table 5).</mark><sup>19</sup> <mark>Second, we restricted the sample to those who migrated before 2001 (during the refugee shock) but living very far away from refugee camps (in clusters/regions that are located in the South-East of the country). For this group, we should expect a much lower impact than for those who migrated in regions closer to the camps and indeed we find no significant effect (Test 2, Table 5). We also follow Baez (2011) by restricting the sample to those who were residing in Northwestern Tanzania</mark><sup>20</sup> <mark>distinguishing between geographical clusters of high refugee intensity and low refugee intensity and we find as expected significant results for high refugee intensity clusters (“North-West” in Table 5) in line with our results in Tables 3 and 4. Therefore, distance from the refugee shock is indeed a critical factor to understand the relation between child stunting and mothers’ exposure to shocks.</mark> <mark>Third, we checked whether results are not driven by a “</mark> _<mark>cohort effect</mark>_ <mark>” by comparing anthropometrics of children from mothers born after 1993 to those born before 1993 without introducing the distance to a refugee camp (Test 3, Table 5). Also, in this case we found no statistically significant differences between the two groups in terms of their children’s anthropometrics. This confirms that the cohort effect found in Tables 3 and 4 is determined by the distance from camps (the refugee crisis).</mark> <mark>Fourth, we made use of the 1990/1991 pre-refugee arrival DHS survey to study whether parents/children living further away from refugee camps were