rafmacalaba/data-use-mentions-extended huggingface dataset:
# **IV.   EAST ASIAN GROWTH INCIDENCE CURVE**

Over the past two decades and a half, average consumption in EAP as a whole increased by more than three times (or about 5.5% per annum). The EAP GIC over the period 1988 to 2012 (Figure 6) shows how this growth was distributed along the distribution.<sup>19</sup> For example, it shows how the average consumption of the bottom 5% in 1988 compares with the average consumption of the bottom 5% in 2012. With the exception of the very top ventile group, the growth incidence was pro-rich. More specifically, the annual growth of the bottom half was lower than the growth in the mean, while the annual growth rates were relatively high among the 12<sup>th</sup> to 19<sup>th</sup> ventile (60<sup>th</sup> to 95<sup>th</sup> percentile) groups.

Growth dropped significantly for the top ventile group. This is largely explained by its relatively low growth during the Asian financial crisis period, as shown by the GICs for the separate 5-year periods between benchmark years (Figure 7). The GICs are upward sloping for almost all periods. However, it is interesting that in the most recent period, which spans the global financial crisis, growth appears to have been slightly pro-poor.

It is important to stress that the discussion so far has focused on growth rates, or relative gains, which can imply very different absolute gains in expenditures. Figure 8 shows the EAP GIC but with absolute gains (per year) in expenditure on the vertical axis. It is thus equivalent to the difference between the Pen’s Parades (or quantile functions) in 2012 and 1988.<sup>20</sup> The mean expenditure of the richest 5% of East Asians rose by nearly PPP-2011 $400 per annum between 1988 and 2012 (or more than PPP-2011
1.9|2.9|3.8|3.6|‐2.9|‐9.3|‐7.5|‐7.2|‐7.5|‐3.5|‐2.5|
|EA‐19*|3.5|1.6|0.7|0.0|0.1|0.3|1.3|2.2|0.8|‐3.5|‐3.4|‐1.2|‐0.6|‐0.2|0.1|
|EU‐28*|‐|1.8|0.5|‐0.3|‐0.2|0.1|1.0|1.7|0.3|‐4.1|‐3.8|‐1.6|‐1.4|‐0.6|‐0.4|

*: weighted average.

Source: European Commission, Annual macro‐economic database By all measures, the countries undertook substantial fiscal adjustments, but Greece has done the most by some measures as shown in Tables 5c and 5d. Greece’s primary _structural_ budget was actually in better shape than that of IPS in every single year AND the pace of adjustment was faster during 2010‐ 2012. The heavily front‐loaded Greek adjustment, on the heels of the financial crisis induced recession, and tightening monetary policy (the ECB raised its reference rate during this time (Figure 10a)), had all the conditions for a strong negative impact on output. Comparison with the EU and EZ averages shows the much larger adjustment the GIPS adopted Another way to look at the size of the adjustment governments made is to consider actual expenditure decisions (a policy variable), abstracting from the fall in GDP (not a policy instrument). Greece’s performance is striking. In 2010‐2014, real expenditures
# 4. Methods

The key survey data we use is the 2014 MCS-ENIGH. Although we are interested in estimating poverty for all municipalities in Mexico, the survey is not designed to estimate poverty at the municipality level, but rather for urban and rural areas within states. Therefore, the direct estimates from the survey will not consistently estimate poverty accurately for municipalities.

The rest of this section describes how we construct the direct estimates and the household-model estimates, as well as two robustness checks using sub-area and area models.

## 4.1 Direct estimates

The direct survey estimates for each municipality are the weighted mean, across sample households, of a dummy variable indicating whether the household’s per capita income falls below the official poverty line, which is defined separately for urban and rural areas. These are taken for each municipality, using the product of the household sample weight and household size as weights. Estimating uncertainty properly is a bit more complicated. This is because standard methods assume that per capita income is independent across primary sampling units, which can significantly underestimate the uncertainty associated with municipal poverty estimates. We therefore use two methods for estimating uncertainty: the Horvitz-Thompson approximation (Horvitz and Thompson, 1952) and the standard method of clustered standard errors, using AGEBs as the clustering unit (Huber, 1967). As shown below, the Horvitz-Thompson approach gives much more accurate estimates of uncertainty and is therefore preferred.

## 4.2 Household model estimates

The baseline method for the household model is similar to the method used to predict nonmonetary poverty rates in Masaki et al. (2022), in that it relies on a conditional random effect model, which is also often referred to as a mixed model. Many previous iterations of householdlevel models rely on detailed census data (e.g., Elbers, Lanjouw, and Lanjouw (2003) and Tarozzi and Deaton (2009)). However, such data is usually not available for many developing countries, while satellite imagery is. As such, instead of using
, authorities announced several economic measures, including additional expenditure on healthcare, provision of emergency food to the vulnerable, tax and social security payment deferrals, and liquidity injections and extension of forbearance measures in the financial sector.

The restrictions imposed by the government on people’s movement and business operations in the early stage of the pandemic resulted in a sharp decline in mobility levels. As shown in Panel A of Figure 1, the Facebook movement data indicate that the mobility level plummeted in April 2020 to 50 percent of the baseline level—which is February 2020. This reduction in mobility was caused by both people’s voluntary responses and government restrictions on public gatherings and transport services between regions.<sup>5</sup> Unlike other countries, there was no enforcement for the closure of nonessential businesses, including restaurants, and within-region transport services.

> 4 Andersson et al. (2018), for example, find that better job accessibility reduced the duration of joblessness among low-income workers—especially blacks, women, and older workers—after a massive layoff in the United States. In the context of cities in developing countries, Franklin (2018) finds that a transport subsidy to young workers resulted in their successful job search in Addis Ababa, Ethiopia. Indeed, many workers face a limited level of job accessibility in Sub-Saharan African cities, as shown by Nakamura and Avner (2021) for Nairobi, Kenya.

> 5 Ethiopia is a country with a federal system, consisting of 10 regions and 2 chartered cities.

4
PPP exchange rate per country.

22 Schultz (1998) however tries to account for intra-household inequality by using genders gap in schooling (for each country) to estimate gender gaps in income at the household level.

23 The meaning of this sentence is that the ratio of food prices consumed by the poor in (say) Indonesia to the food prices of the same goods in the US is higher than the ratio between the overall price level in India and that in the US.

24 This reason behind income overestimation is as follows. The weights in the “world” consumption basket of goods and services are decisively influenced by the prices and the structure of consumption in rich countries since they are obviously the largest consumers. Then, a relatively high consumption of services in poor countries (which are cheap there) but are assessed at much higher “world” prices tends to show poor countries’ (and poor people’s) income higher than it “really” is. One possible way to adjust for this is to move from the commonly used Geary-Khamis index which has the just explained property to more “neutral” price indexes (Afriat or EKS) where the weight of rich countries is less (see Dowrick and Akmal, 2001).

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year primary classes at year .<sup>22</sup> i is a vector of municipal controls including the number of pre-schools, total male to female sex ratio, total and per-recipient Bolsa Família transfers, and—tt−5 XX both in logs and four-year log-differences—municipal GDP and public spending (welfare, education,<sup>23</sup> health, transportation, and housing). Unobservable heterogeneity is captured by a year trend, , a municipal fixed effect, , and a time-varying ss ii ii ii municipal component, yyf yy , a state × year specific shock, δδi . ηη The resulting estimate, εε , reflects the intention-to-treat effect of a one unit increase in secondary school density on teenage birthrates. Municipal differences of the within-variation in αα� the secondary school expansion between the years 1997 and 2005 identify this coefficient.<sup>24</sup> For an unbiased estimate of we assume strict exogeneity between the error term and school introductions. This assumption is supported by the analysis in Section 4. We subject this αα assumption to further tests and specifications in sections 5 and 6. The remarkable stability of our results across specifications indicate to us that this assumption is plausible.

**5. Results** Estimates for Equation (3) are presented in Table 3. The coefficient of interest, , is reported in the first row [ _Secondary school density (t-4)_ ]; estimates for selected control variables in αα i and P XX ii,ii−5<sup>are reported in the remaining rows. All regressions include a year trend and municipality</sup>

> 22 The first observation for P 23 This largely captures municipal spending on pre-primary and primary education, rather than on secondary education ii,ii−5<sup>(at tt= 2001) is constructed from the 1996 wave of the Brazilian School Census.</sup> (which is the responsibility of the states). Excluding education expenditure leaves the estimates virtually unchanged
# Foreign Banks and Trade: Bridging the Information Gap?<sup>_∗_</sup>

Francesca de Nicola Shawn W. Tan The World Bank The World Bank

**JEL Classification** : F14 Empirical Studies of Trade, G2 Financial Institutions and Services, O1 Economic Development, O5 Economy wide Country Studies. **Keywords** :Trade, Credit constraints, Foreign banks, Regional development

## **1 Introduction**

Foreign banks can promote exports by providing trade financing, enforcing contracts and reducing information asymmetries. Claessens, Hassib, and van Horen (2015) show that sectors that are more dependent on external finance export more when a foreign bank is present in the country. They focus on the financing aspects of foreign banks, but they do not examine the other roles foreign banks may play in boosting exports. Indeed foreign banks can also reduce information asymmetries for exporting firms, especially in countries where there are fewer financing constraints. These foreign banks can help firms assess the profitability of a given product in the foreign market and provide valuable information about export market conditions. It is difficult, however, to disentangle the information role foreign banks play from the financing role.

In this paper we examine the effects foreign banks have on exports of sub-regions in Turkey. Turkey has experienced a large expansion in exports, growing by over 250 percent between 2002 and 2008. The export boom can be attributable to a variety of reason such as devaluation of the Turkish lira, improved macroeconomic conditions, and entry into the EUTurkey Customs Union.<sup>1</sup> The focus on Turkey allows us to isolate the bank’s information

> _∗_ We thank Mehmet Celbis for kindly sharing the sub-regional trade data. 1See Aysan and Hacihasanoglu (2007) for a discussion on the macroeconomic determinants of Turkeys export boom and Cebeci and Fernandes (2015) for an analysis of the microdynamics behind this growth.
health system capacity, quality of care, infection prevention and control practices, and availability of personal protective equipment, could have age-specific effects on the IFR .<sup>28,34</sup> A form of survivor bias may be a factor as well. High quality medical care in wealthier countries keeps many older people alive despite weak health conditions that make them vulnerable to COVID-19. It may be that older people in developing countries are on average healthier than those in wealthier countries because those with ailments would have been at high risk for death at a younger age. If this is the case, older people in developing countries might be more resilient to the disease.

Distinguishing various explanations for the observed age patterns of mortality identified in this paper should be a topic for further research. The analysis in this paper is also a reminder of the value of demographic data and in particular vital statistics as a tool amid the COVID-19 crisis. Excess mortality analysis by age presented here is only possible for countries that have wellfunctioning vital statistics systems and that have made their data widely available. Investments in vital statistics systems and greater data transparency should be encouraged as a global public good.

# **Data Availability**

_Data sources_ Official reported COVID-19 deaths aggregated in the COVerAGE database For this analysis the output file with death counts aggregated to 5-year age-groups is used.

There are 114 countries in the COVerAGE database (excluding England and United Kingdom). For this analysis, the political units that make up the United Kingdom are treated separately as follows: England and Wales, Scotland, and Northern Ireland.

84 countries have at least one record of cumulative deaths during 2020 or 2021.

64 countries have a record of cumulative deaths that covers at least 6 months since the first case was detected in the country (per case-data derived from Our World in Data). Countries with an “exposure” of less than 6 months as of the end of 2020 are excluded from the analysis.

16
19 international remittances are evidently allocating their increments to expenditure to try to take advantage of these higher rates of return to secondary and university education.

# 6.  Conclusion

This paper has used a large, nationally representative household survey from Guatemala to analyze how the receipt of internal remittances (from Guatemala) and international remittances (from the United States) affects the marginal spending behavior of households on various consumption and investment goods. Three key findings emerge.

First, contrary to other studies, this analysis finds that the majority of remittance earnings are not spent on consumption goods. In fact, at the mean level of expenditure, this study finds that while households without remittances spend 58.9 percent of their increments to expenditure on consumption goods – food and consumer goods, durables – households receiving internal and international remittances spend 54.2 and 55.9 percent, respectively, on consumption goods.<sup>xi</sup> In other words, at the margin, households receiving remittances actually spend less – not more – on consumption than do households without remittances. There is no evidence here that households receiving remittances tend to “waste” their remittance earnings on “conspicuous” consumption.

The second finding follows closely from the first. This study finds that the marginal spending behavior of households receiving remittances is qualitatively different from that of households which do not receive remittances. Instead of spending more on consumption, households receiving remittances tend to view their remittance earnings as a temporary (and possibly uncertain) stream of income, one to be spent more on
is binary. The identifying assumption in (1) is that conditional on observable variables X�� and time fixed effects T�, and in the absence of economic shocks, the trend of the dependent variable would have been parallel between Afghan refugees and the host communities. For the LFS sample, we estimate (1) for all individuals in the data and control for age, household size, job experience, having insurance, education, occupation, and dummies for being household head, being married, and gender. We estimate (1) at the household level for the HEIS sample and control for household size and head’s characteristics, including age, gender, literacy, occupation, and being married.

The second approach for estimation is based on a matching method. We match each Afghan household or individual with an Iranian counterpart as explained in section 4.1.1 and then compare the heterogeneous impact of economic shocks between the two groups by estimating (1) but without controlling for variables X�� and time fixed effects T�. The identifying assumption of this approach is that in the absence of shocks, the trend of Afghan refugees’ outcomes would have been similar to the matched sample of Iranians. This approach is similar to matched difference-in-difference strategy, as described in Heckman et al. (1997, 1998).

# **5. Results**

In this section, we present our key findings. We first discuss the results regarding the heterogenous effect of economic shocks on the labor market, income, and consumption. Next, we present our findings on social cohesion measures. We then examine the asymmetric effect of negative and positive economic shocks on the outcomes. Finally, we study how inter-community and intra-community inequalities are affected by economic shocks in Iran.

**5.1. The unequal effect of economic shocks on the labor market** Panel A of Table 3 presents the regression results of estimating (1) using outcome variables computed from LFS surveys. The coefficient of the Afghan
2 (1) This differential equation has particular solution, so wealth is just the present value of consumption along the profit-maximizing path.

Since genuine saving for this economy is given by _G_ ≡ _K_<sup>&</sup> − _FR R_ , expression (1) can be rewritten as, This is the expression tested by Ferreira _et al._ (2003) – genuine saving plus capital gains at time _t_ should equal the difference between a particular average of future consumption and current consumption. This approach to the problem entails two restrictive assumptions – profit maximization and constant returns to scale – and encounters one considerable practical problem, the measurement of capital gains. Although expression (2) shows only capital gains on the exhaustible resource, a more general model would suggest that all capital gains should be included – however, cross-country time series data on capital gains are lacking.

An alternative approach to testing genuine saving may be derived from Hamilton and Hartwick (2005). For the same profit-maximizing model, This has a particular solution, which provides the basic test of saving employed below: current genuine saving should equal the present value of future changes in consumption. This is a more parsimonious model, requiring only profit maximization. Hamilton and Withagen (2004) show that this result holds for a much more general model provided that the economy is competitive (households maximize utility
and Pierola (2016). The underlying data for Costa Rica cover the universe of exporting firms over 1998–2012 and provides information on export value and quantity at the firm-product-destination-year level. The data cover 14,253 firms exporting to 211 destinations. We limit our analysis to destinations with population greater than 5 million, which reduces the sample to 12,918 firms and 115 destinations. Costa Rican data provide a good environment for our analysis due to the extensive time coverage and diversified export profiles in terms of products and destinations.<sup>9</sup> The data also exhibit high rates of firm turnover, providing sufficient dynamics to

> 8The legal enforceability of each provision is assessed based on the presence of dispute settlement or legal language. For more information on the database and coding, see Hofmann et al. (2017). The database is publicly available at https://datacatalog.worldbank.org/dataset/ content-deep-trade-agreements.

> 9Costa Rican firms export an average of 7.9 HS4-products, with a median of 2. In terms of destinations, the average is 3 and median is 1. See Appendix Table A.2 for their top exported products ranging across various industries.

4
# **1 Introduction**

In the first half of 2020, the UNHCR reported over 80 million people as forcibly displaced, and expects this trend to continue in the near future (UNHCR, 2020). Although the constitutions of many countries guarantee protection for people fleeing from conflicts and wars, hosting refugees remains a divisive policy issue. On the one hand, governments view hosting refugees as a moral obligation. If countries of origin cannot guarantee the safety of their citizens, other countries have the humanitarian duty to provide protection, and State parties that signed the Refugee Convention of 1951 also have a legal obligation to do so. However, hosting and integrating refugees comes with economic and social challenges. Common economic and social concerns about the effects of immigration also apply to refugee inflows: natives may worry about the effect on the labor market, the welfare state, or crime. Moreover, natives may perceive refugees as outsiders, which may spur xenophobia and undermine the social fabric of the country.

In this paper, we investigate whether hosting refugees affects social cohesion in a major receiving country. We study the case of Germany, which recently welcomed over one million asylum seekers and refugees from Syria, Afghanistan and the Western Balkans. Although the inflow initially received broad support in the German population, attitudes towards refugees in parts of society drastically changed after a series of events on New Year’s Eve 2015/16. The most salient incident occurred in Cologne, where around 600 women were sexually assaulted, with some of the perpetrators being recently arrived asylum seekers. This event, along with the growing general discontent about the country’s immigration policy is often seen as an accelerator for the growth of the far-right movement in Germany (Gedmin, 2019).

To study the impact of the refugee inflow in 2015/16 on social cohesion, we use data from a large-scale panel survey as well as regional data on incidents of anti-immigrant violence. We measure social cohesion along several dimensions, namely generalized trust, perceived fairness, attitudes
Unlike the measure of localization, which focuses on one industry, the industrial diversity index considers the industry mix of the entire city economy. The largest value for is one when the entire city economy is dominated by a single industry. Thus a higher value signifies lower levels of economic diversity.

Business environment is proxied by the amount of license fees and taxes paid by firms in given industries and cities over time. Infrastructure access is measured by the cost of transportation, electricity and bank loans, and rents proxy the cost of land. To control for time-invariant factors, year fixed effects are included. City fixed-effects control for factors common to the city. And finally, to account for firm-level differences and to understand dynamic changes, firm fixed-effects are included.

The response variable is the log of employment growth at the firm-level. In model (1) the economic geography variables are introduced, followed by business environment variables in model (2) and then by infrastructure variables in model (3). In model (4) city-level dummies are included, supplemented by year dummies in model (5) and industry dummies in model (6). Finally in model (7) firm dummies are included (which 22