rafmacalaba/datause-encoder-data huggingface dataset:
46

# **Saigon River Height**

There are several factors that contribute to a change in the Saigon River’s height:

- Eustatic sea level rise brought about by anthropogenic climate change;

- Subsidence caused by falling groundwater levels and other processes; and

- Construction of dikes, which may narrow the width of the river.

Subsidence and dike construction may vary locally. However, modeling local variation sin SWMM is a significant modeling effort and beyond the scope of this project. Instead, for demonstration we consider uniform subsidence and dike construction. Additionally, because SWMM cannot model overland flow, the effects of these changes on the model are equivalent: they each change the difference in height between the outfall and the Saigon river. Thus, we vary a single uncertainty – Saigon River height – that encompasses many different potential contributors to this effect.

The Vietnam Ministry of Natural Resources and the Environment (MONRE) has developed a series of SLR projections for Vietnam (2009). These are based on the IPCC Fourth Assessment Report (IPCC, 2007) using the IPCC B1, B2, and A1F1 emissions scenarios, which correspond to low, medium, and high emissions. In these scenarios, SLR ranges from 28cm to 33cm by mid-century. Consistent with traditional single-scenario methods, MONRE further recommends that the medium scenario be used for planning.

However, recent research suggests that the Fourth Assessment Report estimates may significantly underestimate sea level ride because they do not take into account rapid ice flow changes. Two sources provide data that takes into account rapid ice melt. Vermeer and Rahmstorf (2009) and NOAA (2012) both estimate that SLR by 2045 could be approximately 40cm Ho Chi Minh City is built upon marsh and swampland, and development coupled with groundwater extraction and development has resulted in significant subsidence over the past years. A study of by MONRE (2010) found that subsidence in the city varied from 1 cm/year to, in some places, 5 cm/year, on average between 1 and 3 cm/year. Assuming even a modest rate – 1 cm/year --, subsidence could reach 25 cm by 2025 and 45 cm by 2045 over 2000 levels. The Water Resources Plan for Flood Control in Ho Chi Minh City (MARD, 2008) proposes the construction of 172km of dikes around Ho Chi Minh City and 13 tide gates at the mouth of the drainage canals that discharge in the Saigon River. By constraining the expansion area of the river, the dikes may increase the height of the river. In discussions with the project team, the Steering Center for Flood Control estimated an increase of 20-30cm from dike construction and noted that a revised plan for dike construction is being considered to reduce these effects.

The combined effect of eustatic sea level rise, subsidence, and dike construction may be an increase in river height of over a meter by 2045. However, for the purposes of this study, we consider a range between 20 and 100cm. Levels less than 20 cm are inconsistent with climate and subsidence projections. Levels higher than 1m would result in overland flow and permanently or semi-permanently inundate certain areas. This
**Table 3: Sample statistics, by type of wage rigidity**

|Variable|Mean<br>DNWR<br>(9.6% of<br>firms)|Mean<br>DRWR<br>(16.7% of<br>firms)|Mean<br>FW<br>(73.7% of<br>firms)|t-stat<br>DNWR/<br>FW|t-stat<br>DRWR/<br>FW|Obs<br>(total)|
|---|---|---|---|---|---|---|
|Low-skilled blue-collar (%)|0.355|0.433|0.399|-3.929|4.826|13408|
|High-skilled blue-collar (%)|0.276|0.212|0.249|2.918|-6.424|13408|
|Low-skilled white-collar (%)|0.137|0.185|0.151|-1.968|8.271|13408|
|High-skilled white-collar (%)|0.231|0.170|0.201|3.699|-6.623|13408|
|Covered workers (%)|0.457|0.797|0.520|-3.648|25.937|11696|
|Only firm-level agreement|0.097|0.096|0.075|2.527|3.835|13426|
|Only outside agreement|0.333|0.649|0.392|-3.623|25.857|13426|
|Firm-level and outside agreements|0.138|0.148|0.175|-2.959|-3.511|13426|
|<br>No union contract|0.432|0.107|0.358|4.580|-27.816|13426|
|Permanent workers (%)|0.911|0.908|0.899|1.793|2.213|13449|
|<br>Tenure up to 1 year (%)|0.135|0.147|0.155|-2.900|-1.195|7608|
|Tenure 1–5 years (%)|0.366|0.353|0.375|-0.976|-2.303|7605|
|Tenure over 5 years (%)|0.494|0.497|0.467|2.318|2.595|7605|
|<br>Labour cost (%)|0.349|0.330|0.333|2.325|-0.672|12243|
|<br>Sector = Manufacturing|0.426|0.414|0.412|0.861|0.224|13551|
|<br>Sector = Energy|0.005|0.023|0.010|-1.634|5.291|13551|
|<br>Sector = Construction|0.068|0.087|0.068|0.052|3.558|13551|
|Sector = Trade|0.184|0.209|0.203|-1.394|0.796|13551|
|Sector = Market services|0.274|0.247|0.273|0.095|-2.863|13551|
|Sector = Financial interm.|0.015|0.016|0.019|-0.938|-1.156|13551|
|Sector=Non-market services|0.027|0.004|0.015|2.940|-5.070|13551|
|Country = Austria|0.043|0.019|0.045|-0.218|-6.612|13614|
|Country = Belgium|0.001|0.401|0.002|-0.706|77.695|13614|
|Country = Czech Rep.|0.094|0.011|0.027|11.382|-5.084|13614|
|Country = Estonia|0.067|0.004|0.026|7.219|-7.738|13614|
|Country = Spain|0.019|0.299|0.087|-7.563|30.917|13614|
|Country = France|0.144|0.045|0.156|-0.974|-16.531|13614|
|Country = Greece|0.038|0.021|0.027|2.105|-1.722|13614|
|Country = Hungary|0.123|0.070|0.175|-4.185|-14.752|13614|
|Country = Ireland|0.072|0.024|0.087|-1.628|-12.248|13614|
|Country = Italy|0.036|0.005|0.094|-6.144|-17.298|13614|
|Country = Lithuania|0.057|0.008|0.027|5.396|-6.435|13614|
|Country = Poland|0.081|0.017|0.080|0.159|-12.645|13614|
|Country = Portugal|0.205|0.033|0.115|8.310|-13.848|13614|
|Country=Slovenia|0.019|0.043|0.053|-4.721|-2.349|13614|
|Size = 5–19|0.210|0.320|0.230|-1.387|10.327|13612|
|Size = 20–49|0.217|0.235|0.229|-0.832|0.708|13612|
|Size = 50–199|0.365|0.252|0.318|3.058|-7.081|13612|
|Size=200+|0.207|0.193|0.224|-1.198|-3.720|13612|
|Price comp = very likely|0.192|0.175|0.174|1.369|0.141|11412|
|Price comp = likely|0.459|0.379|0.456|0.167|-6.469|11412|
|Price comp = not likely|0.286|0.319|0.306|-1.275|1.124|11412|
|Price comp=not at all|0.064|0.127|0.064|-0.072|9.888|11412|
|Perceived comp = severe|0.459|0.397|0.402|3.243|-0.306|8803|
|Perceived comp = strong|0.438|0.490|0.488|-2.813|0.134|8803|
|Perceived comp = weak|0.078|0.088|0.078|0.037|1.027|8803|
|Perceived comp=none|0.025|0.025|0.032|-1.141|-1.115|8803|

17
for developing countries through household surveys such as the DHS, and typically are infrequently collected.

The rest of Section 3 details the specific steps of this procedure for calibrating the effects of health on worker productivity. Section A3.2 discusses micro‐econometric estimates of the return to height, γ������ . Section A3.3 discusses estimates of the relationship between adult height and adult survival rates, β������,��� . Section A3.4 discusses how to calibrate estimates of the change in adult height attributable to reductions in stunting, β������,�������� .

# **A3.2  Estimates of the Return to Height**

Weil (2007) uses a baseline estimate of γ������ �0.034 , i.e. one additional centimeter of height raises earnings by 3.4 percent. This evidence is taken from two previous studies comparing height and earnings within twin pairs in the United States and in Norway. The main strength of these twin studies is their identification strategy of relying in random variations in birthweight between twins as a plausibly exogenous source of variation in their eventual adult heights. However, one might reasonably be concerned with the external validity of these findings, since these studies are based on pairs of twins in two advanced economies (Norway and the United States). To assess this concern, it is useful to briefly consider three other sets of estimates of returns to height from 19 other studies covering a range of developed and developing countries. All of these other estimates are based on instrumental variables regressions of log wages on height, with instruments of varying degrees of plausibility. Conditional on the validity of the instruments, these should all recover the effect of height on wages conditional on education, either because education is included in the regression, or because the instrument is uncorrelated with omitted education (under the null hypothesis that the exclusion restriction holds).

The first set can be found in Table 1 of Weil (2007), which reports estimates of the return to height conditional on education from three studies in Colombia, Ghana, and Brazil in the 8 to 9 percent per year range. The second set is summarized in Table 1 of Galasso and Wagstaff (2016), who summarize five studies in developing countries, none of which overlap with those in the first set. They arrive at mean return to height of about 1.5 percent per year. The third set of studies are summarized in Horton and Steckel (2011). Their Table 1 reports estimates from studies for 8 advanced economies not covered in the previous two sets, with a mean return to height of 0.5 percent per centimeter. Their Table 2 reports studies for developing countries. The three studies not included in the previous sets of 41
local market during the crisis. Finally, De Haas and van Lelyveld (2006) examine how foreign and domestic banks in ten Central and Eastern Europe (CEE) reacted to business cycle conditions and host country banking crises from 1993 to 2000.<sup>7</sup> Their results show that while during crises domestic banks contracted their credit, foreign banks maintained their credit supply.

Overall, the evidence available so far suggests that foreign banks can have a stabilizing influence on credit markets in developing countries, at least where financial sector depth is concerned. Next we turn to the impact of foreign bank participation on financial sector breadth or reach.

# **_(iii) Access to credit_**

The extent to which foreign banks contribute to greater access to credit, in particular for small firms, in developing countries is perhaps the most controversial aspect of the process of foreign bank participation. Those opposing this process ague that foreign banks are likely to “cherry pick” the most profitable and transparent customers, reducing financing to some market segments, like small businesses. On the other hand, those in favor of foreign bank entry point to the fact that foreign banks have access to a larger pool of loanable funds that can help them sustain higher levels of lending. Also, proponents of foreign bank participation argue that even if foreign banks focus on the most transparent firms, this process can enhance access to credit by smaller firms by forcing domestic banks to move down the market. The existing empirical evidence on the impact of foreign bank participation on access to credit is to date mixed.

Studies such as Berger, _et al._ (2001), Detragiache, Gupta, and Tressel (2005), and Mian (2006) present evidence that suggests that foreign banks limit access and serve only the largest and most transparent firms in an economy. Using data for 61,295 firms with 195,695 loans from 115 different banks in Argentina as of the end of 1998, Berger _et al._ (2001) find that smaller and more opaque firms are less likely to obtain loans from large or foreign-owned banks. Detragiache, Gupta and Tressel (2005) develop a model that shows that when foreign banks have a more efficient technology to monitor high-end customers than domestic banks, foreign bank participation may lead to lower aggregate credit, higher operating costs, and lower welfare. Using data for 89 low income and lower middle income countries, they find that a larger foreign

> 7 Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic and Slovenia

13
# Figure 4: Korea’s Human Capital Growth

<!-- Start of picture text --> 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Year<br>HC Trend HC Actual<br>HC Growth Trend HC Growth Actual<br>.03<br>3.5<br>.025<br>3<br>.02<br>2.5<br>.015<br>Human Capital Level 2<br>Growth Rate of Human Capital<br>.01<br>1.5<br><!-- End of picture text --> 2.7% in 1960 to 0.9% in 2014, as is shown in Figure 4. Figure 5 shows that the labor-augmenting technology level (what we would call ”productivity” which has a one-to-one relationship with the TFP) also almost monotonically increased by 2.8 times, implying the annual average growth rate of 1.91%. Unlike the human capital growth, the path of the productivity growth rate does not show much salient trends. It is just mildly hump-shaped.

There are two labor market demographic factors to the GDP per capita growth other than the GDP per worker growth, i.e., the changes of working-age population share and those of labor force participation rate, which are displayed in Figures 6 and 7.

11
households based on household livelihoods. In addition, we also examine the heterogeneity in the effect of shocks across households based on the intensity of using coping strategies, help, and loans received to pin down the effectiveness of these responses in cushioning the impact of shocks. The objective of this exercise is to draw insights on the vulnerability and resilience of Sudanese households by identifying vulnerable households from the regions and livelihoods most affected by shocks and the extent to which the use of coping strategies enhances resilience by mitigating the impact of shocks.

Table 4: Estimating the Heterogeneity of the Impact of Shocks on Household Consumption Per Capita

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Variables**|**Region**|**Household**<br>**Livelihood**|**Use of Coping**<br>**Strategy**|**Borrowed**|**Received**<br>**Help**|
|Household affected by shock = 1|0.0207<br>(0.0158)|0.0252*<br>(0.0150)|−0.0717***<br>(0.0246)|−0.0258**<br>(0.0131)|−0.00738<br>(0.0118)|
|**Interaction of shocks and**||||||
|**Region**<sup>**a**</sup>||||||
|Darfur|−0.0369*<br>(0.0206)|||||
|Eastern|0.0272<br>(0.0227)|||||
|Khartoum|−0.0975***<br>(0.0282)|||||
|Kordofan|−0.00332<br>(0.0208)|||||
|Northern|−0.0480*|||||
||(0.0250)|||||
|**Interaction of shocks and**||||||
|**household livelihood**<sup>**b**</sup>||||||
|Animal husbandry||−0.00418<br>(0.0448)||||
|Wages and salaries||−0.0423**<br>(0.0171)||||
|Owned business enterprise||−0.0698***<br>(0.0212)||||
|Property income||0.0950**||||
|||(0.0441)||||
|Remittances||−0.0374||||
|||(0.0394)||||
|Pension||<br>−0.0461||||
|||(0.0762)||||
|Others||−0.0649**||||
|||(0.0327)||||
|**Interaction of shocks and**||||||
|**household coping strategy**<sup>**c**</sup>||||||
|Middle|||0.119***|||
||||(0.0351)|||
|High|||<br>0.194**|||
||||(0.0825)|||
|Highest|||<br>0.196***|||
||||(0.0356)|||
|**Any shock (=1) x borrowed**<br>**money (=1)**||||0.0377***||

33
the parasitic ones and thereby perpetuate the misallocation of resources.

_Third_ , the severity of the impact of the COVID-19 shock and the recovery speed are associated with the degree of informality. Moreover, firms’ external attributes such as size, sector, age, and gender of the owner do not explain the depth of the impact. Characteristics of informal firms that are intrinsic, such as poor management capabilities and education of the manager and owners are strong predictors of poor outcomes among informal firms.

_Fourth_ , results, including split regressions, fully interacted models, Shapley decomposition, and Oaxaca-Blinder decomposition, suggest that informality by itself is a critical factor in explaining the impact of the shock on firms that goes beyond the firms’ choice of sector, their size and age. Informal firms driven by necessity witness a larger drop in sales during the shock period compared to the parasitic type of informal firms.

_Fifth_ , the adjustment response of informal firms to crisis is lower even after accounting for their lower initial capabilities. In particular, the adoption of digital technology that was a key adjustment response to lockdown restrictions and partially helped firms mitigate the negative demand shock of the crisis was lower in informal firms and particularly the parasite firms. Encouragingly, female-led informal firms were more likely to start the use of digital technologies and sell online but less likely to invest in such technologies.

_Sixth_ , Access to public support by informal firms is significantly lower. We also suspect mistargeting in support, given that the probability of accessing support is uncorrelated with initial productivity and positively associated with the level of change in sales. Moreover, the instruments used do not address the underlying factors that make informal firms vulnerable, nor do they use the crisis as an opportunity to provide incentives for firms to formalize.

Although a large informal sector is a distinctive characteristic of many developing countries (Perry and Maloney, 2007; La Porta and Shleifer, 2014; Ulyssea, 2020), countries in South Asia exhibit even higher rates of informality (Bussolo et al., 2020) such that more than 90% of the businesses in South Asia are informal. Thus, South Asia provides a unique setting for this type of analysis. In addition, South Asia was also among the regions that were disproportionately affected by the COVID-19 shock (Brucal et al., 2021). The combination of these two factors reinforces the importance of analyzing the role of informality in explaining firm-level outcomes.

Our study makes four main contributions to the literature. _First_ , our analysis takes advantage of several rounds of novel firm-level data specifically collected to analyze the 3
22 low‐income housing less affordable even as it represents a net wealth gain for the native population that owns housing.

Since many advanced countries, most notably in Europe, experience high and persistent unemployment over many years, even when growth is near or above long‐term potential, the worry that increased immigration will simply make the structural or cyclical unemployment problem worse resonates widely. Brucker and Jahn (2011) consider an economy where collective bargaining predominates in some “rigid” sectors and where the labor market is very flexible in others. They find that migration can increase unemployment in the rigid sector, while having a relatively small negative effect on wages in the rigid sector, and reduces the wage in the flexible sector where unemployment remains low by definition.

In the long run, Brucker and Jahn find, these effects tend to dissipate as investment responds. Thus, the model that assumes perfect labor markets tends to overestimate the impact of migration on average wages and to underestimate the effect on unemployment. Brücker (2011) conducted a meta‐analysis of studies examining the effect of immigration on unemployment in the U.S. and Europe and found that in general, an increase in immigration by 1 percent of the population leads to an increase in unemployment of no more than 0.3 percent.

In contrast, studies of the U.S. labor market, which is among the most flexible, have found no significant effect of immigration on employment opportunities for native workers (Peri 2012), including low‐skilled native workers, between 1990 and 2007. Peri and Sparber (2008) report that migrants induce task specialization of natives. They found that, among less‐educated workers, those born in the United States tend to have jobs in manufacturing or mining, while migrants tend to have jobs in personal services and agriculture, providing an explanation for why low‐skilled migration has a limited impact on employment.

In fact, the share of migrants among the less‐educated is strongly correlated with the extent of U.S.‐born worker specialization in communication tasks. In states with a heavy concentration of less‐educated migrants, U.S.‐born workers have shifted toward more communication‐intensive occupations. Those jobs pay higher wages than manual jobs, and so such a mechanism has stimulated the productivity of workers born in the United States and generated new employment opportunities.

The main point that emerges from this brief review is that return policy cannot abstract itself from immigration policy more generally or even from labor market policy. Countries that have more flexible labor markets, for example, are better able to absorb refugees and will be less inclined to press for their return. We now consider the effects of refugees on the public purse, an issue that has been the object of extensive political debate.

_<u>The Fiscal Impact of Refugee Flows</u>_
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# **1. Introduction**

A 2012 report by the World Bank on teenage pregnancy stresses the correlation between teenage childbearing and socioeconomic variables including poverty, inequality, public health expenditure and female labor force participation. The report shows that, despite substantial reductions in teenage pregnancy rates in virtually all countries, we continue to see rates differ vastly between high- and low-income countries, with Brazil—as a middle-income country—being placed somewhere in the middle of the distribution (World Bank 2012).

Improved access to education, not explored in detail in the World Bank report, potentially provides an important channel through which teenage pregnancy and the above socioeconomic variables are correlated. Observational evidence shows a strong negative relationship between school availability and teenage childbearing in Brazil. We plot this relationship over time for Brazil in Figure 1. Between 1997 and 2009,<sup>1</sup> 9,402 secondary schools where introduced in Brazilian municipalities (a 57% increase), raising the average school density in municipalities from 1.06 per 100 teenagers to 1.54 per 100 teenagers. Over the same period, the birth rate for teenage girls<sup>2</sup> decreased by 21% from 8.1 births per 100 to 6.4 births per 100. This suggests an additional secondary school per 100 teenage females is associated with a decrease in births of 3.33 births per 100 teenage females. Evidence based on cross-sectional data shows a very similar picture. In Figure 2, we present the state-level relationship between school density and the rate of teenage childbearing. Based on this clear negative relationship we calculate that an additional secondary school per 100 teenage females is associated with a larger decrease in births of 17.9 births per 100 teenage females.<sup>3</sup>

> 1 Section 3 provides the reasoning for the chosen period of 1997-2009 of this study.

> 2 In this paper we define ‘teenage’ as ages 15–19 to match with the target entry age into secondary school.

> 3 This estimate is based on a weighted linear fit, where the weights are the population of females aged 15-19 across Brazilian states.

2