rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 2. GDP per Capita Relative to the World’s, 1950-2000**

<!-- Start of picture text --> 3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>LatAm South East Asia Southern Europe<br>19501952195419561958196019621964196619681970197219741976197819801982198419861988199019921994199619982000<br><!-- End of picture text --> Notes: LatAm includes Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Mexico, Panama, Peru, Uruguay and the República Bolivariana de Venezuela. South-East Asia includes China, Indonesia, Malaysia, Singapore, Thailand and the Philippines. Southern Europe includes Greece, Italy, Portugal and Spain. _Source: Maddison Project._
Going back to 2001, the Russian government has been progressively increasing the number of tariff lines with a specific tariff component. Table 1 shows that the number of such tariff lines increased from 1609 in 2001 to 2020 in 2011. WTO accession will have an opposite impact. As a result of WTO accession, the number of tariff lines with a specific tariff component will progressively fall, reaching 1371 by the year 2019.

# **Impact of the WTO Commitments (Simple Average Bound Tariff)**

The ―bound‖ or maximum average un-weighted MFN tariff of Russia will be 8.6 percent after all commitments are implemented. Countries may apply tariffs at rates less than their ―bound‖ maximum rates.<sup>17</sup> Assuming the applied tariffs are not increased, the average ―bound‖ MFN tariff of Russia in 2020 will be higher than the average applied un-weighted MFN tariff of 7.9 percent.

We calculate that there are 1498 tariff lines where the current applied tariff of Russia is less than the final ―bound‖ rate under the commitments of Russia to the WTO. 760 of the total tariff lines with applied MFN tariffs less than WTO bound tariff levels are in the machinery and mechanical appliances and electrical machinery and equipment categories (2-digit categories 84 and 85). Examples at the ten digit tariff line level where the value of imports is large are the following. Over $2 billion of imports were in three ten digit categories of auto parts. They entered with a zero import tariff in 2010 in the Customs Union, while the final bound rate at the WTO for these categories was either five or fifteen percent.<sup>18</sup> Over $1 billion in electricity turbo-generators (ten digit code 8502292000) entered in 2010 with a Customs Union tariff of zero, while the WTO bound rate is five percent.

> 17 The WTO (2011) Secretariat estimated that the un-weighted average bound tariffs of Russia will be 7.8 percent in 2020.  Our estimates are slightly higher for two reasons.  Due to data limitations, the WTO statistical office had to calculate the ad valorem equivalents of the specific tariffs at the eightdigit level, rather than at the ―ten digit‖ tariff line level of the tariff schedule of the Russian Federation. We had the more detailed ten digit data available, which allows more accurate calculations. See WTO (2007) for more details on their methodology for calculating the ad valorem equivalents of specific tariffs. Further, also due to data limitations, the WTO calculations ignored specific tariffs on a limited number of tariff lines where the specific tariffs and the import data were in different units, an omission which leads to a downward bias in the WTO estimates.  This problem arises in the case of motor vehicles. We obtained concordance from using information in other parts of the tariff classification that allowed the concordance (as explained above). Thus we estimated the tariff equivalents of the specific tariffs for all ten digit tariff lines.

> 18 These categories are 8707101000 (auto bodies), 8708291000 (parts and accessories) and 8708402001 (gear boxes).

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and male-owned firms. A 10% increase in tradable services employment leads to a 13.7% increase in female-owned firms in non-traded services for women compared to a statistically insignificant 1.6% increase for male-owned firms. Finally, we find that the effects are only significant for small non-tradable service firms (for firms between 1-10 workers).

Our paper contributes to several strands of literature. First and foremost, our paper is related to the literature on structural transformation into the services sector. Eichengreen – and Gupta (2013) find that the growth of modern, tradable services finance, ICT, and business services – started at lower levels of per capita income after 1990 than in the preceding four decades, thereby benefiting developing economies relatively early in their structural transformation process. Furthermore, the growth of these services has improved educational outcomes. Oster and Steinberg 2013 show that the IT revolution in India boosted the enrollment of girls and boys, equally, in schools with English as the language of instruction. Nano et al. (2021) find that employment growth in telecommunications and financial services, boosted by liberalization in these sub-sectors, increased school enrollment rates. As a result, the increase in the skill premium was also less pronounced in India (Shastry 2012). Using data from India, Fan et al. (2021) show that even traditional, non-tradable services have contributed to productivity growth, albeit benefiting consumers at the top of the income ladder more.

Our paper also contributes to the literature on how linkages between the services and manufacturing sectors benefit overall economic growth. A substantial body of evidence across countries shows that the services “embodied” in manufactured goods have a significant impact on manufacturing productivity (Arnold et al. 2016; Arnold et al. 2011; Bas and Causa 2013; Francois and Woerz 2008). Services used as inputs in the manufacturing sector have benefited from growth in the latter too. Evidence from India shows that growth in manufacturing has accelerated growth in value added and worker productivity in services firms within the same geographic region (Dehejia and Panagariya 2016).

We also contribute to the literature on the effects of globalization on non-tradable services. Mu ̃noz (2021) analyzes the impact of “posting” policies in the European Union (EU) that enable firms in one country to send (“post”) their workers to perform non-tradable services jobs, such as plumbers or drivers, in another country. She finds that firms in previously “non-tradable” services increase their sales, profits and wages when accessing foreign markets through the movement of workers across national boundaries. Such exports of services are less prevalent outside the EU where the movement of labor is constrained by regulatory barriers. Non-tradable services can also benefit from globalization indirectly through greater 4
Improving Preschool Provision and Evidence from a Encouraging-Demand: Large-Scale Construction Program<sup>_∗_</sup> Jan Berkes<sup>_†_</sup> , Adrien Bouguen<sup>_‡_</sup> , Deon Filmer<sup>_§_</sup> , Tsuyoshi Fukao<sup>_§_</sup> Originally published in the Policy Research Working Paper Series on _December 2019._ This version is updated on _February 2022._ To obtain the originally published version, please email prwp@worldbank.org.

_Keywords:_ early childhood development; preschool; cognitive skills; socio-emotional skills _JEL Codes:_ I2,I3

> _∗_ This research project is a joint effort between the Cambodian government, the World Bank, a team of field researchers (Angkor Research), and a team of academic researchers. We are particularly grateful to the World Bank for its constant support: special thanks to Simeth Beng. This research was funded by SIEF. Special thanks to Alaka Holla for her useful comments throughout the program’s duration. The project could not have been conducted without the collaboration of the Ministry of Education, Youth and Sport in Cambodia. We are particularly grateful to Sok Sokhom and Lynn Dudley. Data were collected in the field by a team of very dedicated field researchers from Angkor Research: special thanks to John Nicewinter, Ian Ramage, Benjamin Lamberet, Kimhorth Keo, and Ratanaksophea Saing. Finally, many researchers also contributed to this research through their very useful comments: Craig McIntosh, Patrick Kline, Christopher Walters, Karen Macours, Diego Vera, Markus Fr ̈olich, Paul Gertler, Supreet Kaur, Edward Miguel, Katja Kaufmann, Elisabeth Sadoulet, Analia Schlosser, Alain de Janvry, Cl ́ement de Chaisemartin, Antoine Camous, Harald Fadinger, Luc Behaghel, Clement Imbert, and Marc Gurgand. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. The study was preregistered at AEA’s Social Science Registry (AEARCTR-0001045).

> _†_ DIW Berlin, Federal Ministry of Labour and Social Affairs Germany

> _‡_ Santa Clara University. Adrien Bouguen also acknowledges financial support from the German Research Foundation (DFG) project SFB 884 during his stay at the University of Mannheim

> _§_ The World Bank
estimated using micro data from household surveys are smaller than those produced by macro studies.

This is especially true for elasticities of electricity access, previously computed in the 0.18 to 0.43 range, while our estimate is at most 0.06 on average and 0.16 for the highest country value (Peru). Similarly, macro estimates for cell phones ownership and internet access are lower than when estimated with household survey data. The closest values are for water and sewerage, although the macro estimates tend to match the highest household data country estimates.

It is probable that this disparity is partly due to the ability of disaggregated household data to control adequately for the prices of infrastructure through country-time fixed effects, while macro data can only include time invariant country fixed effects. Consequently, the difference in estimates appears bigger when dealing with services for which the price matters more and has experienced large shifts over the last two or three decades, such as cell phones, internet access, and to a lesser extent electricity.

Several factors are likely to be key in understanding the differences in how countries have been able to accommodate the demand pressure on infrastructure stemming from growing incomes. First, demand side issues are likely to matter, for example the extent that credit constraints have limited the ability of households to access infrastructure and related equipment.

In addition, on the supply side, it is likely that constraints related to limited investment and institutional capacity are playing a role. Given these likely binding supply constraints, it is enlightening to simply ask what these elasticities mean in terms of the ability of policy makers throughout the region to meet very simple universal access goals, akin to the early MDGs criteria, such as for water “access to a source of water in the house or lot.” Making simple assumptions on future income growth, we can forecast for each country the number of years it would take to offer universal access to a given infrastructure service under a “business as usual” scenario.

Consider very simple mechanical projections, assuming a high income growth scenario similar to the one of the 2002-2012 period and the average historical elasticities just discussed (Table 7).<sup>11</sup> With the exception of Argentina, which is close to full access according to this criterion,

> 11 Using average elasticities is a safe approximation given that Table 5 shows they display little variations across income quantiles. Given that the values in Table 5 give “semi-elasticities”, for any value ε, ε/100 is the unit change in access when income increases by 1%. The number of years to close the gap to full access is then given by N = (1-C)/(ε/100.g), where C is the current coverage (between 0 and 1), and g is the income growth rate.

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_Figure 23: Decomposition of consumption change in real terms compared to the level of 2014, %_ <!-- Start of picture text --> 6 3<br>inkind<br>4 2<br>2<br>1<br>other food<br>0<br>0<br>-2<br>fruits and<br>-1<br>-4 vegetables<br>-6 -2<br>diary<br>-8 -3 products<br>-4 meat and<br>fish<br>average Poorest Deciles 2-3 Deciles 4-7 Deciles 8-9 Richest<br>-5<br>decile decile<br>2015 2016 2017 staples<br>food other consumables durables<br>Poorest decile<br>utilities other services total<br>2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> Total consumption by deciles<br><!-- End of picture text --> <!-- Start of picture text --> (b) Food consumption by the 1st<br>decile<br><!-- End of picture text --> Note. See Methodological notes for description of the procedure applied to estimate real consumption. Source: Authors’ estimates based on HSS data.

Across different types of households, the most severe cuts in food expenditures during the recession were observed among households with two or more children (Figure 24). It should be noted, that these households have the lowest level of per capita expenditures on food among the analyzed social groups, in part because of the economies of scale in these households and the significant role of in‐kind income from subsistence farming. This is worrisome, as it may be indicative of worsening nutrition in households with children, although we cannot confirm this with the data at hand. A significant reduction of expenditures on food was also observed among the working age population with a low level of education, i.e., the low paid labor force, and in the rural population, who have a very high probability of being poor. In contrast, such groups as residents of Minsk, retirees, households without children, working age population with a high level of education, that have a very low risk of poverty, spend on food much more than the average household and did not cut these expenses substantially during the crisis.

_Figure 24: Real change in food consumption during crisis (2014‐2016) for major socio‐economic groups depending on initial level of food consumption, %_ <!-- Start of picture text --> 2<br>food expenditure per capita in 2014, in BYN in prices of 2014<br>0 partly_employed<br>40 50 60 70 80 90 100 110 120 130<br>minsk<br>-2<br>retiree_hh<br>small_cities retiree<br>-4 hh_single_parent high_edu hh_no_kids<br>average<br>child large_cities<br>nonemployed<br>-6 hh_1child working_ageemployed<br>sec_specialized_edu<br>-8 hh_2kids rural<br>other_edu<br>hh_3more_kids<br>-10<br>-12<br><!-- End of picture text --> Source: Authors’ estimates based on HBS data.

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**Figure 1: High satisfaction with health and education services in Indonesia**

<!-- Start of picture text --> Health services Education Services<br>100<br>100<br>80<br>80<br>58.1<br>60 60 50.2<br>32.2<br>40 40 30.1<br>20 7.7 20 10.7 7.2<br>1.2 0.8 1.7<br>0 0<br>% of households<br>NA NA<br>Satisfied Quite Satisfied Quite unsatisfied Not Satisfied Satisfied Quite Satisfied Quite unsatisfied Unsatisfied<br><!-- End of picture text --> _Source_ : GDS-2 (2006) 34
|With control for structure|GDP/Price<br>Aggregate/Price<br>GDP/Aggregate<br>_↓_<br>_↓_<br>_↓_<br>Aggregate<br>GDP<br>Price||_−_1_._05_∗∗∗_<br>0.12<br>0.15<br>(0.21)<br>(0.11)<br>(0.67)<br>_−_1_._31_∗∗∗_<br>_−_0_._13<br>_−_0_._36<br>(0.28)<br>(0.19)<br>(0.77)<br>_−_0_._87_∗∗∗_<br>_−_0_._65<br>_−_2_._73_∗_<br>(0.31)<br>(0.44)<br>(1.44)||9.38_∗∗∗_<br>4.23<br>3.03<br>0.31<br>0.19<br>0.52<br>0.59<br>1.28<br>3.83|gnifcance at the 1%, 5%, and 10% level respectively.|. Standard errors are reported in parentheses.|
|---|---|---|---|---|---|---|---|
|out control for structure|Aggregate/Price<br>GDP/Energy<br>_↓_<br>_↓_<br>GDP<br>Price||0.31_∗∗_<br>0.61<br>(0.15)<br>(0.65)<br>_−_0_._01<br>0.31<br>(0.21)<br>(0.92)<br>_−_0_._97_∗∗_<br>_−_3_._30_∗∗_<br>(0.47)<br>(1.56)||15.79_∗∗∗_<br>1.45<br>0.35<br>0.40<br>0.09<br>1.01|r causality. _∗∗∗_, _∗∗_, and _∗_denotes si|ic for the test of Granger causality|
|With|GDP/Price<br>_↓_<br>Aggregate||_−_1_._19_∗∗∗_<br>(0.28)<br>_−_1_._33_∗∗∗_<br>(0.28)<br>_−_0_._88_∗∗∗_<br>(0.31)||7.74_∗∗_<br>0.42<br>1.18|tion of Grange|quared statist|
|Income group||Long run|High income<br>Upper middle income<br>Lower middle income|Short run_†_|High income<br>Upper middle income<br>Lower middle income|Note: _↓_denotes direc|_†_ represents the chi s|

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slightly older). When estimating variants of equation _(1)_ , I therefore define the treated cohort as comprising individuals aged five to nine years at the beginning of the conflict in 1996, such that all treated cohorts are exposed to the conflict during most of their potential primary schooling careers and the youngest exposed cohort is observed at age 14 in the 2006 DHS. In the main regressions, the control cohorts include individuals aged 16 to 19 years at the beginning of the conflict—that is, individuals who were born too early to have their primary education affected by the conflict but are as close, and therefore as comparable, to the treated cohorts as possible. I also provide a robustness check in which the control group comprises individuals aged 18–25 years at the start of the conflict. In the baseline specification, I exclude cohorts aged 10 to 15 years in 1996 because the treatment status of these cohorts is less clear. Many of these individuals could have been enrolled in primary schooling during the conflict, but they were not exposed to conflict during most of their primary schooling careers (see figures S3a and S3b).

Panel A of table 1 illustrates the basic identification strategy used in the baseline specification. This panel shows the difference in the increase in primary schooling between cohorts exposed (row (1)) and not exposed (row (2)) to conflict during their primary schooling careers in districts experiencing above-median conflict casualties (columns (1) and (4)) compared to districts experiencing below-median conflict casualties (columns (2) and (5)). Women born too early to be affected by the conflict during their primary schooling years have a much lower rate of primary schooling in high-conflict districts compared to women in low-conflict districts. In contrast, primary schooling completion is slightly higher in high-conflict districts compared to low-conflict districts among the cohort of women who were entering primary school around the beginning of the conflict, resulting in an additional increase in female primary schooling of 19 percentage points between the two cohorts in high-conflict areas compared to low-conflict areas. A qualitatively similar but less dramatic effect is observed among men.

To shed light on the direction of the potential biases due to differential preconflict trends, I conduct several control or “placebo” experiments in which conflict exposure is artificially assigned 17
# **IV. Rates of Response**

**Table 1: Regional Breakdown of Survey Responses**

||**Number of Surveys**<br> <br>||
|---|---|---|
|**Region**|**Sent to Region**<br>**Number of Responses**|**% Response**|
|AFRICA|218<br>39|18|
|EAST ASIA|134<br>38|28|
|LATIN|||
|AMERICA|289<br>70|24|
|MIDDLE|||
|EAST|74<br>24|32|
|SOUTH ASIA|135<br>24|18|
|EUROPE|273<br>88|32|
|TOTAL|1123<br>283|25|

As with any survey, the first question to be asked is: What was the response rate? In the case of this survey, Table 1 shows that the overall response rate was 25%, with 283 surveys received from 78 countries.<sup>8</sup> This response rate is relatively high for an international survey of this breadth. An interesting point of comparison is the lower response rate of a survey of Fortune 1000 companies conducted in 1994 by James Neelankavil.<sup>9</sup> In order to evaluate how American firms viewed MBA programs, Neelankavil sent out a questionnaire to 1000 human resource directors of Fortune 1000 firms and received 108 responses, implying a response rate of 10.8%, which is significantly lower than the response rate of this survey. The high response rate is a result of the fact that the surveyed firms are IFC clients, which means that they belong to a specific subset of firms with strong international relationships. Therefore, the sample of firms in the survey is not fully representative of all firms in developing countries, and the results may be slightly biased.

The regional breakdown of firms is based on the World Bank classification system: (1) Africa (excluding the North African countries), (2) East Asia, (3) Latin America and the Caribbean, (3) Middle East and North Africa, (4) South Asia, and (5) Europe. Though Europe can be divided further into Eastern and Western Europe, most of the European responses were from Eastern European countries (see Annex B) and thus the general heading of Europe was used to classify responses from that region Table 1 also shows that the regional response rates are uniformly high, ranging from 17% for the African region to 32% for the European region. Even though it is clear that conclusions from the survey for each region are also dependent on the distribution of

> 8 See Annex B for the list of countries, and number of responses from each country.

> 9 Neelankavil, J., “Corporate America’s Quest for an Ideal MBA,” _Journal of Management Development_ , Vol. 13 No. 5, 1994.

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(iii) One common finding of empirical research (based on eq. 1) on Q theory is that variations in Q are unable to explain a large part of the variations in investment<sup>5</sup> ; the residuals also turn out to be highly serially correlated, suggesting that important explanatory variables may have been omitted.<sup>6</sup> For instance, output and profit variables still enter significantly when added to the Q investment equations. Abel and Blanchard (1986) examined specifications where the two components of marginal Q--marginal profit and the cost of capital--are specified as separate regressors. They found that the marginal profit component had a larger and more significant effect on investment than the cost of capital component, lending further empirical support to the earlier finding of Bischoff (1971) and Eisner (1978) that investment responds more to changes in output than to variations in the user cost of capital. Likewise, Fazzari et al. (1988) and others, have found the cash flow term to be significant in specifications of the Q model.<sup>7</sup> This approach is considered in greater detail below.

(iv) The empirical validity of the Q theory remains an unresolved issue. A strict interpretation of the Q model would predict a decline in investment following a dramatic decline in stock prices. This however did not happen after the stock market crash of October 1987. For instance, in Dun and Bradstreet's (1988) survey of 5000 firms, about 75 % of the companies said that their 5 See for instance von Furstenberg (1977), Summers (1981), and Poterba and Summers (1983).

**6** Given that the levels specification leads to serially correlated errors, the Q model is usually estimated in first differences. See Blanchard et al. (1993) for instance.

' Cash flows are defined as the sum of retained earnings and depreciation. In this paper, the terms cash flows and internal finance are used interchangeably.

6