rafmacalaba/datause-encoder-data huggingface dataset:
- 22 -

Table 5 suggests that most of the 1963 labor intensive product sectors were also relatively labor intensive in 1982. This implies that most items which were identified as suitable for developing country exports in the 1960s would also be selected in the 1980s. However, there are some lower level four-digit SIC products that shifted from relatively labor intensive to capital intensive production techniques and would now be deleted from the original 1965 NBER product list (see Appendix Table 1).

Given the key role that textiles and clothing played in the initial export expansion of some developing countries, Table S examines changes in employment and factor intensity ratios for these products in more detail. The data show that considerable labor shedding occurred in these sectors since approximately 230,000 jobs were lost over 1963-1982 (146,000 in textiles), and that the labor intensity of both sectors has changed relatively little. On average, the labor intensity of apparel and clothing (SIC 23) is approximately 50 per cent higher than for all United States industry, while textile mill products (SIC 22) appear to be only slightly less labor intensive. The im, .cation is that the very strong comparative advantage developing countries have in the production of textiles and clothing has changed little and that these countries will continue to exert strong pressures on developed country suppliers in the 1990s.

Table 6 addresses the question of where labor intensive products are concentrated (i.e., in which industry sectors do they generally originate) in the 1980s. The table tabulates the number of four-digit SIC labor intensive products in each industry sector and also indicates the extent that these
some of the mortgages for themselves, and on these mortgages they bore the risks, but overall this is a market failure problem as the capital was not allocated efficiently.<sup>26</sup>

# **Moral Hazard and the Credit Rating Organizations**

The pools of mortgages were rated regarding their riskiness by Moody’s, Standard and Poor’s or Fitch. These firms enjoyed a preferred designation of the Securities and Exchange Commission (SEC) as a “nationally recognized statistical rating organization” (NRSRO), better known as Credit Rating Organizations (CROs). Unfortunately, the CROs, who have a responsibility to objectively evaluate the risks of the instruments they assess, had a conflict of interest. They had an incentive to undervalue the risk. These rating agencies became heavily dependent on the fees from rating mortgage pools. Securitizers were the ones who paid the ratings agencies, and repeat business for a CRO was dependent on good ratings. Moreover, secritizers routinely employed the practice known as “rating shopping.” A securitizer would ask a rating agency how the agency might hypothetically rate a pool of mortgages. If the rating were low, the securitizer would go to another rating agency. In what they call “the alchemy of the CDO credit ratings,” Benmelach and Dlugosz (2009a) have shown that the CDOs received credit ratings that were strikingly higher than the credit quality of the underlying collateral pools, and Benmelach and Dlugosz (2009b) show that severe downgrading of these securities started in 2007 leading to financial institution write downs of more than $200 billion in early 2009. Caprio _et al_ . (2008) argue that a critical failure in the system was the fact that the securitizers were the ones who paid the raters. That is, the CROs had a moral hazard problem as the fees too strongly influenced their evaluations. The result was a gross undervaluation of the riskiness of these pools of mortgages and buyers purchasing very risky assets they assumed had low risk. This led to a market failure of buyers purchasing more of these risky securities than was efficient.

The poor performance of the CROs highlights long standing regulatory distortions of the industry that artificially restricted their supply and increased demand for their services. Supply was restricted by the SEC definition of the category of NRSRO in 1975 and subsequent SEC restraint on which firms could be so designated (there were never more than five and there were

> 26 The Lehman Brothers bankruptcy revealed that Lehman keep some of the riskiest tranches of the mortgage pools on its own books. It is not clear if Lehman kept these tranches in order to secure AAA ratings of the remaining mortgage pool or if it was willing to bear increased risk to obtain a higher return.
# **_2.2  The production response_**

The increasing demand for food will have substantial impacts globally. The results of integrated scenario work (Rosegrant et al., 2009) indicate that the prices of meats, milk and cereals are likely to increase in the coming decades, dramatically reversing past trends. These price increases will be driven by both demand and supply factors. Rapid growth in meat and milk demand is projected to put pressure on prices for maize and other coarse grains and meals. Bioenergy demand is projected to compete for land and water resources. Growing scarcities of water and land are projected to increasingly constrain food production growth, causing adverse impacts on food security and human wellbeing goals. Higher prices can benefit surplus agricultural producers, but can reduce access to food by a larger number of poor consumers, including farmers who do not produce a net surplus for the market. As a result, progress in reducing malnutrition is projected to be slow (Rosegrant et al., 2009).

This scenario work involved simulations using the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT; Rosegrant et al., 2002), a partial equilibrium agricultural sector models that can provide insights in long-term changes in food demand and supply at a regional level, taking into account changes in trade patterns using macroeconomic assumptions as an exogenous input. Food production is based on economic, demographic, and technological change, and the model provides estimates of food and water supply and demand, food prices and trade, and the number of malnourished children. In Rosegrant et al. (2009), IMPACT was used to quantify a "reference run", which imagined a world developing over the next decades as it does today, without anticipating deliberate interventions requiring new or intensified policies in response to the projected developments. Current policy pathways are expected to continue out to 2050. Population growth was based on the medium variant projections of the UN, and economic growth assumptions were loosely based on the TechnoGarden (TG) scenario of the Millennium Ecosystem Assessment (MA, 2005) -- these are near the mid-range growth scenarios in the literature for the world as a whole and most regions. Agricultural productivity assumptions were also based on the TechnoGarden scenario and on FAO interim report projections to 2030/2050 (FAO, 2006). Growth in nonagricultural sectors was projected to be lower than in the agricultural sector in the reference case. The non-agricultural GDP growth rates are likewise based on the MA TechnoGarden scenario but with adjustments to align with World Bank medium-term projections.

16
1, the sample covers entirely all EU Member States but Poland, for which data were partially unavailable in 20 of 73 regions.<sup>16</sup> _Table 1: Structure of the sample and geographic coverage._

|Country||Regions<br>||
|---|---|---|---|
||Population|Sample|Percentage Covered|
|Austria|35|35|100.00|
|Belgium|44|44|100.00|
|Bulgaria|28|28|100.00|
|Cyprus|1|1|100.00|
|Czech Republic|14|14|100.00|
|Germany<br>|401|401|100.00|
|Denmark|11|11|100.00|
|Estonia|5|5|100.00|
|Greece|52|52|100.00|
|Spain|57|57|100.00|
|Finland|19|19|100.00|
|France|96|96|100.00|
|Croatia|21|21|100.00|
|Hungary|20|20|100.00|
|Ireland|8|8|100.00|
|Italy|110|110|100.00|
|Lithuania|10|10|100.00|
|Luxembourg|1|1|100.00|
|Latvia|6|6|100.00|
|Malta|2|2|100.00|
|Netherlands|40|40|100.00|
|Poland|73|53|72.60|
|Portugal|25|25|100.00|
|Romania|42|42|100.00|
|Sweden|21|21|100.00|
|Slovenia|12|12|100.00|
|Slovak Republic|8|8|100.00|
|United Kingdom|179|179|100.00|
|Total|1,341|1,321|98.51|

Notes: Percentages are defined on row values. The five French Overseas Departments, and the Spanish extra-territorial autonomous cities of Ceuta and Melilla have been excluded from the sample _à priori_ . 20 Polish regions are out of the sample due to data availability issues.

# **2.2. Defining Lagging Regions**

**The NUTS-2 typology of regions provides the basis for our analysis at NUTS-3 level.** Under Cohesion Policy, NUTS-2 level regions are defined according to their GDP per capita level as being in one of three regional types: (i) ‘more developed’, with a GDP per capita over 90% of the EU average; (ii) ‘transition’, with a GDP per capita between 75% and 90% of the EU average; and (iii) ‘less developed’, with a GDP per capita less than 75% of the EU average.<sup>17</sup> Under the European Commission’s ‘Catching Up Initiative’,<sup>18</sup> two further subsets of lagging regions were defined: (i) ‘low-income regions’, with a GDP per capita under 50% of the EU average in 2013; and (ii) ‘low-

> 16 Specifically, the sample does not include the Polish NUTS-3 regions of Nowosądecki, Nowotarski, Oświęcimski, Koszaliński, Szczecinecko-pyrzycki, Szczeciński, <mark>Nyski, Opolski, Grudziądzki, Inowrocławski, Świecki, Włocławski,</mark> Słupski, Chojnicki, Starogardzki, Ciechanowski, Płocki, Ostrołęcki, Siedlecki, and Żyrardowski.

> 17 Definition taken from “https://ec.europa.eu/eurostat/web/regions/background”.

> 18 The initiative was launched in 2015 with the aim of providing more targeted technical assistance to support regional development in a subset of regions facing the greatest challenges in unlocking growth potential.

6
**Table A2.CR.C: Intergenerational Persistence in Schooling in Rural China - -** **<u>(18 35 Years Age Cohorts. First born Sample)</u>**

|||**LINEAR CEF**||**Q**|**UADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft|0.26|0.33|0.069|0.25|0.33|0.08|
||(0.019)***|(0.019)***|(0.027)**|(0.055)***|(0.019)***|(0.078)|
||[0.036]***|[0.046]***|[0.023]***|[0.097]**|[0.046]***|[0.074]|
|Quadratic Coefft.||||0.00031|-0.00071|-0.001|
|||||(0.0044)|(0.0047)|(0.0064)|
|||||[0.0060]|[0.0073]|[0.0067]|
|Intercept|7.72|6.85|-0.86|7.72|6.84|-0.88|
||(0.15)***|(0.15)***|(0.21)***|(0.17)***|(0.17)***|(0.24)***|
||[0.31]***|[0.41]***|[0.27]***|[0.36]***|[0.47]***|[0.29]***|
|No. Observations|2444|2772|5216|2444|2772|5216|
|||**Estima**<br>|**tes of Mobility**|**from Quadrat**|**ic CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|7.72|6.85||**_IGRC_**|0.26|0.33|
|**_ES6 _**|9.28|8.83|||||
|**_ES9_**|10.06|9.82|||||
|**_ES16_**|11.88|12.13|||||

Notes: (1) The data used are CFPS 2016 with first-born children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
# Section 3: Data

Table 1 presents the 18 surveys included in our analysis. This list comprises 12 countries in West Africa including Nigeria, four countries in East Africa, Gabon in Central Africa, and South Africa. These countries account for 59 percent of the population of SubSaharan Africa in 2020 and were identified as those with a consumption survey between 2012-2019 with both region of birth and parental education available. As our outcome, we use per-capita household consumption rather than income given that consumption is a more direct measure of well-being and income measurement in surveys is subject to more error (Carletto et al. 2022). We include the following circumstance variables: birth region, birth urban-rural status, parental education, parental industry of work, religion, and ethnicity (race in the case of South Africa). Birth region and parent education are available in all countries, while other circumstances are available in most but not all countries. Father and mother industry and ethnicity are available in 12 countries, while religion and urban-rural birthplace are available in 15 countries (this is presented in Table A1 in the Appendix). Our study sample contains people aged 15 or over in each country.

**Table 1 : List of Countries and Surveys Included in Analysis**

|**Country**|**Year**|**Survey**|
|---|---|---|
|Benin|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Burkina Faso|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Côte d'Ivoire|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Ethiopia|2018-2019|Ethiopia Socioeconomic Survey<br>|
|Gabon|2017|Enquête Gabonaise pour l'Évaluation de la Pauvreté|
|Gambia, The|2015-2016|Integrated Household Survey|
|Ghana|2016-2017|Ghana Living Standard Survey|
|Guinea-Bissau|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Liberia|2016|Household Income and Expenditure Survey|
|Malawi|2019-2020|Integrated Household Survey|
|Mali|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Niger|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Nigeria|2018-2019|Living Standards Survey|
|Senegal|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|South Africa|2017|National Income Dynamics Study|
|Tanzania|2017-2018|Household Budget Survey|
|Togo|2018-2019|Enquête Harmonisée sur le Conditions de Vie des Ménages|
|Uganda|2012-2013|Uganda National Panel Survey|

We exclude gender from the list of circumstances because the outcome, consumption, is measured at the household level and does not capture within-household resource 9
# **6 Data**

We use a subsample of 209,762 children between 0 to 48 months of age that live in rural areas from the Third Round of the District Level Household Survey (DLHS-3). The DLHS is a nationwide survey with district level representation of India’s households, which collects information on family planning, maternal and child health, reproductive health of ever married women and adolescent girls, and utilization of maternal and child healthcare services.

As described previously, an important feature of this paper is the explicit identification of the external benefit of sanitation, or externality, as measured through the ratio of access to sanitation at the village level. In this paper, we use the ratio of access to sanitation at the primary sampling unit (PSU) level as a proxy for the ratio at the village level. The sample design of the DLHS-3 survey makes this measure possible since in rural areas the DLHS-3 uses census villages as PSU (International Institute for Population Sciences (2010)). This gives us a plausible measure of access to sanitation infrastructure at the village level. However, because PSU’s for urban areas are not clearly isolated (so neighbors are not clearly defined), we restrict the sample to rural areas only. In our sample, the ratio of access to sanitation ranges from 0 to 100 percent. Our main health outcome measure is a binary variable (Equation (12)) that takes value 1 if a mother reports that her child, “ _has had diarrhea in the last two weeks_ ,” and 0 if otherwise.

When we analyze the differences between those who have access to improved sanitation and fixed-point defecation, the differences in diarrhea prevalence emerge. While the limitations of caregiver-reported diarrhea have been noted in the literature (Das et al., 2012), we chose this outcome in order to make the findings more comparable to previous studies on the health of sanitation.

Figure 4 shows the distribution of type of sanitation for this sample. We find that 18 percent of children under 48 months live in households with improved sanitation, 9 percent have unimproved facilities which include shared improved facilities, and 72 percent live in households which lack access to any type of sanitation facility and thus defecate in 12
Aector is domestic money.3 Let the domestic demand for real balances be a Cagan-style function of the expected rate of inflation: (2) h - exp(-ad _)._ a o.

Figure 1 shows the determination of steady state equilibrium. The curve - OM incorporates money market clearing and convergence of expectations (we P), both of which are characteristices of the steady state; it is the inflation-tax Laffer curve' (Dornbusch and Fischer (1985)). Revenue from the inflation tax is maximized at h*, where the elasticity of demand for the monetary base is unity. The DD schedule gives the total amount that must be financed through the inflation tax if growth in real money demand is zero (another requirement of the steady state). The diagram gives a quick check of sustainability of fiscal requirements; if DD lies above OM, the deficit cannot be financed in the steady state and must be financed in the short run through reserve decumulation, foreign borrowing, or increased aid. If financing requirements are sustainable, there will be two steady state equilibria. The stability properties of these equilibria depend on how expectations about inflation are formed; Figure 2 shows the dynamics under both rational and adaptive expectations. In the rational expectations case, the high inflation, low real balances equilibrium is stable and the other unstable. In the adaptive expectations case, we use the following dynamics fcr expected inflationt 8 Pinto (1985, 1987) studies a similar models in which domestic residents also hold foreign currency obtained on the black market.

C
**List Of Tables**

|Table1.Household characteristicscommontotheCensusandtheVLSS..........................6|
|---|
|Table2.Determinantsofpercapitaexpenditureforrural andurbanareas........................14|
|Table3.Testsofsignificanceofgroupsofexplanatoryvariables inurban-rural...............16|
|Table4.ComparisonoforiginalandCensus-basedpovertyheadcounts...........................20|
|Table5.Differencesinregionalpovertyheadcountsandtheirstatisticalsignificance.....21|
|Table6.Provincialpoverty headcountsestimatedwithurban-ruralregressionmodel......25|
|Table7.Accuracyofdifferentvariablesintargetingpoorhouseholds ..............................34|

# **List Of Figures**

|Figure1.Incidenceofpoverty byprovince.............................................................<br>23|
|---|
|Figure2.Incidenceofruralpovertybyprovince ..............................................................26|
|Figure3.ProvincialPovertyHeadcountsestimated using Urban-RuralandStratum-Level|
|Regression Models..............................................................<br>29|
|Figure4.ReceiverOperating CharacteristicCurvesforSelectedTargeting Variables.....32|
on quality of education in schools, this paper contributes to the urban economics literature by analyzing the existence of externalities and complementarities between this kind of public infrastructure and schools or households near to the libraries.

The causal effect of access to public libraries on student academic performance is assessed using a Difference-in-Differences (DiD) methodology, combined with propensity score matching as a robustness test of the results. The procedure takes advantage of the spatial location of the libraries with the first, _El Tunal_ constructed on the grounds of a public park, and the second, _El Tintal_ , in an old garbage processing plant. We compare the average results on standardized test scores at the end of secondary level studies of schools (SABER 11) close to the libraries and those far from them from 2000 to 2008, that is, before and after the libraries’ opening. This concept is implemented under both parametric and nonparametric specifications of the relationship between distance to the library and test scores. We also implement Oaxaca-Blinder decomposition of the impact of the program on the quality of education to explore the possible improvement via the variation of traditional inputs of education quality.

Given our specification, we are considering both the direct and indirect impacts that the libraries could have on student performance. Direct impact might come from the possibility that students living close to libraries access library services and programs independently or that nearby schools deliberately take advantage of the library for their own activities. Indirect effects might come from the impact of the renovation of the public infrastructure on the area which could improve crime perceptions, the general mood of the population, or other neighborhood effects. Due to the lack of information on students’ actual residences or on specific school programs which take advantage of the libraries, we cannot assess these channels separately.

Our main results show that while the relationship shows the expected positive sign, results are not statistically significant. This either tells us that the libraries are not fully exploited by schools or that the possible gains are concentrated among particular types of individuals. This opens the question of how aligned incentives are to foster cooperation between schools and public libraries in order to improve the quality of education. Perhaps it is not enough to construct beautiful and well-equipped libraries that are near to schools; a second generation of policies might be required to enhance the coordination between these 3
**Figure 2: Population, geographical scale of population dispersion, and dispersion-adjusted population sparsity across case-study PICs**

<!-- Start of picture text --> 1,000<br>800<br>600<br>400<br>200<br>0<br>Fiji Solomon Vanuatu Kiribati FSM Tonga RMI Tuvalu<br>Islands<br>800<br>600<br>400<br>200<br>0<br>Fiji Solomon Vanuatu Kiribati FSM Tonga RMI Tuvalu<br>Islands<br>20<br>15<br>10<br>5<br>0<br>Fiji Solomon Vanuatu Kiribati FSM Tonga RMI Tuvalu<br>Islands<br>Panel A<br>Population ('000)<br>Panel B (km)<br>Geographical scale of population dispersion<br>Panel C<br>Dispersion-adjusted population sparsity<br>(square km per persron)<br><!-- End of picture text --> 11