rafmacalaba/datause-encoder-data huggingface dataset:
List of **Appndix Tables** - 2

**Table** A.13: Rural Consumption Parameters Across States With Dlffering Agricultural Incomes, India 1983 20 Table A.19: Semi-input-Output Parameters for India, 1979/80 21

# APPENDIX **B**

The authors are grateful to Ataman Aksoy, Pamela Cox, Kelth Obiltas, Frank Place, Toja Raparlu, and Roger Slade for helpful comments on earlier drafts.
41 where *(x) is the amount of land devoted<sup>to roads at x. As emissions</sup> are predicted as a function of vehicle speeds and volume using EPA<sup>emissions</sup> factors (now out of date), and traffic and congestion are highest in the city center, amenities are lowest there. The cost of commuting<sup>is inversely</sup> related to speed and may be adjusted<sup>by changes in gasoline prices and vehicle</sup> purchase price, etc. Complicated expressions are derived for the compensating variation welfare measure with respect to changes<sup>in pollution</sup> and congestion. The model is used for three types of cities,<sup>differing</sup> by the parameters assigned to amenities and vertical dispersion. Marginal WTP functions are assumed linear in emissions. Positively sloped rent gradients near the citycenter arise if congestion externalities are sufficiently small relative to environmental externalities. The former tends to increase the demand<sup>for</sup> housing close to the city, pushing up rents<sup>(the traditional</sup> solution); the latter, owing to the greater density of<sup>vehicles as one moves into the center</sup> of the city, tends to increase the demand<sup>for housing in outlying areas,</sup> depressing demands in the center city. The model<sup>is also used to examine the</sup> welfare effects of congestion tolls (optimal and equal per mile) and alternative mandated changes in engine designs (differing in operating costs and emissions) for two different city types.

This type of model, and this model in particular, can be used to estimate welfare losses associated with alternative types of policies for drastically simplified representations of cities. The simplicity of this model, its inclusion of congestion and emissions as well as their feedback (the latter through health effects) on location choice), and its consistent framework for estimating welfare losses are notable advantages. Because<sup>it explicitly</sup> considers space, albeit in the simplist way, it can capture the effects on welfare of congestion tolls, parking charges, changes in highway capacity, and
assessment summarizes national regulatory frameworks and procurement practices at the beginning of June 2017. Most BRI countries would benefit from improved PPP regulatory frameworks. Project preparation and implementation are the areas that have the most room for improvement across both groups. In these areas, LIDCs are below 50 percent of internationally recognized best practice, while EMs have higher average scores across all PPP project cycle stages. There are also significant variations within the country groups, with countries with highest vulnerability levels faring worse across all dimensions of the assessment (Figure 20).

<!-- Start of picture text --> Figure 20. Quality of PPP Regulatory Framework<br>(Median level of groups of countries with the same level of debt vulnerability)<br>LIDCs EMs<br>80 100<br>64 64<br>60 80 68 73<br>45 49 46 48 60 58<br>60 52 50<br>40 35 33<br>24 40<br>20<br>20<br>0 0<br>High Medium Low High scrutiny Low<br>Preparation stage Procurement stage Preparation stage Procurement stage<br>Implementation stage MIN Implementation stage MIN<br>MAX<br>Strength of Regulatory Framework Strength of Regulatory Framework<br><!-- End of picture text --> Source: The World Bank Procuring Infrastructure PPPs 2018 scores; staff estimates for LICs/MACs Many important elements remain unknown and require a country-specific approach. Pre-existing debt vulnerabilities and recipient countries’ exposure to shocks need to be carefully accounted for in designing financing strategies. Important elements of the analysis also include the countries’ available type of financing, the level of public investment efficiency, the absorptive capacity, revenue mobilization and access to international and domestic borrowing.

For this reason, the paper outlines also the macroeconomic implications that would guide the assessment of the impact of large BRI project on debt sustainability and fiscal risks. Debt sustainability is a major anchor of fiscal policy, underpinning the development strategy of any economy. Fiscal risks would identify the source, type and likelihood of higher fiscal costs or lower revenue, related to BRI investment, which would require additional financing and could further complicate debt sustainability and debt management.

# VII. Conclusions

This paper finds that 28 percent of BRI investment recipients (12 out of 43 countries considered) are likely to experience increased debt vulnerability as a result of BRI in the medium term. These includes 7 LIDCs and 5 EMs. Long-term debt sustainability projections find that 37 percent of countries (11, out of 30 countries, of which 5 LIDCs and 6 EMs) are likely to experience an increase in their debt-to GDP ratio as a result of BRI investment and financing, and 8 among them would be vulnerable to changes in the cost of financing. Most of the countries that have been identified through the medium-term metric and the long33
# **<u>Table C.4 First-stage regressions, Mauritania</u>**

||**2004-**|**2008**|**2008-**|**2014**|**2014-**|**2019**|
|---|---|---|---|---|---|---|
||**2004**|**2008**|**2008**|**2014**|**2014**|**2019**|
|Head`s age|-0.007***|-0.003***|-0.006***|-0.004***|-0.007***|-0.004***|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Head is female|0.022|0.075***|0.065***|0.128***|0.103***|0.130***|
||(0.02)|(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|
|Highest education level is primary|0.187***|0.143***|0.146***|0.073***|0.079***|-0.014|
||(0.03)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Highest education level is secondary|0.384***|0.383***|0.382***|0.223***|0.245***|0.145***|
||(0.03)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Highest education level is tertiary|0.708***|0.609***|0.621***|0.382***|0.408***|0.382***|
||(0.04)|(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|
|Urban|0.342***|0.605***|0.596***|0.353***|0.380***|0.368***|
||(0.02)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|
|_cons|11.532***|11.710***|11.819***|12.279***|12.356***|12.421***|
||(0.05)|(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|
|adjusted R2|0.18|0.32|0.32|0.16|0.18|0.18|
|N|6065|9269|9088|6672|6219|6425|

**Note:** ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. Regression is estimated using Weighted Ordinary Least Squares. Household heads’ ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second round. The reference groups are household with no primary education and living in rural areas.

82
regularly to receive their payments.<sup>28</sup> As a consequence, we chose to avoid direct monitoring of attendance in order to retain as sharp a _differential_ test as possible of the relative merits of conditional and unconditional transfers.

As important as school attendance may be for adolescent girls, perhaps as important is learning achievement and cognitive skills.<sup>29</sup> To measure these, we conducted independently developed tests of mathematics, English reading comprehension, and cognitive ability. Total number of correct answers in each of these tests is standardized to have a mean equal to ‘zero’ and standard deviation equal to ‘one’ in the control group and program impacts are presented as changes in standard deviations (SD).

_Marriage and Fertility_ Teenage pregnancy in Malawi is common with the adolescent fertility rate at 133 per 1,000 women aged 15-19.<sup>30</sup> Many girls cite pregnancy as the main reason for dropping out of school and getting married at an early age. Each of the core respondents was asked the following questions in each round: “Have you ever been pregnant or are you currently pregnant?” and “What is your marital status?” We use the answers to these questions to calculate the incidence of marriage and pregnancy in Rounds 2 and 3.

> 28 This is nicely illustrated during an in-depth interview with one of the core respondents (respondent ID 1332203): After describing the CCT girls being followed to their schools to monitor their attendance, which she explained showed her that the program was interested in ‘attracting girls to go to school,’ she was asked by the interviewer in what way the program managers cared (about schooling). She answered by saying: “They cared for the conditional group only but on the other group they didn’t care.”

> 29 Other schooling outcomes, such as repetition and reentry rates are also important and can lead to different inferences regarding program impacts on schooling attainment. See, for example, Behrman, Sengupta, and Todd (2005).

> 30 For comparison, the same figure is 35 in the U.S.A. and 64 in Mexico (World Development Indicators 2010).

16
|**Table 13: G**|**inasio Carioca**<br>(1)|**& IDEB Middle**<br>(2)|**Schools**<br>(3)|(4)|
|---|---|---|---|---|
||<br>IDEB Mid<br>(SD)|<br>IDEB Mid<br>(SD)|<br>IDEB Mid<br>(SD)|<br>IDEB Mid<br>(SD)|
|Carioca Middle School * Post|1.454<br>[0.125]***|0.806<br>[0.110]***|0.410<br>[0.130]***|0.958<br>[0.149]***|
|Single-Shift * Post|0.153|0.011|-0.300|-0.318|
||[0.157]|[0.133]|[0.109]***|[0.244]|
|School of Tomorrow|0.138|0.026|-0.505|0.092|
||[0.088]|[0.090]|[0.109]***|[0.126]|
|More Education|0.101|0.09|0.262|0.1|
||[0.053]*|[0.053]*|[0.090]***|[0.074]|
|Mean items in the house||0.184<br>[0.033]***|0.296<br>[0.058]***|0.104<br>[0.050]**|
|% Mothers w/ HE||0.087|-1.199|-0.106|
|||[0.243]|[0.438]***|[0.379]|
|Participation rate-Prova Brasil Mid||0.009|-0.037|-0.012|
|||[0.034]|[0.062]|[0.051]|
|Age-grade distortion||-0.023|-0.018|-0.023|
|||[0.003]***|[0.004]***|[0.004]***|
|Constant|-0.543|-0.412|-1.482|-0.063|
||[0.032]***|[0.293]|[0.531]***|[0.469]|
|Observations|1956|1635|164|820|
|Number of schools|326|327|41|164|
|R-squared|0.46|0.49|0.79|0.43|
|<br>School FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Restricted Sample<br>Robust standard errors in brackets|No|No|Yes|Yes|

<mark>* significant at 10%; ** significant at 5%; *** significant at 1%</mark> Notas: IDEB Middle Schools 2005, 2007, 2009, 2011, 2013 and 2015. Columns (2) to (4) refer to years 2007 through 2015. Column (3) refers to only schools that eventually become Single-Shift until 2015, in years 2007 through 2013. Column (4) refers to only schools with low age-grade distortion in 2010 (prior to the program). Clustered standard error in school level.

33
their assets to small businesses than other banks (See Goldberg and White (1998), DeYoung _et al._ (1999), and Jenkins (2000)).

Foreign entry through merger and acquisitions (M&As) might have different effects on small business lending. Some studies find that M&As among small banks led to increased propensity to lend to small businesses following their consolidation (Peek and Rosengren (1998), Walraven, (1997), Strahan and Weston (1998), Berger _et al._ (1998)). However, when medium-sized and large banks are involved, a number of recent papers have reached conflicting conclusions. Keeton (1996) and Berger _et al._ (1998) uncovered that M&As tend to result in a reduction in small business lending when large banks are involved, but Strahan and Weston (1998) found no significant change in the ratio of small business loans to assets following large M&As.

There is, however, very little literature that deals directly with the implications of foreign entry for lending to small businesses in developing countries. Argentina is the only country for which we found such studies. Bleger and Rozenwurcel (2000) indicate that foreign bank participation in Argentina is associated with a reduction of bank lending to small businesses from around 20 to 16 percent of total lending between 1996 and 1998. In contrast, Escude et al. (2001) find that despite their lower tendency to lend to small businesses, foreign banks have increased both their propensity and their market share of lending to the sector between 1998 and 2000. Finally, using a rich data set on Argentinean business debtors in December 1998, Berger, Klapper, and Udell (2000) find that large banks and foreign owned banks are less inclined to extend credit to smaller firms, which are likely to be informationally opaque.

Given the paucity of research on the impact of foreign bank entry on access to credit and the importance of this issue from a policy standpoint, further investigation is clearly warranted. Using bank level data for Argentina, Chile, Colombia, and Peru during the mid-1990s, this study empirically investigates whether bank origin affects the share and growth rate of bank lending to small businesses. After Eastem Europe, Latin America has been the region that most rapidly and widely allowed foreign bank entry. In our study, we focus on four of the most important countries in the region that have witnessed the largest increase in foreign entry into their banking sectors. Having bank-level time series 3
<!-- Start of picture text --> Regional Trade of Selected Selected Regions<br>50.3<br>35.9%<br>30.%<br>25.30<br>20.%<br>15.%10.%10.%<br>59%<br>fm ~ an ms<br>5s 56 6s 6865s 8 &B S288 85 8 42 8<br>aoofo&#ofo&# FR &A A A FR A A A A RR A A<br>meee Seo) meme CEM AC AC ee FAC ee ECOWAS «© eee WAEML<br><!-- End of picture text --> <!-- Start of picture text --> ooo World Export Shares of Selected Export Shares of Selected Shares of Selected of Selected Selected Regions (2002-2016) (2002-2016)<br>8.00%<br>800%<br>5.00%<br>4.00%<br>3.00%<br>200%;; ee<br>1.00% ee eww ww ww 8 ow www en<br>eeso8so88 2 ge 8 8 eats —————2s2s a g<br>8 8 8 & 8 & RRR RR RR RA<br>ma ASEAN me CEMAC ee EAC ee ECOWAS mee VIERCOSUR me WAEMU WAEMU = == 3 regions regions<br><!-- End of picture text --> <!-- Start of picture text --> ooo World Export Shares of Selected Export Shares of Selected Shares of Selected of Selected Selected Regions (2002-2016) (2002-2016) Regional Trade of Selected Selected Regions<br>8.00% 50.3<br>800% 35.9%<br>5.00% 30.%<br>4.00% 25.30<br>3.00% 20.%<br>200%;; ee 15.%10.%10.%<br>1.00% ee eww ww ww 8 ow www en 59%<br>eeso8so88 2 ge 8 8 eats —————2s2s a g fm ~ an ms<br>8 8 8 & 8 & RRR RR RR RA 5s 56 6s 6865s 8 &B S288 85 8 42 8<br>aoofo&#ofo&# FR &A A A FR A A A A RR A A<br>ma ASEAN me CEMAC ee EAC ee ECOWAS mee VIERCOSUR me WAEMU WAEMU = == 3 regions regions meee Seo) meme CEM AC AC ee FAC ee ECOWAS «© eee WAEML<br>és. 4550 i : Oil exporters<br>2 40 I = Non-oil resource-intensive counties<br>Ez 35 I e @ Rest of Sub-Saharan Affica<br>3 8 30 I © ‘“ TM Comparator countries<br>CS 25 i . e © 1991-95 average t<br>ees 20 I q e é ee<br>S38 15 I 1 P J<br>‘GeS 40 1 aan | : ec<br>no eeeeewoo Qe2RgxessgegasoeoSS SESE SRE GS SESS° SBSaARSLSESCASSSESSSOSLSHYESSEVNLGRDSS ESSE ESERIES SSSRERESESZ ESBS?<br>ReEesges-sezxs&SSSS5 2E8< = Su SS 7TMS.65 FOS2ZLO2 HES CHEE<br>sc 33S gugz=%as Op SES 838 P§°S g 85s g°> Pses<br>DNs“© HK °o Ss& a=N 8 ==Es as = ero2.3 a2 baoS Presetas<br>5 Fs 2 = 388<br>ea32 8 8 SEs32<br>Sources: Eora database; and IMF staff calculations. o5<br>Note: See the list of country groups in Annex 3.2. =<br>1 Excluding sub-Saharan African countries. -<br><!-- End of picture text -->
4 upward social and occupational mobility upon return, potentially translating into an important tool of economic development. Only recently, the importance of Albanian returnees and their potential contribution to growth at home have been recognized, and a number of civil society programs, such as MJAFT!, AlbStudent, and New Albanian Generation, have been initiated to encourage the return of high skilled Albanian Diaspora (Germenji and Gedeshi, 2008). However, unleashing the full potential of return migrants rests on a number of interventions that are part of a comprehensive package that not only creates sufficient incentives to encourage return but also recognizes the heterogeneity of returnees in terms of skill levels and occupations, and emphasizes the provision of opportunities in the domestic labor market as to match the needs and capabilities of return migrants. Unfortunately, these interventions remain vastly overlooked both in Albanian policymaking and research.

This study uses the 2005 Albanian Living Standards Measurement Study Survey (ALSMS05) and estimates the impact of international migration experience on occupational mobility of return migrants _vis a vis_ working-age Albanian residents that never migrated. Since such impact is likely to depend on earning potentials and opportunities for skill acquisition in different destination countries, we explore the heterogeneity of impact by host countries, namely Greece _vis a vis_ Italy and beyond (ITB)<sup>2</sup> . The topic of interest has never been explored in the context of Albania and has been given fairly limited attention in other transition economies. Our study is also a more recent contribution to the scant literature that has attempted to analyze labor market returns to past migration experience by comparing return migrants with non-migrants.

One unique aspect of the ALSMS05 was its unusually rich module on migration, which collected detailed information on international migration histories of all current household members, as well as information concerning household migration networks abroad. The ALSMS05 also collected data on _initial_<sup>3</sup> (prior to first migration episode for return migrants) and _present_ occupations of return migrants and non-migrants. The individual level information on international migration experience and employment outcomes over time, and a methodology for ranking occupational groups with respect to their human capital requirements, allow us to test the

> 2 While an increasing number of Albanians are migrating to further destinations, such as Germany, the United Kingdom, the United States and Canada, migrant flows to these destinations are still quite small. Hence, we classify the migrants to Italy and countries other than Greece under the “Italy and beyond” category.

> 3 Initial employment status is measured at January 1, 1990 for individuals that were 15 years of age or older at that date. For individuals below 15 years of age on January 1, 1990, initial employment status pertains to the date that they turned 15.
developing countries are making progress in improving sanitation, over one billion people still defecate in the open, without using a toilet or latrine (WHO and UNICEF, 2012). Increasingly, open defecation is concentrated in rural areas, but it is also becoming increasingly concentrated in countries with high rural population densities, such as Indonesia, Pakistan, and especially India, where the 2011 census finds that 90 percent of households without a toilet or latrine live in rural areas.

Open defecation is a practice with strong negative health externalities: it spreads infectious diseases such as diarrhea, polio, cholera and parasites. Greater population density could exacerbate these negative externalities by providing more opportunities for disease transmission. Although there are several examples of population density-health interactions in present-day developing countries in the literature,<sup>2</sup> and there is some evidence from present-day developed countries,<sup>3</sup> discussion of the evidence that population density can intensify an epidemiological externality often begins with the history of urbanization in now-rich countries.

Much has been written about the lethal combination of population density and poor sanitation in 19th century London. To illustrate an exemplary use of observational statistics, Freedman (1991) recounts John Snow’s investigation of the 1853-54 cholera epidemic. By tracing deaths to the supply of their households’ water, he demonstrated the nature of the epidemic, and is widely credited for establishing the infectious mechanism of the disease.

2For example, Root (1997) finds that population density is correlated with child mortality across provinces of Zimbabwe. A study of typhoid in Dhaka showed that crowdednesss has a considerable impact on the transmission and distribution of the disease: areas with low risk of typhoid were those with the lowest population density and those with the highest risk had the highest population density (Corner et al., 2013). Ali et al. (2002) show that higher population density is associated with a greater risk of cholera in a rural part of Bangladesh. Grassly et al. (2006) describe challenges to polio eradication in densely populated Uttar Pradesh and Bihar: “high population density and poor sanitation can lead to more frequent infectious contacts and increase levels of excreted polio-virus in the environment.”

> 3An observational study in rural Wisconsin in the U.S. found that a higher density of septic tanks was associated with an increased prevalence of diarrhea (Borchardt et al., 1979). Studies of the Tama River in Tokyo and the Cumberland River in Nashville showed that fecal bacteria concentrations, possibly originating from sewer overflows, were significantly affected by population density (Ham et al., 2009) and (Young and Thackston, 1999). An aggregated (or “ecological”) study across three developed countries also found suggestive evidence that higher population density may be related to increased antibiotic resistance, because higher interpersonal contact can lead to the spread of resistant bacteria (Bruinsma et al., 2003).

6
period of more protection, and it is difficult to explain why the short run elasticities would get larger in 1990-92 and then revert to their previous values thereafter as Chilean industry faces a more competitive environment. The Wald tests serve as a measure of the break in the long run elasticity and show break only in 1986 and 1989 and do not suggest a consistent movement in line with Rodrik's theory.

White collar workers do show a provocative consistent structural break in the long run elasticity from 1987-1989, a period of arguably greater protection. Yet all three LR elasticities become greater in absolute value rather than lower. The variable Wald tests suggests that there are significant time effects in the own wage.

# _Colombia_

In table 3, the specification Wald tests for Colombia also show no consistent pattern of structural break although sporadic breaks occur in 1984, 1985 and 1988. In the first case, none of the dummies on the components of the LR own elasticity are significant, and in the second, the short run elasticity become larger in a period of greater protection. Individual dummies on the contemporaneous own wage are significant in 1988-1989, but there is no obvious correlation with policy change. Though the long run own wage elasticity does appear larger in 1991, a period of increased liberalization, the Wald test does not suggest significance.

Table 4 shows that neither the specification Wald tests or the coefficient Wald tests suggest any break in the white collar specification and no individual dummies are significant.

19