rafmacalaba/datause-encoder-data huggingface dataset:
participation by Japanese banks (Peek and Rosengren, 2000). Similarly, while U.S. banks’ claims on foreign countries from 1984 to 2000 were not highly sensitive to GDP and interest rates in a host country, they were sensitive to GDP growth in the U.S. (Goldberg, 2002). A study of claims on emerging economies from banks in the world’s largest markets from 1992 to 2007 indicates that negative shocks to the financial health of those banks coincided with slowdowns in the growth of credit to those economies (McGuire and Tarashev, 2008).

Our overall view is that there are mechanisms by which foreign banks transmit economic problems from their country of origin to host markets, but, based on the cross-country studies and case studies of specific crises, it appears that on balance that disadvantage is outweighed by the relatively stable lending relationships forged by foreign banks and their generally stabilizing response to systemic crises in host countries.

# **_Foreign bank entry and access to credit_**

A long-standing concern is that foreign banks skim off the top customers of domestic banks, thus undermining their financial health. In principle, this competition could be so destabilizing that some domestic banks would go out of business and the overall level of credit in the host country might decline. Indeed, one theoretical model demonstrates that when domestic banks are better at relationship lending, meaning they rely on ‘soft’ information in assessing a borrower’s creditworthiness, the entry of foreign banks that are able to compete away clients that can secure loans based on hard information (e.g., balance sheets) could leave soft-information clients worse off and reduce welfare (Detragiache, Tressel, and Gupta, 2008). Ultimately, however, whether this theoretical possibility is valid is an empirical question.

Some cross-country level evidence suggests that foreign bank presence is associated with less provision of credit. For example, foreign bank presence is negatively associated with the average level of credit to the private sector (relative to GDP) and the growth rate in credit for 89 low income countries over the period 1999 to 2002 (Detragiache, Tressel, and Gupta, 2008). In effect, this is cross-sectional analysis because private credit levels and growth rates are averaged over the period of study. Some of our own research that looks more closely at the timing of foreign entry suggests that those associations might not be causal but rather are driven by the non-random entry of foreign banks into banking markets that were in crisis. First, we find strong 15
**Figure 8: Regional comparison,  public health expenditure and mortality under five**

<!-- Start of picture text --> Public health expenditure and mortality under 5, 2009<br>200<br>150<br>100<br>50<br>DOM<br>LAC<br>0<br>-50<br>Mortality<br>per 1,000<br>0 5 10 15<br>Health expenditure, public (% of GDP)<br><!-- End of picture text --> In the last decade there have been important improvements in access to the health system, but the quality of public services is still insufficient in some areas. For example, the maternal mortality rate has remained stuck at the 100/100,000 live births<sup>10</sup> level since 2005. By looking at the graph, it can be observed that, even when the DR is not one of the underperformers above the regression line, infant mortality rates are still significantly high (32 per 1000) and above regional averages (22 per 1000).

The DR ranks at the bottom of LAC region in what regards public expenditure on health, averaging 1.40 percent of GDP in 2000-07. The result is that the state is just financing around 36% of total spending, whereas individual “out of pocket” expenditure amounts 56% and private insurances just cover 8%. “Out of pocket” expenditure is clearly regressive, as the poorer households have to devote a larger share of income in relative terms, thus posing an important challenge for development.

The middle and upper strata of the Dominican population, in terms of income, usually have access to the public health system through the contributory regime, when they are employed. However, given the high levels of informality of the Dominican economy, there is a large share of the working population that has either to opt for the partially subsidized regime, or to recur to private insurances. Moreover, imperfections in the public system, together with a lack of incentives to improve quality standards causes that some of the people in the lower strata prefer to get treatment in private hospitals.

> 10 WHO country health statistics, 2010

[15]
- 2 -

# **I.  Introduction**

Competitive bidding is an important policy instrument for governments to procure goods and services at the lowest possible cost. Auction theory indicates that intense competition allows for the identification of the lowest-cost vendor or contractor in the market because the probability of winning a public contract declines for a firm as it faces more contenders in the auction (Wilson, 1977; Milgrom and Weber, 1982; Wolfstetter, 1996). As theory predicts, competitive bidding normally works well if an object to be purchased is simple and standardized, such as office supplies and computers.

In practice, however, traditional public procurement systems may not function well when purchases are complex and highly customized, such as infrastructure projects and consulting services. This is due to the fact that traditional systems by and large follow an input-based, least-cost approach, in which auctioneers need to determine all technical standards and other specifications before calling for tenders. Potential vendors or contractors, then, compete only in the price arena.

Multidimensional auction is one practical solution when various aspects—both monetary and nonmonetary—need to be evaluated at once. The theory exists (Che 1993; Cripps and Ireland 1994; Branco 1997; Asker and Cantillon, 2008) but there has been little empirical evidence to show how it actually works. In theory, a fundamental problem of multidimensional auctions is that there is a tradeoff between price and “quality,” which is often a catch phrase for anything that the auctioneer cares about other than prices. There is asymmetric information: procurers do not ex ante know how price and quality are related to each other, and therefore, have to ask bidders to propose what the best combination is. Then, bidders propose their solutions as a combination of price and quality.

This paper examines the bidding strategy in multidimensional auctions, using data from the public energy service company (ESCO) market in Japan. ESCO projects are
use a monthly panel of RAIS created at the BCB, starting in 2013 and ending in 2019. In December 2019, RAIS included nearly 51 million employees.

# **_3.e. Timeline_**

Table 2 provides a timeline of the study. It first summarizes when the financial education intervention happened in high schools (2010 and 2011). It then shows the years for which we have data from the sources described above. The table also lists the corresponding average student age for each year. At the time of the intervention, students were 16 and 17 years old on average. In this study, we use data that follows the students until they were up to 26 years old on average. Most of the administrative data is only available for the post-intervention period, except for account ownership, which we have for 2008 and 2009.

# **4. Long-Term Results**

When analyzing the long-term effects of the financial education program, we use a monthly panel and estimate the following intent-to-treat OLS specification: yi,s,r,t = α + βTreatmenti,s,r + ∑γs,rds,r + ηfi,s,r r mrt + εi,s,r,t, (2) + ∑θθ where yi,s,t is a financial or labor market outcome of student in school pair , located in state , in month . The variables Treatmenti,s,r, ds,r, fi,s,r, and εi,s,r,t are defined as in equation 1. ii ss rr Following Bruhn et al. (2016), we show results for three different specifications: (i) no controls, tt (ii) with school pair dummies, ds,r, and (iii) with school pair dummies and gender, fi,s,r. We add fixed effects for state r in month t, mrt, to all specifications to account for regional trends in dependent variables.

We also estimate a specification with time-varying treatment effects. Here, we split the postintervention years into two separate time periods: (i) 2012 to 2018,<sup>11</sup> which corresponds to the years when students may still be in university, and (ii) 2019 to 2020, when most students would have entered the labor market.

# **_4.a. Financial Inclusion and Behavior_**

Table 3 presents the effect of the financial education program on holding accounts at financial institutions. Since account ownership is the only variable for which we have comprehensive preintervention data, we also include a regression using only pre-intervention years to test for baseline balance in the administrative data (column 1). Only 6.9 percent of students had an account at a

> 11For credit use estimations (Tables 4 and 5), we use data since June 2016, when the reporting threshold of credit contracts fell significantly.  For formal employment estimations (Table 6), this period starts in 2013 due to data availability.

12
|Firms enter with<br>k**i** **t,**B**i** **t,**b**i** **t,**B**i** **t,**b**i** **t**<br>~<br>~<br>,<br>,<br>,<br>,<br>,|xt<br>revealed|Labor market<br>**(**w**t)**: l**i** **t** **p**aid<br>,<br>|Production;<br>consum**p**tion<br>|Liquidity<br>market**;**r**t,**r**t,**<br>~|
|---|---|---|---|---|
|||with bi,t<br>and xtbi,t<sup>~</sup>|(ci,t) and<br>investment (ki,t+1);<br>firms repay Bi,tand xtBi,t<br>~|Bi,t+1, bi,t+1,<br>Bi,t+1, bi,t+1<br>~<br>~|

Figure 2: The within-period timing of the model.

decisions.

Second, unlike publicly backed claims, collateral backed claims are (potentially) risky: there is a random variable _xt ∈_ [0 _,_ 1] that determines the fraction of collateral backed claims can be enforced. The use of collateral backed claims therefore may expose the economy to aggregate “enforceability” risk. Denote by _S_<sup>_T_</sup> the history of the realizations of _xt_ up to time _T_ ( _S_<sup>_T_</sup> = ( _x_ 1 _, x_ 2 _, ..., xT_ )). To simplify, this is the only source of uncertainty that I will consider.

Realistically, there is a sense that collateral-backed claims are less liquid than publicly-backed claims. To capture this, I will assume that collateral backed claims are _specialized_ , in the sense that they are not enforceable across borders. While, in principle, wage bills in one country can be paid with publicly backed claims issued in another country, collateral backed claims are only enforceable domestically. This captures the idea that collateral backed claims are more specialized, and, in some sense, serve as “inside money” within a domestic financial system. This assumption will not be essential for any of the theoretical results, but will play a role in the numerical simulation.<sup>7</sup> To summarize, let _Bi,t_ denote the total amount of publicly backed liquid claims that firm _i_ can issue, and let _B_<sup> ̃</sup> _i,t_ denote the total amount of privately

> 7In the numerical simulation, this assumption affects the wage dynamics associated with an enforceability shock. Specifically, if emerging markets hold privately-backed liquid claims issued by developed economies, part of the instantaneous adjustment in wages would take place in emerging economies.

9
Figure 2: Poverty headcount and opportunity-sensitive poverty headcount. Rankings <!-- Start of picture text --> GR<br>LT<br>ES<br>LV<br>IT<br>PL<br>EE<br>CY<br>FR<br>HU<br>LU<br>BE<br>SK<br>FI<br>NL<br>AT<br>DE<br>CZ<br>0 5 10 15 20<br> FGT(0) rank<br>20<br>15<br>10<br> Op. FGT(0) rank<br>5<br>0<br><!-- End of picture text --> _Source: Authors’ calculation from EU-SILC (2005)_ with similar levels of poverty. The third group is composed of Eastern European and former Soviet Union countries with even higher standard headcount poverty, but somewhat lower opportunity-sensitive headcounts.

Where do these cross-country differences in opportunity-sensitive poverty come from? Differences in opportunity-sensitive poverty across distributions arise from three basic sources: differences in overall poverty levels in the population (the level effect); differences in the distribution of poverty across types (the distribution effect); and differences in population shares across types (the population composition effect). For the case of the headcount OSPM, defined in Equation 5 as _PH_ ( _F, z, T_ ) = _n_<sup><u>1</u></sup> � _ni_ =1<sup>_q_</sup> _i_<sup>_F_(</sup><sup>_n_+1</sup><sup>_−r_(</sup><sup>_i_))</sup><sup>_Fi_(</sup><sup>_z_)</sup><sup>_,_the difference between two countries,</sup> _F_ and _G_ , can be written as follows: where the two counterfactual poverty incidence measures are given by: Analogous decompositions can straightforwardly be written for _α >_ 0, by replacing _Fi_ ( _z_ ), with �0 _z_ � _<u>z−z x</u>_ � _α fi_ ( _x_ ) _dx_ . Notice that the first term in brackets on the right-hand side of Equation 6 denotes the _level effect_ , which is obtained by scaling up poverty incidence across types in country _F_ by a common factor, so 15
species, both rated as Critically Endangered. B is populated by 20,000 species, but all are rated as of Least Concern. Our extinction risk indicator for each area is the sum of normalized extinction probabilities for resident species. Assignment of weights for Mooers' IUCN-derived 50-year extinction probabilities yields a total risk indicator of 2 for A -- twice the total for B, because each Critically Endangered species is weight-equivalent to 20,000 Least Concern species. In contrast, assignment of the Isaac weights yields an overall risk rating for B (1,250) that is 625 times greater than the rating for A (2), because each Critically Endangered Species is weight-equivalent to 16 species of Least Concern. The other two cases are intermediate, but far closer to the 50-year IUCN case.

**Table 8:  Implications of alternative weighting schemes**

||||T|otal<br>E<br>Pro<br>Fu|Scores<br>IUCN<br>xtincti<br>babilit<br>tureY|<br>on<br>ies:<br>ears|
|---|---|---|---|---|---|---|
|Area|Species<br>Count|Status<br>(Uniform<br>Within Areas)|Isaac|50|100|500|
|A|2|CR|2|2|2|2|
|B|20,000|LC|1,250|1|2|10|

# **7.3  Biome Vulnerability**

Measures based on animal species alone provide an incomplete accounting of biodiversity.

A more complete measure would incorporate plants and insects, using indices similar to those we have developed for animals. Although no such indices exist at the requisite geographic scale, WWF has provided a first approximation by segmenting the world into 825 terrestrial ecoregions (Figure 16). WWF defines an ecoregion as “a large unit of land or water containing a geographically distinct assemblage of species, natural communities, and environmental 41
# Bosnia and Herzegovina 2001-2004: Enterprise Restructuring, Labor Market Transitions and Poverty

Erwin R. Tiongson and Ruslan Yemtsov<sup>1</sup> The World Bank, 1818 H Street, NW, Washington, DC 20433 JEL classification number: J21, J64, I30 Keywords: labor market transition, unemployment, poverty

> 1 E-mail: ryemstov@worldbank.org, etiongson@worldbank.org.
– major region of the world more so in Latin America or the Middle East than in Asia. The largest declines occurred in the first three pandemic months and the subsequent recovery has been only partial. Using local area poverty estimates, the paper finds high lockdown stringency significantly reduced mobility even in high-poverty areas, although to a lesser degree than in low-poverty areas. Google Mobility Trends data also show substantial reductions in workplace visits during the first months of the pandemic in all sub-Saharan African countries where data are available. These mobility reductions happened during periods of stringent lockdowns. On April 15th, for example, the number of visitors to workplaces were 50-75% lower than the pre-pandemic level in Uganda, South Africa, Botswana, Namibia and Zimbabwe, and 25-50% lower in in Kenya, Tanzania, Angola, Gabon, Nigeria, Ghana, Guinea-Bissau, and Senegal.<sup>9</sup> Even if there were pronounced economic downturns related to the pandemic, it may still be that low-income country lockdown policies were comparatively ineffective, and the reductions in economic activity came purely from private behavioral responses. Emerging evidence suggests, however, that lockdowns did have some effect. Beyer et al. (2021) exploit differential timing and severity of lockdown measures across 600+ Indian districts and a difference-in-differences estimator to show that more severe lockdown restrictions curtailed economic activity substantially at the district level, relative to localities with less severe restrictions. Thus, it appears that lockdown policy interventions did have a direct effect on economic activity in India.<sup>10</sup> Finally, we note that throughout our analysis, there continues to be community transmission that is not entirely eliminated by the lockdown, and we calibrate community transmission to be higher in developing countries. This feature of our quantification could be thought of as one means to capture lower state capacity in poorer countries.

> 9The data are here: https://ourworldindata.org/covid-google-mobility-trends.

> 10Ultimately it may never be possible in the data to cleanly separate the impact of enforcement of lockdown restrictions _per se_ from private behavioral responses, if – plausibly – agents have imperfect information. When households do not perfectly observe the exact COVID-19 prevalence in their area and do not perfectly know the associated risks, a lockdown imposed by a policymaker signals to the households that the outbreak is severe, and thus induces a belief update and a greater private behavioral response. The signal value of a lockdown means that a policy may have a marginal effect (relative to no policy) even in environments with low state capacity to enforce lockdowns.

19
Our paper contributes to the literature on the nexus between urbanization and poverty. Several studies show urban-rural gaps in productivity, wages, and amenities (see Lagakos 2020 for a review). Some studies find earnings and welfare gains from rural to urban migration in the developing world, such as rural to urban migration in Tanzania (Beegle, de Weerdt, and Dercon 2011), seasonal migration in Bangladesh (Bryan, Chowdhury, and Mobarak 2014), and the interplay of locations and migrant characteristics in determining gains in China (Combes et al. 2020). Hamory et al. (2021) analyzed panel datasets in Indonesia and Kenya, finding that a large part of the measured returns from migration came from the sorting of migrants. However, very few have analyzed the role of climate change as a hindrance to urban agglomeration as the urban escalator out of poverty. Therefore, we attempt to shed light on the role of the climatic and environmental shocks on the urban escalator out of poverty.

The rest of the paper is organized as follows. Section 2 explains the framework with a description of our hypotheses. In Section 3, we elaborate on the methodology with the description of the datasets and our econometric approach. Section 4 reports the results of our econometric analysis. Finally, Section 5 concludes with the summary of findings and discussion, policy implications, and limitations.

# **2. Framework**

## **Urban escalator out of poverty: Hypothesis 1**

Larger or denser urban areas potentially provide people with ample economic opportunities to escape from poverty, through better access to higher-wage jobs, infrastructure, services, and so on. As a result, more urbanized areas tend to be characterized by higher incomes and consumption, higher productivity, better access to services, and higher human capital. Earlier studies have found that nominal wages are higher in larger or denser cities due to productivity gains from agglomeration economies in developed countries (Glaeser and Mare 2001; Melo, Graham, and Noland 2009; Puga 2010; Rosenthal and Strange 2004) and in the developing world (Chauvin et al. 2017; Combes et al. 2022; Grover, Lall, and Timmis 2021; Quintero and Roberts 2022). Rural to urban migration can be welfare enhancing, given the gaps in income and amenities in the developing world (Lagakos 2020). When both poverty and urban areas are measured in a comparable way across countries, poverty tends to be lower in dense urban areas (Combes et al. 2022) (Figure 1).<sup>2</sup>

> 2 On the other hand, some studies highlight urbanization without growth (Bloom, Canning, and Fink 2008; CastellsQuintana and Wenban-Smith 2020; Fay and Opal 1999; Gollin, Jedwab, and Vollrath 2016).

3
|||**Site 1**||**Site 2**|
|---|---|---|---|---|
|||Attenuation of||Attenuation of|
|Scenario|Surge Level<br>(m PWD)|<br>surge height from<br>the baseline(cm)|Surge Level<br>(m PWD)|<br>surge height from<br>the baseline(cm)|
|50m_Sonneratia apetala_at 5m spacing<br>followed by 50m_Avicennia officinalis_at<br>6m spacing|4.002|5.151|4.159|5.219|
|50m_Sonneratia apetala_at 7.5m<br>spacing followed by 50m_Avicennia_<br>_officinalis_at 4m spacing|4.002|5.140|4.160|5.177|
|50m_Sonneratia apetala_at 7.5m<br>spacing followed by 50m_Avicennia_<br>_officinalis_at 6m spacing|4.003|5.100|4.160|5.150|

Table 5A: Attenuation of surge heights from afforestation of mangrove species in Study Area 2

|||<br>**Site 3**||<br>**Site 4**|
|---|---|---|---|---|
|||Attti f||Attti f|
|Scenarios|Surge Level<br>(m PWD)|enuaon o<br>surge height from<br>the baseline(cm)|Surge Level<br>(m PWD)|enuaon o<br>surge height from<br>the baseline(cm)|
|Baseline: without mangroves|**4.223**||**4.238**||
|Only 50m_Sonneratia apetala_at 5m<br>spacing|4.176|4.677|4.188|4.981|
|Only 50m_Sonneratia apetala_at 7.5m<br>spacing|4.177|4.603|4.189|4.889|
|Only 50m_with Avicennia officinalis_at<br>4m spacing|4.177|4.619|4.189|4.912|
|Only 50m_with Avicennia officinalis_at<br>6m spacing|4.177|4.589|4.191|4.739|

19