rafmacalaba/data-use-mentions-extended huggingface dataset:
regulation restricting new entrants or, in the case of transitional privatization, through the choice and structure of the buyers the state chooses (Bennett and Maw, 2003). That is, _a priori_ , we do not know whether to expect if state ownership will enhance or limit political influence.

# _Business associations and lobbying_

Participation in business and industry groups can be seen as a balance between the private interests of firms, the transactional costs of collective action, and the ability to move political actors (Grossman and Helpman, 2001). To act collectively, business associations must overcome a natural inclination of private actors to passively benefit from like interests by free-riding in the absence of compulsion or specific, ‘selective’ goods (Olson, 1965). The incentives of those within such collective action groups will be to penalize and reduce free riding (Becker, 1983). In equilibrium, then, it is unsurprising that in many economies, membership in industry chambers, labor groups, and other associations is mandatory. It follows that different firms, including those with more or less influence, will draw different benefits that these associations confer, including providing key information, coordinating market actors, and even engaging in collective negotiations.

> 20We note that this is a restatement of the construction of the Measurement Model in 2.2, which pins political connections to 1, but does not impose any directional constraints on the discrimination parameters of other items. 21Note that the Enterprise Surveys require at least some level of private ownership to be considered eligible for the survey, and so firms with complete state ownership are excluded.

11
a country for both domestic and foreign investment. The first is the ICRG measure of Expropriation Risk that has been widely used as a measure of property rights protection in the literature on institutions and growth. The annual data cover the period 1984-1997, and we average this over all available years for each country. The other is a measure of Rule of Law constructed by Kaufmann, Kraay, and Mastruzzi (2004). As this variable begins only in 1996, we simply use the 1996 value of this variable. The simple correlation between our measure of TFP and the net foreign asset position is negative, while the correlations of the two institutional measures with net foreign asset positions are positive. The latter positive correlations reflect the 18
**Table 15: FDI Spillovers Take Place through Foreign Backward Linkages**

|Dependent variable: ln labor<br>productivity (**_ln Y_** )|(1)|(2)|(3)|
|---|---|---|---|
|_ln FDI_for_backward_|0.017***||0.016***|
||(3.17)||(2.70)|
|_ln FDI_for_forward_||0.007|0.004|
|||(0.93)|(0.54)|
|_Constant_|3.629***|3.668***|3.628***|
||(92.39)|(94.62)|(90.71)|
|Country-sector FE|Yes|Yes|Yes|
|Country-year FE|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|
|Number of country-sector|2,017|2,017|2,017|
|Observations|11,892|11,986|11,865|
|R-squared|0.407|0.402|0.406|

**Notes:** The dependent variable is the log of labor productivity. The standard errors are clustered at the country-sector level. Country-year, sector-year and year fixed effects are included. *** p<0.01, ** p<0.05, * p<0.1.

# 7 Concluding remarks and further research

In this paper we link a project-level FDI database with the Multi-Regional Input and Output Table (MRIOT) to build a database with investment information and GVC participation indices for 61 economies and 35 sectors from 2011 to 2017. Our analysis finds evidence that FDI has an overall positive effect on labor productivity. In our preferred IV regressions, doubling FDI will lead to an 8 percent increase
through changes in observed trade policies, e.g., tariffs. However, it is more difficult to find appropriate measures for the initial change in trade costs in the case of “deep” and “hard” trade liberalization and protection scenarios, respectively. To this end, two popular approaches have been adopted. The first approach is to rely on additional observable trade policy measures (e.g., nontariff trade measures, NTMs) and to add the changes in those measures to the changes in tariffs from the “soft” scenario. This approach has two drawbacks. First, data on NTMs are often scarce and patchy, and the measurement and aggregation of NTMs can be difficult, c.f., UNCTAD-WTO (2012). In addition, even if NTM data were available, the combination of changes in observable

> 1While “shallow” vs. “deep” FTAs can be distinguished by the (number) of provisions included, for example, the concept of “soft” vs. “hard” effects is a bit vague. One way to think about the difference between “soft” vs. “hard” trade protection, which is consistent with our approach in this project, is as the difference between directly observable measures that can be included explicitly in quantitative analysis vs. obstacles to trade that are not directly observable and for which data for the econometric analysis are not available.

1
25 pro-heights likely suggests looser height restrictions there.

# **6. The Influence of Urban and Rural Areas as Countries Develop**

We just found that upward construction and outward construction respectively account for 60%-65% and 40%-35% of international volume differences, with the contribution of upward construction almost entirely driven by low-rises. Since countries simultaneously urbanize as they develop, and since urban areas differ from rural areas in many dimensions, we investigate how construction patterns depend on urban status. We first show that low-rises and high-rises more strongly explain volume differences across world cities than across countries. This leads us to examine if cities drive international volume differences across countries. However, we find that rural areas and small towns, not cities, disproportionately influence the distribution of volume per capita at a global scale. Medium to small cities, not large cities, then dominate total urban volumes. More generally, we discuss how urbanization is not the primary factor underlying the building stock-income relation observed across countries. Finally, we use our city-level analysis to study the upward vs. outward specializations of selected urban systems and world megacities.

**6.1. International Volume Differences across Cities**

Our definition of _urban areas_ includes 12,831 cities above 50,000 people in 2015 as identified by GHS-UCDB (Florczyk et al., 2019). Therefore, in our analysis, _rural areas_ include small towns and villages below 50,000 people in 2015. Table 2 shows selected summary statistics for urban vs. rural areas. Comparing Tables 2 and 1, the volume per capita added by building upward makes a larger difference at the city level than at the country level. For example, upward building accounts for 55% of total city volumes as compared to 48% at the country level.

Similarly to what we found at the country level, city volume per capita increases with city incomes (Appx. Section C2.-C4. and Appx. Fig. C.3).<sup>6
29 standard MICS wealth index, and for the WHS we used all the questions on assets available in the WHS survey.

Averages are presented for each quintile of households; because the number of births and child deaths are not equal across household wealth quintiles, there are typically more children in the lower wealth quintiles. The poorest quintile thus contains 20 percent of households but typically more than 20 percent of children.

In addition to presenting the quintile means, the 2018 HEFPI database, like previous HEFPI databases, also includes the concentration index and its standard error (Kakwani et al. 1997). This captures the degree of inequality (by wealth) in each indicator. A negative value indicates, on average, higher values among the poor; a positive value indicates, on average, higher values among the better off. The minimum is -1, and the maximum is +1. The concentration index and its standard error were computed using individual-level data via the Stata module CONINDEX (O'Donnell et al. 2015, 2016), with per capita household consumption or income or the wealth index as the ranking variable; it is therefore not affected by the fact that the quintiles are quintiles of households. When quintiles were very small, the quintile mean is not included in the data set. A quintile-specific datapoint is excluded if the sample size in any quintile was less than the following thresholds: 250 per quintile for infant and child mortality estimates and 25 per quintile for all other indicators; this follows the practice of Gwatkin et al. (2007).

# <u>Data-quality checks</u>

On the health equity side of the HEFPI data set, our quality checks involve checking population and quintile specific rates from DHS and MICS against rates published by WHO’s Health Equity Monitor, MICS reports, DHS STATcompiler and World Bank’s ‘Health, Nutrition and Population Statistics by Wealth Quintile’ databank whenever our and the publishers’ indicator definitions align (e.g. for the infant mortality and diarrhea
**Figure 5. Affordability to build or improve a toilet by household income**

<!-- Start of picture text --> Affordability by Household Income<br>0 20 40 60<br>Total household income (mill Rp)<br>10<br>8<br>6<br>4<br>2<br>Estimated Cost/ Max Willing to Pay<br>0<br><!-- End of picture text --> The surveys also collected information on respondents’ level of satisfaction with their sanitation facility. One might expect treatment communities to become less satisfied with having no sanitation facility as a result of the program. The data show slightly more dissatisfaction among households without access to a toilet in treatment communities but the difference is small and statistically insignificant (29.2 percent in treatment communities versus 28.1 percent in control communities, _p=_ 0.74). Satisfaction with open defecation remains high: 55.6 percent of households that don’t have access to sanitation reported that they were satisfied (38.8 percent) or very satisfied (16.8 percent) with their sanitation. This reflects the custom of defecating in rivers, which is thought to be hygienic because the waste is washed away. East Java is rich in rivers and 117 of our 160 communities (73 percent) are within a 10-minute walk from a river. Note that rivers are generally not used as a source of drinking water in Indonesia. Hence, a greater awareness that diarrhea is caused by dirty drinking water is not necessarily inconsistent with high levels of satisfaction with defecating in rivers.

# _8.6 Obstacles to Latrine Construction_

Households that indicated they would like to build a latrine were asked what they consider to be the main obstacle to building one. Figure 6 presents the data from the endline survey. As reported in the baseline survey, the main obstacle was the cost of construction. TSSM’s supply20
Kouame, C. Y., B.B. Kalimili and F. Pirlea, “Many African Economies are Larger than Previously Estimated,” World Development Indicators, 2019. <u>http://datatopics.worldbank.org/world-development-indicators/stories/many-economies-inssa-larger-than-previously-thought.html</u>

- Kuznets, S., “Economic Growth and Income Inequality,” _American Economic Review,_ 45, 1–28, 1955.

Lakner, C. and B. Milanovic, “Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession,” _The World Bank Economic Review,_ 30, 203–32, 2016. doi:10.1093/wber/lhv039 Lakner, C., D.G. Mahler, M. Negre, and E.B. Prydz, “How Much Does Reducing Inequality Matter for Global Poverty?. _Policy Research Working Paper_ No. 8869, World Bank, Washington, DC, 2019. https://openknowledge.worldbank.org/handle/10986/31796

- Lind, J.T. and H. Mehlum, “With or Without U? The Appropriate Test for a U‐shaped Relationship,” _Oxford Bulletin of Economics and Statistics_ , 72, 109–118, 2010. doi: <u>10.1111/j.1468-0084.2009.00569.x</u>

- Milanovic, B., _Worlds Apart: Measuring International and Global Inequality_ , Princeton University Press, Princeton, NJ, 2005.

- Newhouse, D.L. and P. Vyas, “Estimating Poverty in India without Expenditure Data: A Surveyto-Survey Imputation Approach,” _Policy Research Working Paper_ No. 8878, World Bank, Washington, DC, 2019. <u>https://openknowledge.worldbank.org/handle/10986/31868</u>

Nolan, B., M. Roser and S. Thewissen, “GDP Per Capita Versus Median Household Income: What Gives Rise
Table 1: Evidence on Gender Effects on Household Expenditures

|Study|Methodology and<br>Data Source|Outcome<br>Variables|Measure of<br>Female<br>Resources|Effect of Increase in Female<br>Resources|
|---|---|---|---|---|
|Attanasio and<br>Lechene<br>(2002)|Randomized field<br>experiment,<br>PROGRESA,<br>Mexico|Expenditure<br>shares|Woman's<br>income share|Share of expenditure on<br>children's clothing<br>increases, alcohol<br>decreases. In some<br>specifications, the share of<br>expenditures on food<br>decreases.|
|Case and<br>Deaton (1998)|Natural experiment,<br>pensions in South<br>Africa|Expenditure<br>shares|Female versus<br>male pension<br>recipients|In female-headed<br>households, smaller<br>expenditures on alcohol,<br>tobacco, and transportation.|
|Doss (2006)|Cross-sectional data,<br>Ghana|Expenditure<br>shares|Woman's asset<br>holdings|Share of expenditure on<br>education and food<br>increases, and alcohol and<br>medical expenditure share<br>decreases.|
|Duflo and<br>Udry (2004)|Natural experiment<br>using variation in<br>rainfall, Cote d'Ivoire|Expenditure<br>shares and<br>education|Women's<br>income share,<br>instrumented<br>by rainfall|Expenditures on food<br>increase. Mixed evidence<br>on education.|
|Gitter and<br>Barham<br>(2008)|Cross-sectional data<br>and field experiment,
<!-- Start of picture text --> 600<br>Vietnam<br>500<br>Brazil<br>400<br>Tanzania<br>India<br>300<br>Nigeria<br>Ghana<br>200<br>4 6 8 10 12 14<br>Expected Years of School<br>FIGURE II<br>Schooling versus Learning<br>Learning<br><!-- End of picture text --> _Notes:_ We compare the average country learning in the latest available year from our database to expected years of schooling.

_Source:_ Expected years of schooling are from the World Bank Human Capital Index based on data compiled by UNESCO; average learning outcomes are from our database. Both measures take the latest data available.

Figure III explores the contrast between changes in schooling and learning over time. We measure schooling using primary school enrollment rates. We compare this to our measure of learning in primary school for the years 2000-2015. We use data this period since it has the most overlap of schooling and learning measures. We restrict our comparison to countries with at least two data points over this time period for both enrollment and learning data in primary school to maximize comparability and minimize bias due to changing country coverage over the time period.

We see a clear trend towards increased schooling, while learning progress is inconsistent and in some cases stagnated. We observe limited learning progress even in regions where enrollments are relatively constant, such as Latin America and the Caribbean (LAC). We explicitly condition average learning on enrollment across countries and over time using multivariate regression and show results in Figure IV. We find learning has stagnated on average, even when conditioned on enrollment and when including country-fixed effects. Together, these data reveal a striking human capital gap: students are increasingly in school but learning progress is inconsistent and limited. This trend has been referred to as ‘the learning crisis’ (UNESCO 2017; World Bank 2018
1, _†_ p _<_ 0.15.

Table D.18. Inequality of Income at the Canton Level

|||||_Real Per Capita Income_|||
|---|---|---|---|---|---|---|
||Logarithm|Level|Gini Index|Interquartile Range<br>(p75-p25)|Percentile Range<br>(p90-p10)|Percentile Range<br>(p90-p50)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Guerrilla control|-0.223***|-39.08**|0.0119|0.0217|1.490|0.0366|
||(0.0682)|(15.25)|(0.0163)|(0.193)|(1.256)|(0.284)|
|Observations|542|542|542|542|542|542|
|Bandwidth (Km)|3.082|3.082|3.082|3.082|3.082|3.082|
|Dependent mean|5.330|266.8|0.320|2.450|5.240|2.360|

_Note:_ The table presents the results of equation 1 for the real per capita income taken from the Household Surveys (2012 to 2018). Each Column represents a different measure of inequality using the real per capita income. Column 4 report the interquartile range, calculated as the difference of the per capita income in percentile 75 minus the per capita income in percentile 25 for each canton. Column 5 shows the percentile range of the difference between percentile 90 and 10 for each canton. Column 6 reports the percentile range of the difference between percentile 90 and 50 for each canton.
education sector according to the Presidential Economic Reforms Program for 2010-2014 „Prosperous society, competitive economy, effective government‟<sup>4</sup> . Also the „launch of optimization‟ of the school network is envisaged by the Ukraine‟s Budget Declaration for 2011 which was approved on April 19, 2010. Schools are planned to be closed if they:

- have less than 10 students and only offer grades 1-4 (level I schools);

- have less than 40 students and only offer grades 1-9 (level I-II schools);

- have less than 100 students and offer all grades (1-11/12, level I-III schools)<sup>5</sup> .

School size is thus used as the key decision criterion to close schools. Given that pupils of closed schools will be reallocated to other schools, the remaining schools will see an increase in size and most likely will have the size of their classes increase. Also the Budget Declaration 2011 set to revise the school norms that influence the amount and type of staff to be hired.

In this paper, we attempt to study what the impact of school size and class size are on the performance of schools in Ukraine, using data on educational performance from Ukraine‟s Independent External Test and data on educational inputs from the Ukrainian Ministry of Education, Youth, Sports and Science. The paper is intended as a key input in the discussions around the optimization or consolidation of the network of schools in Ukraine that has been ongoing for some time now.

In Section II, we first review the academic literature on the determinants of educational performance with a particular emphasis on the evidence that links class size and school size to educational outcomes. Section III portrays a picture of Ukraine‟s educational system, again with an emphasis on size of schools and classes. Section IV provides a detailed description of the data used in this study and how the database was assembled. It is the core of the paper and presents our estimates of class size and school
2006), Bardhan (2010), Kotwal et al. (2011)).<sup>10</sup> The goal of this paper is to analyze the trends in and levels and patterns of educational mobility over

> guardian‘ of Sahara Group, $ 250,000 was spent on candles alone (Srivastava, S, BBC online, February 11, 2004)! The popular perception that rural areas in India have been largely left out of the recent economic growth is, at least partly, shaped by the reports of farmers‘ suicides, among other things.

> 6 Higher inequality of opportunity is likely to lead to a higher cross-sectional inequality (Atkinson (1981)).  Many observers believe that inequality in India reflects inequality in opportunity. For example, Basu (2008) comments that ―A certain amount of inequality may be essential to mitigate poverty....But the extent of Inequality in India seems to be well above that‖.

> 7 In an immobile society, many high ability children from poor families may not be able to go to school and thus fail to realize their productive potential. High inequality can also lead to political instability.

> 8 For a broader discussion on the importance of equity in economic opportunity for development, see Equity and Development (World Development Report (2006)).

> 9 Reliable data on children‘s and parents‘ income over the life cycle are not available in a developing country such as India.  As emphasized in the recent literature, one needs good quality income data over a number of years at appropriate phase of the lifecycle to tackle the attenuation bias in the estimated intergenerational correlation in income (Solon (1999), Mazumder (2003)). The analysis of intergenerational persistence in income in India is also complicated by the fact that a majority of population especially during parent‘s generation were engaged in family farming as self-employed workers making it difficult to attribute income to individual members.

> 10 This is in contrast to the Chinese experience
44 and comparability. In the 2018 HEFPI database, we rely on several data harmonization efforts (see Table 6):

- <u>LIS database. The LIS acquires data sets with income, wealth, employment, and</u> demographic data from many high- and middle-income countries, harmonizes them to enable cross-national comparisons, and makes them publicly available in two databases, the LIS database and the Luxembourg Wealth Study database. For the 2018 HEFPI data set, we use all available datapoints from the LIS database.

- <u>World Bank regional harmonized databases. Regional teams at the World Bank are also</u> working to produce adaptations of raw country level survey data sets using regionally harmonized definitions and aggregation methods (e.g. the ECAPOV data sets for Europe and Central Asia, the EAPPOV data set for East Asia and Pacific, the SHIP for Sub-Saharan Africa, the MNAPOV for Middle East and North Africa, the SEDLAC for Latin America and the Caribbean, and the SARMD for South Asia). These harmonized data sets are used for the global monitoring of poverty by the World Bank. We also use these harmonized data sets whenever possible.

# _Supplemental indicators used in computing absolute impoverishment_

When we measure impoverishment using the absolute $1.90-a-day and $3.20-a-day poverty lines in 2011 PPP dollars, we have to take into account that the survey values are in local currency units (LCUs), and that they refer to the year of the survey which is not necessarily 2011, the year to which the PPP conversion factors that we use refer. The PPP conversion factor tells us the number of LCUs that would have been needed in 2011 to buy the same amounts of goods and services in the country as one US dollar would have bought in the USA in 2011. Multiplying the conversion factor by 1.9 or 3.2 gives us the amount of LCUs that would have been needed in 2011 to buy the same amount of goods and services in the country as US