rafmacalaba/datause-encoder-data huggingface dataset:
researchers have to rely on the aggregated statistics published by Goskomstat. The Russia Longitudinal Monitoring Survey (RLMS) initiated in 1992 by the University of North Carolina at Chapel Hill and the World Bank is representative on the national level and for 5 macro regions and covers a sample of 4,000 households (Zohoori et al. 1998). About 20 papers rely on the publicly available RLMS data to analyze poverty in Russia. The National Survey of Household Welfare and Program Participation (NOBUS) is a larger survey of 50,000 households that was conducted by Goskomstat with the support of the World Bank in 2003<sup>5</sup> . NOBUS is representative on the national, regional and subregional levels. Data from NOBUS is also publicly available and has been used in four of the papers in our sample.

Empirical data for the majority of publications come from regional, specialized surveys. Fourteen percent of the papers use regional household surveys conducted by the Institute for Social and Economic Studies of the Population (ISESP). Among these are: a longitudinal survey of the living conditions in Taganrog (Katz 2001); a survey of urban households in St. Petersburg and Vyazniki (1997); a household survey of the Volgograd oblast; surveys in Tambov, Nizhnii Novgorod, and Dmitrov (1997). Poverty researchers often rely on data from polls and surveys conducted by the All-Russia Public Opinion Research Center (VCIOM). Data for other studies come from specialized surveys implemented by the Ministry of Labor, local research institutes, and local governments.

Thus, less than 10 percent of all the papers on poverty might claim that their findings can be extended to the national level. Free public access to RLMS and NOBUS data also ensures that studies based on such surveys could be replicated. Neither HBS, nor regional or specialized surveys are accessible by Russian researchers. No paper in our sample presents a clear and detailed description of the sample design methodology, construction of the sample frame, or survey instruments – all of which form a standard set for any empirical publication.

> 5 NOBUS is a literal translation of the Russian abbreviation “НОБУС” that stands for “Национальное обследование благосостояния населения и его участия в социальных программах”. A detailed description of the survey and the actual data could be found at: http://nobus.worldbank.org.ru.

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38 is an 'nreliable indicator of<sup>malnutrition because generally applicable</sup> &tandards of requirements are<sup>more difficult to define."></sup> The most important micronutrients<sup>from the point of view of their</sup> nutritional consequences and the<sup>number of people affected are iodine, iron</sup> and vitamin A. The best known iodine<sup>deficient areas are the Andes in Latin</sup> America, Himalayas in Asia, and the<sup>mountainous areas of sub-Saharan Africa.</sup> Of the estimated 800 million people<sup>at risk of iodire deficiency, about 85</sup> percent are in Asia. The remainder<sup>are equally distributed between Africa and</sup> Latin America. In general, sufficient<sup>data to study regional trends over time</sup> in dietary deficiency in micronutrients<sup>is not available.</sup> In summary, the indicators examined<sup>in this paper only reveal broad</sup> trends, at the aggreg&te level in<sup>the nutrition problem in developing</sup> countries. From the point of view<sup>of household nutritional objective the</sup> policy focus must be on actually<sup>identifying the affected households for</sup> short-term targeted nutritional interventions.<sup>In addition, programs aimed at</sup> strengthening their capacity to<sup>acquire the minimum caloric requirements</sup> through increased income or production<sup>and to cope with the nutritional</sup> consequences of fluctuations<sup>in income or production are essential elements of</sup> the longer-term policy focus.

34Reutlinger Shlomo and Marcelo Selowsky, "Malnutrition and Poverty: Magnitude and Policy Options,"<sup>World Bank Staff Occasional Paper No. 23</sup> (Washington, D.C.: World Bank, 1976), p.<sup>9</sup> .
Policy Research Working Paper 0

# **Abstract**

This paper studies the medium-term impact of early-life welfare transfers on children’s learning. It studies children who were exposed to the randomized controlled trial of the Mexico’s Food Support Program (the P _rograma de Apoyo Alimentario_ , PAL), in which households were assigned to receive cash, in-kind food transfers, or nothing (a control). The children are matched with administrative data on primary school standardized tests, which were taken four to 10 years after the experiment began. The findings show that in-kind transfers did not impact test scores, while cash transfers led to a significant and meaningful decrease in test scores. An analysis of the mechanisms driving these results reveals that both transfers led to an increase in child labor, which is likely detrimental to learning. In-kind food transfers, however, induced a greater consumption of several key micronutrients that are vital for brain development, which likely attenuated the negative impacts of child labor on learning.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted atcavitabile@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Change in HOI over time can be used to assess progress in gaining access to opportunities in a**

**society** , taking into account both the universality of access and the inequality of access among different circumstance groups. To help understand the factors that contribute to a change in HOI, a decomposability property of HOI is useful. A change in HOI can be decomposed into (i) a _composition_ effect, which refers to changes in the distribution of circumstances (for example, if the distribution of income improves, chances of accessing opportunities are likely to increase); (ii) a _scale_ effect, which refers to the proportional change in the coverage rate of all groups (for example, if there is policy directed toward increasing coverage of an opportunity across all groups); and (iii) an _equalization_ effect, which refers to the change in the coverage of vulnerable groups (groups with coverage below the national average), with the average coverage rate held unchanged—in other words, a move toward greater or less inequality for the same average level of coverage.

**Interpretations of the three decomposed components are quite intuitive** . A positive composition effect shows whether a child’s underlying circumstances at birth are improving over time as a result of demographic changes, economic growth, or social progress. A positive scale effect shows whether opportunities are improving for all groups in the society, perhaps as a result of public policy or social progress (for example, increased awareness among all households). The equalization effect in essence indicates the trend in equity in a society, showing whether available opportunities are distributed more equitably among its members, so that a child’s circumstances at birth begins to decrease in influence regarding access to basic goods and services.

**There are a number of limitations associated with the analysis of equality of opportunities** (Abras and Cuesta 2011). First and foremost, the HOI circumvents the challenge of controlling differences of effort among individuals and groups by selecting children, not adults, for the analysis, because children cannot be made responsible for differences in effort, if any. A second limitation is a degree of arbitrariness involved in the selection of some key concepts. The distinction between circumstance and opportunity is not always clear. For example, being exposed to or the victim of conflict may be considered as both a circumstance (in the same way as urban or rural residence) as well as a lack of

9
Schmitz, Troy G., Andrew Schmitz and James L. Seale, Jr. (2003). Brazil’s Ethanol Program: The Case of Hidden Sugar Subsidies.” JRTC 03-1 April 2003 The International Agricultural Trade and Policy Center (IATPC) Food and Resource Economics Department (FRED) of the Institute of Food and Agricultural Sciences (IFAS) at the University of Florida.

Serra, T. (2011). “Volatility Spillovers between Food and Energy Markets: A Semiparametric Approach.” _Energy Economics_ , Vol. 33, No. 6: 1155-1164.

Serra, T. (2012). “Biofuel-Related Price Transmission Literature: A Review.” Unpublished manuscript. Centre de Recerca en Economia I Desenvolupament Agroalimentaris (CREDA), Castelldefels, Spain.

Serra, T., D. Zilberman and J.M. Gil. (2011). “Price Volatility in Ethanol Markets.” _European Review of Agricultural Economics_ , Vol. 38, No. 2: 259-280.

Tokgoz, S. and Elobeid, A. (2006). “Policy and competitiveness of US and Brazilian Ethanol.” _Iowa Ag Review_ 12: 6–11.

Zilberman, D., (2012). “The Future of Biofuel in Brazil” A presentation presented at the Berkeley Bioeconomy Conference, March 28, 2012. - <u>http://www.berkeleybioeconomy.com/wp content/uploads//2012/04/Zilberman_The_Future.pdf</u> Zilberman, D., Hochman, G., Rajagopal, D., Sexton, S., and Timilsina, G. (2013). “The Impact of Biofuels on Commodity Food Prices: Assessment of Findings.” _American Journal of Agricultural Economics_ 95(2): 275-281.

36
economies. The proportion of R&D funded by the business sector also tends to be quite close to the proportion it performs. Two exceptions are Malaysia and Singapore, where the proportion of R&D performed by business is significantly higher than the proportion it finances, indicating significant levels of government funding for R&D performed in the business sector. (Table 5 does not encompass tax incentives for business R&D, another widely used policy instrument).

Does one type of R&D contribute more to growth than another? Most of the evidence relates to developed economies, but it is informative. Guellec and Pottelsberge de la Potterie (2004) look at the long term impact of business sector R&D, public R&D (defined to include R&D performed by universities) and R&D performed in the outside world on total factor productivity growth in 16 OECD economies. (R&D in the outside world is introduced to capture the effect of international technology spillovers and transfers). Over the period 1980-98 they find the elasticity of productivity with respect to the stocks of business and public R&D to be broadly the same Crucially for developing countries, the stock of foreign R&D appears to have an impact 2-3 times as large as domestic business or public R&D, underlining the importance of openness to and capacity to absorb international knowledge.

**Table 5. R&D by Sector of Performance and Funding ***

||Sector o<br>|f Perfor|mance<br>|Sector<br>|of Fund|ing<br>|
|---|---|---|---|---|---|---|
||Business|Govt.|Higher<br>Educ.|Business|Govt.|Higher<br>Educ.|
|East Asia|62.2|21.7|14.4|54.3|35.2|2.3|
|NIEs|63.0|11.7|18.8|58.7|35.9|1.7|
|Hong Kong|33.2|3.1|63.6|35.3|62.8|0.2|
|Korea|76.1|12.6|10.1|74.0|23.9|1.7|
|Singapore|63.8|10.9|25.4|54.3|36.6|2.3|
|Taiwan, China|62.2|24.8|12.3|63.1|35.2|0.0|
|S.E. Asia|51.3|22.1|15.7|46.6|35.4|6.2|
|Indonesia|14.3|81.1|4.6|14.7|84.5|0.2|
|Malaysia|65.3|20.3|14.4|51.5|32.1|4.9|
|Philippines|58.6|21.7|17.0|59.7|24.6|7.5|
|Thailand|43.9|22.5|31.0|41.8|38.6|15.1|
|China|62.4|27.1|10.5|60.1|29.9|..|
|Developed (21)|62.9|13.3|27.0|49.2|33.6|2.1|
|Japan|75.0|9.3|13.7|74.5|17.7|6.3|
|USA|70.1|12.2|13.6|63.7|31.0|..|
|Latin Amer. (11)|29.0|27.2|32.7|32.9|37.3|27.4|
|Emerg.Europe (9)|42.7|29.8|20.1|38.3|54.2|0.5|
|<br>Source: UNESCO S<br>* For 2002-05,lates|cience & Tec<br>t availableye|hnology<br>ar. Med|Statistics<br>ians for r|May 2006.<br>egions and su|b-regio|ns.|

What conditions might affect the impact of each type of R&D has on growth? A key finding by Guellec and Pottelsberge de la Potterie is that a higher current _flow_ of business R&D increases the economy’s ability to absorb benefits from the accumulated _stocks_ of all types of R&D, business, public and foreign. In other words, higher current business R&D by domestic firms not only increases their ability to absorb the results of past and present R&D by other domestic firms, it also enhance firms’ ability to access knowledge created by the public sector and – crucially for developing countries – to access foreign knowledge. Another interesting finding is that foreign R&D appears to benefit small economies more than large ones.

21
percent in Brazil.<sup>18</sup> Relative to these economies, Brazilian spending is tilted towards computerized information and product development (other than scientific R&D).

Interestingly, Table 1 suggests that relative to more advanced economies like the US and the UK, Brazil and China have similar profiles of intangible expenditures. In both Brazil and China, computerized information accounts for 20-25 percent of all intangible expenditures, as opposed to only about 10 percent in the US. Economic competencies account for 30-40 percent of all intangible expenditures in the former, in contrast to as much as 53 percent in the latter.

# **3.2 Aggregate expenditures in intangible assets over time**

Figure 3 presents our estimate of total expenditures in intangibles assets by Brazilian firms during 20002008. The estimates are adjusted for inflation and expressed in year 2000 prices.

There is a clear positive trend in intangible investment, with total expenditure rising from 3.5 percent of GDP in 2000 to 4.8 percent in 2008. Innovative property (which includes R&D and other product development) is the largest among the three major intangible asset categories and spending on it has risen from 1.8 percent of GDP in 2000 to 2.3 percent in 2008. In comparison, total spending on the next largest category – economic competencies (which includes firm human capital, brand equity and organizational improvements) – has been slow to increase, rising from 1.3 to 1.6 percent of GDP during this period.

**Figure 3: Trends in Aggregate Intangible Expenditures (% of GDP)**

<!-- Start of picture text --> 5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>2000 2002 2004 2006 2008<br>COMPUTERIZED INFORMATION INNOVATIVE PROPERTY<br>ECONOMIC COMPETENCIES Total<br><!-- End of picture text -->

> 18 Or 40 percent, if one crudely adjusts for missing services data on employee training.

19
19 trade data in developing countries are in anyway "better" or more "reliable" than investment or consumption data. Furthermore, Bairam and Ng (2001 ) who investigated the stability of export and income elasticities embodied in the T-H model found that the export and import demand functions for Canada and UK were unstable during the period 1973-95. The sample of countries studied by Bairam and Ng included Canada, New Zealand, and the UK, where statistics are better developed than in most developing countries. Thus, there seems to be no guarantee of the stability of the elasticities in question for most of the developing countries. If these parameters are not stable the, T-H model is bound to yield biased results.

# **Concluding remarks.**

The brief examination of the World Bank and the T-H models reveals that the latter is a somewhat of a partial representation of the former. The T-H model lays emphasis on the balance of payments constraint only and has not taken into explicit consideration the aggregative saving-investment behavior that is implicit in the balance of payments picture. Thus, the exposition has missed the important point made by Easterly regarding the theoretical and empirical weaknesses of the investment/growth relationship underlying the World Bank model. Both Easterly and Hussain, however, fail to realize the practical ways in which these theoretical and empirical weaknesses are overcome in practical applications of these models. The World Bank's model is only a starting point of a policy dialogue between donors and developing countries that
<!-- Start of picture text --> Input Micro data Macro projections Price data<br>1)<br>g F &<br>3a=<br>a0 a 3<br>Oookeraticimocel Ind. Ain occ. status<br>Earniarnings . Ind. real earnings Aggregateto hhd level<br>Output . : equation. - .<br>Migration/remittances »| Hhd. remittances »| Consumption<br>Poverty _ incidence<br>Population<br>: =]<br>3<br>=<br>Population growth i<br>Income/consumption<br><!-- End of picture text -->

<!-- Start of picture text --> distributions<br>Rule: Best fit te micro Rule: Replicate; macro Headcount ratio<br>data proportional changes at GINI, Thiel T<br>micro level<br><!-- End of picture text -->
where is the depreciation rate. Suppose is the proportion of current period capital which is nonmalleable (i.e., sector-specific).<sup>9</sup> Following this notation, remains malleable in period and can move freely among sectors to equalize the capital rental rate according to condition (12). The nonmalleable capital of each sector, , is assumed to grow proportionally with the total nonmalleable capital as shown in Equation (18): Besides capital accumulation, this study also considers the labor force growth and labor productivity growth as motors that drive the economy. The labor force growth rate in Equation (23) is from the World Bank (2010). Additionally, while in the baseline, the labor productivity growth rate is calibrated to match BAU projected GDP growth rate given exogenously, in policy scenarios, the baseline labor productivity growth rate becomes exogenous and the GDP growth rate becomes endogenous.<sup>10</sup> II.3 Aggregating Electricity Generated by Distinct Technologies Electricity sector is the main contributor to national GHG inventory in many countries.

Although, this is not the case in Brazil at present because of pre-dominance of hydropower in the nation‟s electricity supply system, the role of fossil fuel based electricity generation technologies is increasing. It is therefore important to retain the characteristics of various types of electricity generation technologies in the model instead of representing them by a single technology. This

> 9 We assume that as in Markusen (2007).

> 10 This treatment is the same as Rausch et al. (2010).  Further, the BAU projected annual GDP growth rate of Brazil is assumed to be 4.1% up to 2030 according to the Empresa de Pesquisa Energética (EPE).  After 2030, we assume a 3% annual GDP growth rate.

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**Table 7. Effects of Standards on Firm Export Value, Quantity, and Unit Price**

||<br>D|<br>ependent Variabl|<br>e:|
|---|---|---|---|
||Log Export<br>Valuec,i,k,d,t|Log Export<br>Quantityc,i,k,d,t|Log Unit<br>Pricec,i,k,d,t|
||(1)|(2)|(3)|
|Rel_restrictivenessc,k,d,t|‐0.173|‐0.179|0.020|
||[0.057]***|[0.065]***|[0.032]|
|Log 1+Tariffc,k,d,t|‐0.045|‐0.071|‐0.003|
||[0.016]***|[0.023]***|[0.011]|
|Log GDP per capitad,t|1.340|0.852|0.182|
||[0.164]***|[0.189]***|[0.078]**|
|Log Total Exportsc,k,t|0.321|0.232|0.045|
||[0.015]***|[0.016]***|[0.012]***|
|Exporting Firm‐Importing<br>Country‐Product Fixed Effects|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|
|Estimation method|OLS|OLS|OLS|
|Observations|256,882|159,914|159,914|
|R‐squared|0.948|0.959|0.922|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

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to hold with adjustments to measurement of the key variables.

# **7. Conclusion**

This paper uses data on the near universe of US online job postings from January 2017 to December 2018 to analyse how the 2018 tariff hikes by both the US and its trading partners affected US labor market opportunities. We calculate three measures of tariff exposure to account for: (i) the protection of US industries by tariffs on their output products; (ii) the increased cost for US producers due to tariffs on their imported inputs; and (iii) foreign tariffs on US exports in retaliation to the US import tariff increases. We employ a shift-share estimation strategy that exploits the timing of the tariffs and the _ex ante_ reliance of each commuting zone’s labor force on trade in tariff-affected products.

We find that both the retaliatory export and imported input tariffs had an economically meaningful negative impact on job postings. In contrast, the impact of output tariffs is not statistically significant once imported input tariffs are controlled for. In terms of magnitude, a one-standard-deviation increase in commuting zone input tariff exposure led to a 3.6 percent decrease in online job postings, and a one-standard-deviation increase in commuting zone export tariff exposure led to a 7.3 percent decrease in online job postings. A back-of-theenvelope counterfactual calculation shows that these effects were concentrated in the second half of 2018 as the tariffs started to build up, and led to a estimated combined effect of 175,000 fewer job postings. Just over two thirds of this aggregate decline was due to the imported input tariffs and one third due to retaliatory tariffs. The lost postings represent a 0.6 percent decrease for the whole of 2018 and a 0.9 percent decrease for the second half of the year.

Both higher skilled and lower skilled job postings were affected by the tariffs, but the magnitude and statistical significance of effects is stronger for lower skilled jobs, for both intermediate inputs and retaliatory tariffs. In the lower-skilled category, adverts for production jobs were negatively affected by retaliatory tariffs, while adverts for farming, forestry, fishing, construction and extraction jobs were negatively affected by the input tariffs and installation, cleaning, maintenance and repair jobs were hit by both retaliatory and input tariffs. In the higher-skilled category intermediate input tariffs most negatively affected job adverts for professional and managerial positions, while retaliatory tariffs most negatively affected those 35