rafmacalaba/datause-encoder-data huggingface dataset:
Panel C. Headline CPI (Percent)

||1970-2016|1970-85|1986-2000|2001-16|
|---|---|---|---|---|
|Factor||All cou|ntries||
|Global|15.7<br>(22.9,6.2 - 36.5)|18.7<br>(28.2,10.3 - 42.4)|4.0<br>(5.8,1.9 - 7.5)|32.8<br>(30.8,19.5 - 44.7)|
|Group|9.1<br>(12.8, 3.4 - 21.4)|8.6<br>(14.5, 3.8- 22.9)|5.7<br>(9.5, 4.8 - 10.3)|19.1<br>(20.2, 10.3 - 24.7)|
|Global + Group|34.1<br>(35.7,25.4 - 44.6)|42.9<br>(42.7,25.0 - 56.7)|12.1<br>(15.3,8.1 - 19.0)|55.2<br>(51.2,47.7 - 60.8)|
|Factor||Advanced|economies||
|Global|21.4<br>(23.7, 11.0 - 36.6)|20.0<br>(27.3, 8.7 - 42.6)|4.3<br>(6.2, 1.9 - 7.6)|37.0<br>(35.0, 28.8 - 44.9)|
|Group|11.5<br>(14.0, 4.1 - 24.1)|19.8<br>(18.9, 4.3 - 29.7)|5.5<br>(6.4, 4.7 - 7.0)|17.3<br>(16.6, 9.1 - 19.9)|
|Global + Group|35.9<br>(37.7,31.1 - 44.8)|45.1<br>(46.1,28.3 - 59.1)|12.0<br>(12.6,7.8 - 14.6)|54.4<br>(52.8,48.5 - 58.3)|
|Factor||EM|DEs||
|Global|11.6<br>(21.4, 3.4 - 36.3)|17.4<br>(30.0, 13.8 - 41.9)|3.2<br>(5.1, 1.0 - 5.2)|18.1<br>(21.0, 3.4 - 24.7)|
|Group|7.7<br>(10.4, 0.9 - 11.8)|6.8<br>(6.2, 3.2 - 8.6)|10.2<br>(15.5, 5.7 - 19.2)|25.6<br>(30.4, 12.4 - 42.6)|
|Global + Group|25.6<br>(31.9,16.7 -43.9)|26.2<br>(36.2,23.0 - 45.1)|12.6<br>(20.6,10.1 - 27.8)|59.2<br>(48.3,28.8 - 64.7)|

Notes: The contribution of global and group inflation factors is estimated over the three subsample periods with the dynamic factor model (two-factor model) for each of the five different inflation measures: import prices, PPI, headline, GDP deflator, and core CPI. The data set includes 38 countries (25 advanced economies and 13 EMDEs) except import prices for 21 countries (17 advanced economies and 4 EMDEs). The first argument in the first row indicates the unweighted median across countries. The first argument in the second row (in parentheses) is the mean variance share across countries. The second and third arguments in the second row (in parentheses) indicate the interquartile range (25th and 75th percentiles) of variance shares. The results differ from Tables 2 and Table 3 because of the smaller sample size to match the sample with available data for other measures of inflation. CPI = consumer price index; EMDEs = emerging market and developing economies; GDP = gross domestic product; PPI = producer price index.

30
In line with this, Lazear (1999) shows that smaller classes substitute for better discipline – more specific, students of Catholic schools have better school achievements in larger classes. Babcock and Betts (2009) demonstrate that low-effort students lose more from class size increases as they require more attention of their teachers. Wößmann and West (2002), using a sample of 18 developed countries, find that for most countries class size has an insignificant impact on student performance. However, in Iceland and Greece reduced class size does seem to positively influence TiMSS scores, which they explain by the lower quality of teachers in these countries – teachers there have relatively lower salaries and qualifications.

The arguments in favor of **school size** are similar to those in favor of small classes – smaller schools are more „personalized‟, can have more „adaptive‟ pedagogy and will involve parents more. On the other hand, bigger schools could exploit economies of scale and hence provide better quality for a given level of inputs.

Garrett et al (2004) provide an extensive review of school size literature based on the United States (US) and the United Kingdom (UK) data. Their review suggests an inverted-U relation between school size and student exam scores, though larger schools are typically found to be more cost-efficient. They also point out that teacher and student perceptions of school climate decline<sup>7</sup> and some kinds of violent behavior<sup>8</sup> may increase with school size.

More recently, focusing on the US and Canada, Leithwood and Jantzi (2009) conclude that smaller schools mostly benefit younger students and those with a disadvantaged social and economic background. They state that “elementary schools with large proportions of such students should be limited in size to not more than about 300 students; those serving economically and socially heterogeneous or relatively advantaged students should be limited in size to about 500 students. Secondary schools serving exclusively or largely diverse and/or disadvantaged students should be limited in size to about 600 students or fewer, while those secondary schools serving economically and socially heterogeneous or relatively advantaged students should be limited in size to about 1,000 students (p.1).”

> 7 E.g. parental involvement into school matters decline with school size – Walsh (2010).

> 8 For example, Leung and Ferris (2008) use Canadian data to show that that a student attending a school with more than 2000 students is 22 percent more likely to engage in an act of serious violence.

6
# **4.2. Land fragmentation**

Agricultural holdings do not normally comprise a single compact block but are made up of a number of parcels scattered across the village. The fragmentation<sup>32</sup> of operational holdings into multiple plots is commonly perceived to be a serious constraint on agricultural productivity. High direct and opportunity costs in cultivation are frequently ascribed to fragmentation, including: the time and energy expended in moving labor, draft animals, seed, manure and irrigation water from one plot to another, and bringing harvested crops to a common point; supervision of labor; increased expenses of irrigation and drainage; difficulty of access to scattered plots; and loss of land in boundaries (Mearns 1998).

State governments have attempted to control fragmentation through legislative means and by encouraging land consolidation, or making it compulsory. This section examines the extent of fragmentation in Orissa, legal provisions to control it, and progress achieved and problems encountered in the administrative process of land consolidation.

## _4.2.1 Extent offragmentation_

There is widespread consensus that the rate of fragmnentation of land holdings in Orissa is very high, though the evidence is patchy. This is because there is no systematic compilation of data within the state on the number of parcels per operational holding. These data is collected at the time of consolidation operations, and may even be sent to the Director, Consolidation at Cuttack. But they are rarely aggregated so as to form a comprehensive view of the extent of fragmentation, regional variation, or of the relative success of consolidation. Thus, much of this discussion is based on village studies, National Sample Survey (NSS) data, and informed estimates.

The trend in fragmentation over time and across land sizes is shown in Table 9. In 1961-62 there were an average of 6.4 fragments (or parcels) per operational holding across all size classes. By 1981-82, this had fallen to 5 parcels per holding. The average area per parcel has also declined during this period, though marginally, from 0.31 to 0.29 ha. It is evident that the rate of fragmentation is higher in medium and large holdings.

In the absence of more disaggregated data, it is difficult to form an accurate view of regional variation in fragmentation. Knowledgeable sources claim that fragmentation is a more serious problem in irrigated and coastal areas than in western Orissa. However, and we discuss below, there is considerable resistance to land consolidation in western Orissa.

32 Fragmentation is defined as the number of non-contiguous plots per operational or ownership holding within a village.

42
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equal to the 2010 GDP per capita from the World Development Indicators. It should be noted that this is only an approximation to more proper international comparisons (see e.g. Deaton and Dupriez, 2011) and that the HIT database is not the World Bank Group’s official poverty data, which can be found in PovcalNet.<sup>2</sup> Figure 4 pools all the data and shows how the aggregate spending categories identified in our data vary with (the log of) household income per capita (in constant 2010 USD). To start, households spend a large share of their income on food and agricultural products. Across all countries, the average household in our sample spends 44.7% of its income on food items, 17.4% on manufacturing goods, and 15.1% on services. Another 16.9% of expenditure is accounted for by goods households have produced themselves, which highlights the importance of dealing with home consumption in the analysis. This also implies that the total expenditure share in agriculture is 61.6%. While there is huge heterogeneity in spending patterns both across and within countries, the graph shows that, as households get richer, the share of income spent on food decreases, especially for the richest households. Spending on manufacturing goods, services, and other goods first declines with income but then increases sharply. The opposite happens with home consumption. The implication is that the tariff burden on different households will vary as a result of these different consumption patterns.

Agriculture is the most important source of income, accounting for 38.5% of total household income on average. This total comprises a 20.9% share of production for household consumption and 17.6% of sales of agricultural products in the market. Labor income represents 29.6% of household income, on average, and non-farm enterprises account for another 12.7%. Figure 5 shows how these aggregate income shares vary by income (in constant 2010 USD), demonstrating that for the very poorest households in our sample, agricultural income and home production tend to be the most important sources of earnings. As households get richer, wage income becomes a progressively more important source of

> 2Note also that the HIT consumption aggregates may differ from those in PovcalNet because the methodology used to calculate aggregate consumption differs from that of PovcalNet. To give a few examples, the HIT consumption aggregate includes expenditures on durable goods, while PovcalNet aggregates typically aim to capture the rental value of durables. As another example, the HIT data include all health expenditures, whereas health expenditures are not uniformly treated in PovcalNet. Note also that we are scaling up average expenditure per capita to match the national accounts estimates. Specifically, we set mean expenditure per capita equal to GDP per capita in constant 2010 US dollars.

4
their productivity is independent of the number of facilities in which they are employed. The existence of ownership advantages is reconfirmed in the recent theory of heterogeneous firms, which suggests that only the most productive establishments can afford the extra cost of setting up production facilities in a foreign country and predicts that multinationals come from the upper part of the productivity distribution of firms in their country of origin (Helpman, Melitz, and Yeaple 2004).

Consistent with the existence of ownership advantages, the data confirm that multinationals are heavily involved in the creation of new knowledge through their engagement in research and development (R&D) activities. In 2002, 700 firms, 98 percent of which were multinational corporations, accounted for 46 percent of the world’s total R&D expenditure and 69 percent of the world’s business R&D. Given that there existed approximately 70,000 multinational corporations in the world at that time, this is a conservative estimate. In 2003, the gross domestic expenditure on R&D of 3.84 billion dollars by the eight new members of the European Union<sup>8</sup> was equal to approximately half of the R&D expenditure of Ford Motor (6.84 billion), Pfizer (6.5 billion), DaimlerChrysler (6.4 billion) and Siemens (6.3 billion) during the same year and was comparable to the R&D budget of Intel (3.98 billion), Sony (3.77 billion), Honda and Ericsson (3.72 billion each) (UNCTAD 2005). More than 80 percent of global royalty payments for international transfers of technology in 1995 were made from foreign subsidiaries to their parent firms (UNCTAD 1997).

12
**<u>Annex 3: FDI Spillovers and Firm Performance, by Region and Sector</u>**

|VARIABLES|||World Ba|nk Regions|||Economic|Sectors|
|---|---|---|---|---|---|---|---|---|
||EAP|ECA|LAC|MENA|SA|SSA|Manufacturing|Services|
|Linkages Channel|-0.123|0.299|0.009|-0.092|0.349|-0.059|0.092|-0.111*|
||(0.103)|(0.265)|(0.108)|(0.164)|(0.647)|(0.098)|(0.108)|(0.064)|
|Demonstration Channel|0.047<br>(0.050)|-0.051<br>(0.047)|0.042<br>(0.036)|-0.003<br>(0.043)|-0.086<br>(0.101)|-0.001<br>(0.034)|-0.003<br>(0.023)|-0.009<br>(0.037)|
|High-growth firm|0.238*** <br>(0.067)|0.171***<br>(0.065)|0.212***<br>(0.056)|0.241*** <br>(0.066)|0.127*** <br>(0.025)|0.294***<br>(0.071)|0.181***<br>(0.021)|0.235***<br>(0.061)|
|X Linkages Channel|0.814***|-0.202|0.491|2.342***|0.764|-0.207|0.570*|0.581**|
||(0.309)|(0.892)|(0.320)|(0.700)|(2.407)|(0.439)|(0.307)|(0.284)|
|X Demonstration Channel|-0.076|0.427**|0.042|0.019|0.163|0.059|0.078|0.211|
||(0.118)|(0.192)|(0.127)|(0.208)|(0.201)|(0.147)|(0.077)|(0.145)|
|Log Age|-0.056*** <br>(0.015)|-0.086*** <br>(0.016)|-0.059*** <br>(0.011)|-0.059*** <br>(0.012)|-0.045*** <br>(0.006)|-0.088***<br>(0.017)|-0.059***<br>(0.006)|-0.069***<br>(0.012)|
|Exporter|-0.000<br>(0.023)|0.017<br>(0.029)|-0.065*** <br>(0.019)|0.015<br>(0.021)|-0.009<br>(0.014)|0.064<br>(0.045)|-0.013<br>(0.010)|0.083*<br>(0.043)|
|Log Labor Productivity|0.062*** <br>(0.009)|0.086***<br>(0.011)|0.107***<br>(0.007)|0.099*** <br>(0.009)|0.038*** <br>(0.006)|0.122***<br>(0.011)|0.075***<br>(0.005)|0.108***<br>(0.012)|
|Constant|-0.579*** <br>(0.100)|-0.718*** <br>(0.171)|-0.679*** <br>(0.089)|-0.983*** <br>(0.128)|0.062<br>(0.104)|-0.641***<br>(0.131)|-0.444***<br>(0.078)|-0.734***<br>(0.119)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|5,876|2,749|5,557|4,086|9,155|5,882|26,398|6,893|
|R-squared|0.103|0.171|0.116|0.306|0.050|0.184|0.175|0.190|

Note: The dependent variable is total sales. The sample excludes foreign firms. Countries are allocated into six world regions according to the World Bank classification: MENA: Middle East and North Africa; LAC: Latin America and Caribbean; SSA: Sub-Saharan Africa; EAP: East Asia and Pacific; ECA: Europe and Central Asia; and SA: South Asia. Standard errors (in parentheses) are clustered at the country-sector level. *** p<0.01, ** p<0.05, * p<0.1 Source: Author’s computation using the WBES.

29
# 1. Introduction

Electric vehicles and a clean electricity sector offer a promising path to reduce greenhouse gas (GHG) and local air pollution from light-duty vehicles. Passenger cars account for 11 percent of global GHG emissions and 45 percent of global GHG emissions from transportation<sup>2</sup> and are one of the main factors causing poor air quality in major cities (Gwilliam, Kojima, and Johnson 2004; Kojima and Lovei 2001). Although GHG emissions from light-duty vehicles are declining gradually in some developed economies, most countries struggle to dramatically reduce these vehicles’ emissions and achieve international climate objectives.

Designing a cost-effective vehicle policy is particularly important in low and middle-income countries, which will account for most if not all growth of energy and transportation demand in the following decades (Wolfram, Shelef, and Gertler 2012). Non-OECD emissions are expected to increase steadily over the coming decades, absent major technological, market, or policy changes (Bazilian et al. 2019). Apart from China, electric vehicle markets in developing countries are nascent, lagging those in Europe and the United States (throughout the paper, electric vehicles refer to all-electric such as the Tesla Model 3 and plug-in hybrids such as the Chevrolet Volt). Developing countries have a variety of policies from which to choose, including fuel and vehicle taxes and subsidies, trade policy instruments, and regulatory policies, such as, e.g., emissions standards. Yet, despite a growing literature on electric vehicle policy in highincome countries (e.g., Springel 2021), there is little research evaluating these policy options for low and middle-income countries.

For several reasons, electric vehicle policies are likely to have different welfare effects in low and middle-income countries than in high-income economies. First, typical new vehicle consumers in low and middle-income countries have lower disposable incomes. As a result, they may be more responsive to price incentives and fuel cost savings of electric vehicles (Bansal et al. 2021) while having a lower willingness to pay for the relatively more expensive electric cars, making incentives less effective. Second, these countries often have limited charging infrastructure, and electricity may be expensive and unreliable, which may reduce the demand for electric vehicles (Aznar et al. 2021). Finally, many new vehicle markets in low and middle-

> 2 Authors’ calculations based on https://www.iea.org/data-and-statistics/charts/transport-sector-co2-emissions-by- <u>mode-in-the-sustainable-development-scenario-2000-2030</u> [last accessed: April 2022].

2
Table 15 indicates that in the case of Indonesia, the gap to be explained is positive 140 points. Endowments and coefficients on ED WEALTH account for positive 19% and positive 23% of the gap. The ED WEALTH variables explain more as was the case for Colombia. When we look at the WEALTH related variables, we see that the endowments of Indonesia would not have made such a big impact, pointing to the fact that Indonesia is closest to Vietnam amongst the Dev7 countries on per capita income. Of a similar magnitude like Colombia, we can see that the WEALTH coefficients of Vietnam would set back Indonesian students by negative 75%.

Thailand is the country with the lowest difference in mathematics score, only positive 73 points behind Vietnam. The OB decomposition for Thailand indicates a negative 6% contribution of endowments on EDWEALTH and a positive 25% contribution on coefficients. On the WEALTH set of variables, the contribution for Thailand was negative 11% on endowments and negative 177 % on coefficients. The results from Shanghai, added to the mix as a counterpoint to Dev7 countries, do not seem to provide much additional insight. The biggest exlanation of difference between Vietnam and Shanghai would the coefficients on ED WEALTH and WEALTH, with 111% and 91% respectively.<sup>11</sup> Overall, the OB decompositions support the previous findings. In five of the seven Dev7 countries, the ED WEALTH variables show a positive contribution on endowments - meaning that if the other countries have had Vietnam’s endowments on ED WEALTH variables, their performance would have been better. On the coefficients side of ED WEALTH, we see a different picture - the contributions range from +64% for Albania to -52% for Tunisia, with other countries ranged in between. The two Asian countries (Indonesia and Thailand) have similar contributions of 23% and 25%. The predictable WEALTH set of decompositions is of less interest to us as in most cases there are small effects on endowments and large negative on With cross-section data in a non-experimental context, it is very difficult to make definitive conclusions, and only tentative ones can be made that hint at some answers. The findings on Dev7 countries indicate that it is possible that a number of advantages that Vietnam enjoys, depicted in ED WEALTH, can only function effectively in combination. One way to consider this is through a cultural lens - meaning that there is something specific to Vietnamese culture, that enables Vietnam to benefit from hard working students and teachers, with the guidance of committed and involved parents, even in cities and small towns.

> 11For all other countries we used the respective country as the basis; for Shanghai, Vietnam is the base country in terms of equation (5).

36
across countries. The measure of labor productivity based on this measure of real value added is henceforth designated as ‘WIIW labor productivity’. While this approach is the only feasible for the WIIW database - which is of interest given its coverage of South Eastern European countries and Ukraine - it suffers from the drawback of not correcting for sub-sector specificity in domestic and international prices.

The KLEMS database includes output price deflators and production-based PPPs that are sub-sector specific, therefore we transform each services sub-sector’s nominal gross value added in domestic currency into real value added in PPP euros using subsector specific output price deflators and 1997 sub-sector specific PPPs, applying the formula shown in Appendix B.<sup>11</sup> The measure of labor productivity based on this measure of real value added is henceforth designated as ‘KLEMS labor productivity’.

Figure 5 gives a general perspective on the heterogeneity in services performance within the ECA region by showing unweighted (Panel A) and weighted (Panel B) averages of WIIW labor productivity in each country taken across all services sub-sectors in 1997-2000 and in 2001-2004. Weighted averages use as weights each sub-sector’s share in services value added thus accounting for possible compositional effects in the unweighted averages (i.e., the fact that some sub-sectors are more important in certain countries, as shown in Section 2). Three messages emerge from Figure 5. First, average productivity in services varies substantially across countries. A productivity divide separates EU-5 countries from South Eastern European countries and Ukraine, with the latter exhibiting lower average productivity.<sup>12</sup> Second, weighted averages of WIIW labor productivity are higher than the corresponding unweighted averages in all countries. This finding indicates that more productive sub-sectors account for a larger share of services value added and hence suggests efficiency in the allocation of value added across subsectors. Third, both unweighted and weighted averages of WIIW labor productivity increase between 1997 and 2004 in all countries.

> 11 Timmer et al (2007) describe in detail the computation of production-based sector-specific PPPs. However, the authors argue that expenditure-based PPPs are a reasonable approximation for sub-sectors which produce mainly for final consumption and whose products are hardly internationally traded. 12 However, Croatia is an exception in that its unweighted average labor productivity is higher than that of Poland.

13