rafmacalaba/datause-encoder-data huggingface dataset:
capital: evidence from the golden age of upward mobility. Journal of Labor Economics, 40(S1):S39S95.

- Carneiro, P., García, I. L., Salvanes, K. G., and Tominey, E. (2021). Intergenerational Mobility and the Timing of Parental Income. Journal of Political Economy, 129(3):757788.

- Chetty, R., Hendren, N., Kline, P., and Saez, E. (2014). Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States. The Quarterly Journal of Economics, 129(4):15531623.

- Cholli, N. and Durlauf, S. (2022). Intergenerational Mobility. NBER Working Paper Series 29760, National Bureau of Economic Rsearch.

- Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., and York, R. L. (1966). Equality of Educational Opportunity. Government Printing Oce, Washington, DC.

- Currie, J. and Moretti, E. (2003). Mother's Education and the Intergenerational Transmission of Human Capital: Evidence from College Openings. The Quarterly Journal of Economics, 118(4):14951532.

- Deolalikar, A. B. (1993). Gender Dierences in the Returns to Schooling and in School Enrollment Rates in Indonesia. Journal of Human Resources, 28(4):899932.

- Dreze, J., Sen, A., et al. (1999). India: Economic development and social opportunity. OUP Catalogue.

- Dube, L. (1997). Women and kinship: Comparative perspectives on gender in south and south-east asia.

- Duo, E. (2001). Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment. American Economic Review, 91(4):795 813.

- Duo, E. (2004). The medium run eects of educational expansion: evidence from a large school construction program in Indonesia. Journal of Development Economics, 74(1):163 197.

- Emran, M. S., Jiang, H., and Shilpi, F. (2021). Is Gender Destiny? Gender Bias and Intergenerational Educational Mobility in India. GLO Discussion Paper Series 807, Global Labor Organization (GLO).

33
requirements are based on asset quality rather than only on asset type, and banks must use “marking to market” to price assets, rather than book value. As the rules make bank capital requirements more sensitive to changes in the banks’ risk exposure, and as the riskiness of loan books changes over the business cycle, the required regulatory capital varies with the business cycle. For instance, when asset prices start declining, banks may be forced to undertake continuous writedowns (accompanied by increased provisioning), and this raises their need for capital. Capital requirements may therefore increase in a cyclical downturn. If banks are highly leveraged, to maintain their capital ratio during a recession, they must either raise capital (which is difficult and/or costly in bad times) or cut back their lending, which in turn tends to amplify the downturn. Thus, the introduction of risk-sensitive capital charges may not only unduly increase the volatility of regulatory capital, it may also (by limiting banks’ ability to lend) exacerbate an economic downturn.

Most existing studies of the cyclicality of capital regulatory regimes, both theoretical and empirical, are based on industrialized countries.<sup>3</sup> However, the pervasiveness of financial market imperfections in developing countries, coupled with their greater vulnerability to shocks, makes a focus on these countries warranted. For middle-income countries, in particular, these imtheir portfolios and to determine their required capital cushion–provided that their internal model is validated by the regulatory authority. It also acknowledges the importance of two complementary mechanisms to safeguard financial stability, namely supervision and market discipline.

> 3For empirical studies on industrial countries, see for instance Ayuso, Pérez, and Saurina (2004), Bikker (2004), Gordy and Howells (2006), and Van Roy (2008). For theoretical contributions, see Blum and Hellwig (1995), Zicchino (2005), Cecchetti and Li (2008), and the literature surveys by Drumond (2008), and VanHoose (2008). Pereira (2009) provides references to the limited literature on middle-income countries. He also provides a critical review of the empirical evidence, based on the general equilibrium implications of the present paper.

4
the animal.<sup>16</sup> We do find a decrease in whether the animal was reported to the project assessment team as dead, stolen, or sold.

To provide additional information on the balance of the scorecard randomization, we present in Table 8 the same analysis as Table 7 but include the scorecard treatment and interaction terms. This is a form of balance test, as the data from Table 8 are from before the scorecard treatment. We thus expect no statistically significant coefficients from the scorecard and interaction terms. We do not find anything significant except for a small positive effect on the interaction term for whether the animal was productive. The coefficient is small relative to the control and significant at the 10% level. Combining this and the results from Table 5, we conclude that the scorecard sample is well balanced.

# **5.2 Impacts on welfare**

Six months after the initial project assessment, we conducted an additional household survey in the communities to measure household-level welfare outcomes. The household survey allows us to go beyond the measure of project quality obtained from the project assessment and estimate potential welfare impacts of the program at the household level.

In Table 9 we present the main outcome of interest for the household survey: the number of cattle and an index of total household asset ownership. Columns 1 and 2 present the asset outcomes for all project types, columns 3 and 4 present these outcomes only for livestock projects, and columns 5 and 6 present results for projects in the information scorecard sample. Note that we do not expect impacts on the number of household animals for any but the livestock projects. We prespecified a focus on this outcome as livestock projects represent 68% of the sample, and we believe these are the projects that are most likely to lead to direct changes at the household level. They are also the focus on the scorecard intervention. For this analysis, we restrict the sample to those who were direct beneficiaries of the program, i.e., those who were selected to receive animals from NUSAF2.<sup>17</sup>

> 16 Note that the illness index is reweighted as 1 minus the mean number of illnesses, so the positive coefficient means fewer observed illnesses.

> 17 The endline survey was conducted on eight beneficiaries per community in the control group. In the treatment group, we included six beneficiaries, as well as two non-beneficiaries who were selected to join the community management committee as part of the training intervention. We do not include non-beneficiaries in this analysis as we do not expect impacts from the treatment on household welfare.

22
The one go and the gradual approaches are distinct from the approaches followed by other RTAs like NAFTA, FTAA, and the Group of Three (see Table 1), where members aim to make their standards compatible with each other rather than harmonize them.

# **3.3.2 THE EU EXPERIENCE, WHAT HAVE WE LEARNT?**

The EU has the most comprehensive approach to harmonization of standards and conformity assessment. Its approach is markedly different from that of any other RTA and has undergone different phases.

Though by 1968 the EU customs union was complete, progress on the single market was blocked during a decade and a half by all manner of non-tariff barriers: Up to 70 forms were needed by truck drivers crossing EC borders; public contracts were reserved for national companies; product standards and professional qualifications were neither harmonised nor given mutual recognition; service industries were largely confined to their own member state; capital movements were not liberalized (Leach 2000).<sup>11</sup> The EU had been attempting before 1985 to set regional standards on the basis of members’ unanimous decisions and specifying in great detail products’ specifications. The EU realized that these features slowed down the elimination of non-tariff barriers and developed in 1985 _the New Approach_ .<sup>12</sup> This new approach is based on the principle of mutual recognition of standards or principle of equivalence: a country must accept the standards of member countries if such standards have the same objectives (they are then considered as equivalent). The burden of proof is not on the exporting firm but on the importer, who must prove scientifically that the objectives of the standards of the imported product differ from the objectives of the domestic product’s standard. Only in those sectors where the principle of equivalence cannot be applied because of the great divergence between standards (like health and safety requirements), does the EU follow a process of harmonization of standards. Since 1986, decisions are not based on unanimity but on qualified majority voting.

Support for the new approach was strengthened by studies like that of Cecchini, Catinat and Jacquemin (1988) also known as the “Cost of Non-Europe Report” which estimated the costs of barriers like divergent standards and technical regulations, border controls and customs red tape, conflicting business law and protectionist procurement practice to cost $243,000 billion.

The criteria used in setting EU technical regulations is based on the minimum requirement approach, which does not seek necessarily harmonization with international standards – unlike the Andean Community, Mercosur and ASEAN which do. Under this approach EU technical regulations are only concerned with guaranteeing health and security and protecting the environment and thus set only those specifications necessary

> 11 Quote taken from Single market definition at http://www.euro-know.org/dictionary/s.html

> 12 See European Commission (2000) for a detailed description of this approach.

18
- 17 -

2.16 There is a clear parallel between the income level of the country and the average size of the institutions: low income countries had the smallest institutions, followed by lower-middle income countries, and upper-middle income countries (Table 6).

Table 6: Average Size of Institutions Supworter By Mode and Income Level

|Income<br>Level|Secondary|M<br>Post-<br>Seconary|ode<br>Non-<br>Formal|Teacher<br>Traininrg|Overall<br>ISector<br>:Averages|
|---|---|---|---|---|---|
|Low|334|N6o|228|273|1<br>341|
|Lower-Mid|dle778|OD2|305|405|**I**<br>379|
|LUwer-Ilddle|<br>881|990|383|333|**1**<br>683|
|Overall<br>Averages|843|we|319|34S|I<br>506|

2.17 This suggests that stronger economies with larger modern sectors have higher perceived demand for VET graduates.

# Planned Costs Per Place

2.18 Given data on places supported and disaggregated cost data for the components in the sample, it is possible to derive crude indicators of the average total investment cost per place.

2.19 Interpreting cost comparisons across countries and training modes for projects spanning two decades, on the other hand, presents nearly insurmountable methodological problems. Input prices vary significantly over time, snd among countries. Some projects construct new facilities; others rehabilitate existing ones; many do both. The instructional program of an institution of a given mode in one country can and does vary significantly from that of an institution in the same mode in another country, with Important cost differences. Currency exchange rates differ. Political and natural disturbances cause significant delays and increase (or decrease) costs.
boc SPECUL ToPic 1: PROJECTING AIDS MORTALy

# References

- Bulatao, Rodolfo A. 1991. wThe Bulatao Approach: Projecting the Demographic Impact of the HIV Epidemic Using Standard Parameters.' Chapter X in: United Nations/World Health Organizadon (1991) _The AIDS Epidemic and its Demographic Consequences._ New York and Geneva.

- Bulatao, Rodolfo A. and Eduard Bos. 1989a. _Projecting MortaliyforAll Countries._ Policy, Planning, and Research Working Papers, No. 337. Population and Human Resources Department, The World Bank.

- Bulatao, Rodolfo A. and Eduard Bos. 1989b. _Implcadons of Control Measures for the Spread of HIV Infecton._ Paper presented at the Annual Meeting of the Population Association of America, New Orleans, April 1989.

- Bulatao, Rodolfo A and Eduard Bos. 1992. _Projechng the Demographic Impact of AIDS._ Policy Research Working Paper WPS 941.

- Chin, James. 1991. _Fonnal estimates of 1990 seroprevalence, Sub-Saharan Africa._ Unpublished Manuscript.

- Hill, Kenneth. 1990. _PROJ3S - A Computer Program for Population Projectons._ Washington, DC: The World Bank.

- Ryder, Robert W. et al. 1991. 'Fertility rates in 238 HIV-1 seropositive women in Zaire followed for 3 years post-partum.' AIDS 5,12:1521-1526.

- United Nations end World Health Organization. 1991. _The AIDS Epidemic and its Demographic Consequences._ New York and Geneva.

- United States, Bureau of the Census. _Recent HIV Seroprevalence Levels by Country: September 1990._ Research Notes No. 2, Health Studies Branch, Center for International Research.

- World Bank. 1990. Seminar on AIDS and Population Policy, August 28, 1990 (Background materials). Washington, DC: The World Bank.

> World Bank. Forthcoming. _The Socioeconomic Impact of AIDS in Uganda._ Washington, DC: The World Bank.
1% US income increase is expected to raise goods imports demand for China and India by 8.6 and 8.1%, respectively. Germany and Brazil show elasticities of 1.9 and 1.2. Interestingly, four Asian and two Latin American countries show negative elasticities, which is significant however only for Hong Kong, China.

Regarding services imports, the estimates become more significant. The F-tests show that only India, UK, and Venezuela should not include the error-correction term when calculating short-run effects. The long-run elasticities are nearly all positive and significant in 13 countries. Canada, China, France, Hong Kong, and Germany show the largest service import elasticities which range 7.6 and 4.7 %. Given the high absolute value of services imports from India, an elasticity of 2.1% still seems to be very high.

_Table 2_ shows the long-run income elasticities of US import demand for goods (column 1) and the “crisis elasticities” which are derived from data for the last half of 2008 for the major trading partners of the United States (column 3). In many cases the long-run estimate is many times less than what has been experience in the crisis period. For Brazil, for example, the long-run elasticity of 1.24 jumps to 11.6 in the crisis period. For Taiwan (China) it jumps from 0.77 to 7.6 and for South Africa from 1.21 to 28.9. For Mexico, the estimate is statistically insignificantly different from zero in the long-run estimate, jumping to 10.6 in the crisis. In the cases of India and China the long-run elasticities are not so different from the crisis period, falling slightly in both cases but from already extremely high long-run values. Our findings supports the results of Levchenko et al. (2009) who find an enormous gap (“wedge”) between predicted US imports based on historical data and the actual drop in US import demand.

**Table 2: “Crisis” vs. Long-Run Income Elasticities of US Import Demand**

||**Income E**<br>**Estim**|**lasticities:**<br>**ations**|**Income**<br>**C**|**Elasticities:**<br>**risis**|
|---|---|---|---|---|
||**Q1:1999**|**-Q4:2008**|**Q3:200**|**8-Q4:2008**|
|Country|Goods|Services|Goods|Services|
|Argentina|n.s.|2.25|-0.9|-4.6|
|Brazil|1.24|-0.60|11.6|1.0|
|Canada|n.s.|7.56|16.3|22.7|
|China|8.65|6.02|5.5|1.0|
|France|n.s.|5.41|2.3|10.8|
|Germany|1.86|4.70|5.4|5.9|
|HongKong|-2.75|5.50|13.3|-2.0|
|India|8.06|2.07|6.0|-3.2|
|Japan|n.s.|1.74|5.0|0.8|
|Korea,Rep. of|n.s.|0.68|6.7|13.1|
|Mexico|n.s.|0.79|10.6|2.4|
|Singapore|n.s.|1.92|7.6|7.0|
|South Africa|1.21|0.69|28.9|8.6|
|Taiwan|0.77|2.34|7.6|0.4|
|United Kingdom|n.s.|n.s.|13.1|7.4|
|Venezuela|n.s.|n.s.|61.3|0.1|

Source: Own calculations. Data: Bureau of Economic Analysis. NB: n.s. = not significant. Income elasticities Q3:2008-Q4:2008 are calculated based on equation (2), where  _M t_  _M t_  _M t_  1 , and  _Yt_  _Yt_  _Yt_  1 .

10
Table 19

# Composition of JDB Lending (% of total)

||1961|1971|1981|1991|
|---|---|---|---|---|
|Electric Power|50.3|21.6|28.8|38.3|
|oceanShipping|26.8|33.9|_10.9_|5.0|
|Coal<br>Mining|4.7|4.7|||
|Iron<br>&St-el|9.5|2.3|4.7|1.6|
|Priority<br>IndustrLem|91.3|62.5|44.4|44.9|
|Teztll-.|0.3|1.5|0.8|0.4|
|Chemicals|2.6|5.9|4.6|3.3|
|XachLnery|1.7|4.4|2.6|3.2|
|Other|4.0|25.7|47.7|48.1|
|Total|100.0|100.0|100.0|100.0|

Source: Japan Development Bank _s0_
51 Table 9: Top 25 countries in health economics based on _h_ -index

||Country|% not found<br>in Google<br>Scholar|_I_<sup>3</sup> =0<br>Publication<br>count|Rank|_I_<sup>3</sup> =0.5|Rank|_I_<sup>3</sup> =1<br>Total<br>citations|Rank|_h_-index (_I_<sup>1</sup>)|_I_<sup>2</sup><br>Quadratic<br>influence<br>function|Rank|Maximum<br>citations|Rank|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1|United States|16%|10995|1|37000|1|271014|1|189|120.01|1|4355|1|
|2|United Kingdom|16%|2214|2|7308|2|47056|2|96|21.32|2|774|2|
|3|Canada|18%|859|3|2492|3|15006|3|58|6.81|3|489|4|
|4|Netherlands|10%|408|5|1538|4|11223|4|51|5.07|4|486|5|
|5|Sweden|16%|260|8|954|6|6423|5|46|2.92|5|237|11|
|6|Australia|23%|467|4|1015|5|4993|6|36|2.27|6|415|6|
|7|Germany|17%|331|6|827|7|4125|7|34|1.88|7|358|8|
|8|<br>Switzerland|28%|226|9|576|9|3538|8|31|1.60|8|403|7|
|9|Norway|7%|133|12|450|10|2479|10|30|1.13|10|223|12|
|10|Spain|15%|289|7|608|8|3005|9|28|1.36|9|513|3|
|11|New Zealand|14%|128|13|347|11|1536|13|24|0.70|13|154|20|
|12|Italy|12%|155|11|324|13|1296|14|20|0.59|14|72|27|
|13|<br>Israel|9%|64|20|245|15|1757|11|19|0.80|11|305|10|
|13|Taiwan (China)|28%|97|18|189|20|888|18|19|0.41|18|89|24|
|13|France|28%|199|10|346|12|1623|12|19|0.74|12|157|18|
|16|Finland|13%|64|20|167|21|838|20|17|0.38|20|186|15|
|16|Denmark|12%|94|19|206|17|978|16|17|0.45|16|184|16|
|16|Belgium|13%|109|17|268|14|1083|15|17|0.50|15|71|28|
|19|<br>China|27%|113|14|199|19|863|19|15|0.40|19|104|22|
|19|South Africa|19%|110|16|214|16|801|21|15|0.37|21|68|29|
|21|Ireland|21%|63|22|143|22|926|17|13|0.42|17|219|13|
|21|Japan|18%|113|14|199|18|629|22|13|0.29|22|75|26|
|23|<br>Austria|11%|28|28|88|25|453|25|12|0.21|24|96|23|
|24|India|27%|63|22|125|23|613|23|11|0.28|23|105|21|
|25|Philippines|17%|18|37|62|26|398|26|9|0.18|26|157|18|
|25|Greece|24%|54|24|90|24|345|28|9|0.16|28|58|30|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case of a co-authored publication, but only the first institution was credited where an author listed multiple institutions. Institutional affiliations below the institution level were aggregated up to the level of the institution, so that for example publications originating from the Harvard School of Public Health were allocated to Harvard University along with publications originating from the Department of Economics at Harvard University. As explained in the text, only addresses with five or more articles to their name were retained for cleaning and aggregating. Institutions were then assigned a country. An _h_ - index of 10 means that the author has 10 publications to his or her name each of which has been cited at least 10 times. The quadratic influence function is defined in eqn (1). The measure _I_<sup>3</sup> is defined in eqn (2).
VAT and excise taxes are slightly progressive for women, mainly because of VAT. The VAT concentration curve for women (Figures 6b) is ambiguously progressive because it crosses the market income Lorenz curve; excise taxes are mildly regressive. Indirect subsidies are regressive for both genders, mainly because the wheat subsidy is so regressive (Figures 6a and 6b). This suggests that, although the wheat subsidy is meant to improve the welfare of the poor through price controls, richer consumers capture more of the total subsidy. The kerosene subsidy is also relatively progressive.

**Figure 6a: Concentration Curves for Indirect Figure 6b: Concentration Curves for Indirect Taxes and Subsidies, Men Taxes and Subsidies, Women**

<!-- Start of picture text --> 100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Kerosene Market income Kerosene<br>Wheat VAT Wheat VAT<br>Excise Population share Excise Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of indirect subsidies Cumulative proportion of indirect subsidies<br><!-- End of picture text -->

# _Education_

Public spending on education is progressive in relative terms. There is no difference by gender on progressivity.

Disaggregating the results by level of education, regardless of gender, spending on primary education has been found to be progressive (Figures 7a and 7b). Importantly, poor boys are benefiting relatively more than other boys, and primary education has dramatically reduced poverty for girls. The share of public spending on primary education for the poorest boys is 7.5 percent and for the richest 3.7 percent. Girls in the poorest decile capture 8.7 percent of primary education spending and those in the richest 5.8 percent.

Spending on secondary education is relatively progressive but public spending on tertiary education is regressive for both genders (Figure 7a and 7b). The poorest decile benefit from less than 1 percent of spending on tertiary education, but the top decile captures a remarkable 50 14
Table 1. Trends in caloric intake and undernourishment

|R|ound 1 – 2010|Round 2- 2013|
|---|---|---|
|Panel A: Daily per capita caloric intake|||
|_National_|2,256|2,371**|
||(39)|(45)|
|_Urban_|2,527|2,570|
||(104)|(127)|
|_Rural_|2,205|2,334**|
||(42)|(48)|
|Panel B: Undernourished prevalence|||
|_National_|0.532|0.497*|
||(0.017)|(0.018)|
|_Urban_|0.431|0.442|
||(0.029)|(0.047)|
|_Rural_|0.551|0.507*|
||(0.019)|(0.020)|

Change between two waves is statistically significant at *p<0.10, ** p<0.05, ***p<0.01. Note: Standard errors in parentheses correct for clustering and stratification. The descriptive statistics have been estimated from the cross-sectional samples corresponding to our final balanced sample. The national sample size includes 11,410 individuals in each round; 2,967 and 2,882 individuals are included in the urban samples in rounds 1 and 2, respectively, and 8,443 and 8,528 individuals are included in the rural samples in rounds 1 and 2, respectively.

Table 2. Trends in dietary diversity

||Round 1 – 2010<br>(SE)|Round 2- 2013<br> (SE)|
|---|---|---|
|Panel A: Raw food security score (FCS)|||
|_National_<br>|48.46|50.45***|
||(0.90_)_|(0.83)|
|_Urban_<br>|62.48|63.51|
||(2.17)|(1.31)|
|_Rural_<br>|45.84|47.97***|
||(0.85)|(0.80)|
|Panel B: Poor or borderline FCS|||
|_National_|0.268|0.192***|
||(0.017)|(0.018)|
|_Urban_|0.076|0.035***|
||(0.014)|(0.009)|
|_Rural_|0.304|0.222***|
||(0.018)|(0.020)|

Change between two waves is statistically significant at *p<0.10, ** p<0.05, ***p<0.01. Note: Standard errors in parentheses correct for clustering and stratification. The descriptive statistics have been estimated from the cross-sectional samples corresponding to our final balanced sample. The national sample size includes 11,410 individuals in each round; 2,967 and 2,882 individuals are included in the urban samples in rounds 1 and 2, respectively, and 8,443 and 8,528 individuals are included in the rural samples in rounds 1 and 2, respectively.