rafmacalaba/datause-encoder-data huggingface dataset:
- Burke, P., and Z. Csereklyei. 2016. “Understanding the Energy‐GDP Elasticity: A Sectoral Approach.” _Energy Economics_ 58 (8): 199‐210.

- Baumeister, C., and G. Peersman. 2013. “The Role of Time‐Varying Price Elasticities in Accounting for Volatility Changes in the Crude Oil Market.” _Journal of Applied Econo‐ metrics_ 28(7): 1087‐1109.

- Baumeister, C., and J. Hamilton. 2019. “Structural Interpretation of Vector Autoregres‐ sions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks.” _American Economic Review_ 109(5): 1873‐1910.

- Campbell, C.J., and J. H. Laherrère. 1988. “The End of Cheap Oil _.” Scientific American_ March: 78‐84.

- Canas, A., P. Ferrão, and P. Conceico. 2003. A New Environmental Kuznets Curve? Rela‐ tionship between Direct Material Input and Income per Capita: Evidence from Indus‐ trialized Countries.” _Ecological Economics_ 46(2): 217‐229.

- Chan, H.L., and S.K. Lee. 1997. “Modelling and Forecasting the Demand for Coal in China.” _Energy Economics_ 19: 271‐287.

- Chambers, R.G. 1988. _Applied Production Analysis: A Dual Approach._ Cambridge University Press, New York.

- Chandrasekhar, S. 1989. “Cartel in a Can: The Financial Collapse of the International Tin Council.” _Northwestern Journal of International Law and Business_ 10(2): 308‐332.

- Charfeddine, L., and K. Barkat. 2020. “Short‐ and Long‐run Asymmetric Effect of Oil Prices and Oil and Gs Revenues on the Real GDP and Economic Diversification in Oil‐ dependent Economy.” _Energy Economics_ , <u>https://www.sciencedirect.com/science/article/pii/S0140988320300190</u>

- Christensen, B.V. 2016. “Challenges of Low Commodity Prices in Africa.” BIS Paper no. 87. Bank of International Settlements, Basel, Switzerland.

- Clark, C. 1940. _The Conditions of Economic Progress._ Macmillan, London.

- Cleveland, C.J., and M. Ruth. 1998. “Indicators of Dematerialization and the Materials Intensity of Use.” _Journal of Industrial Ecology_ 2(3): 15‐50.

- Considine, T.J. 1987. “Understanding Trends in Metals Demand.” _Materials and Society_ 11(3): 349‐370.

- Cooper, R.N., and R.Z. Lawrence. 1975. “The 1972‐75 Commodity Boom.” _Brookings Pa‐ pers on Economic Activity_ 3: 671‐723.

- Costantini, V., and C. Martini. 2010. “The Causality Between Energy Consumption and Economic Growth: A Multi‐Sectoral Analysis Using Non‐stationary Cointegrated Panel Data.” _Energy Economics_ 32 (3): 591‐603.

- Crompton, P. 2015. “Explaining Variation in Steel Consumption in the OECD.” _Resources Policy_ 45: 239‐246.

- Crowson, P. 2017. “Intensity of Use Reexamined.” _Mineral Economics_ 31 (1‐2): 61‐70.

_— 25 —_
|Country Group<br>Estimations<br>**Variables**<br>This table presents estimations of annual in<br>change in the CRB general commodity ind<br>current prices from a year before. The last <br>fixed effects. P-values from robust standard<br>WEO, Bloomberg, GEM World Bank.|EM<br>Panel<br>**IP Growth**<br>**Real decoupli**<br>dustrial production<br>ex. G7 growth is a <br> four rows of the ta<br>errors are presente|EM<br>Panel<br>**IP Growth**<br>**ng for Indust**<br>growth (m/m<br> weighted aver<br>ble present a jo<br>d in italics. *,|EM<br>Panel<br>**IP Growth**<br>**Tab**<br>**rial Produc**<br>data) of EM<br>age of each i<br>int test of i_<br>** and *** de|AM<br>Panel<br> <br>**IP Growth**<br>**le 4**<br>**tion (AM and**<br>and non-G7 AM<br>ndividual countr<br>growth+i_growt<br>notes significan|AM<br>Panel<br>**IP Growth**<br>**EM, panel e**<br>against G7 and<br>y belonging to <br>h*late=0 where<br>ce at the 10, 5|AM<br>Panel<br>**IP Growt**<br>**stimations)**<br>China grow<br> that group. G<br> i represents <br>and 1% level|OLS<br>**h**<br>**CRB Commodity Index Growth**<br>th. CRB Commodity Index Growth is the log<br>rowth is weighted by PPP-measured GDP in<br>G7 or China. Panel estimations have country<br>respectively. Source: IMF IFS Statistics, IMF|
|---|---|---|---|---|---|---|---|
|G7 IP Growth|0.425***|1.069***|0.949***|1.029***|0.810***|0.756***|0.929***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|G7 IP Growth*Late|0.510***|-0.327***|-0.296***|-0.290***|0.000|0.015||
||_0.000_|_0.000_|_0.001_|_0.000_|_0.997_|_0.845_||
|China IP Growth||0.308***|0.213***||-0.118***|-0.159***|1.909***|
|||_0.000_|_0.000_||_0.000_|_0.000_|_0.000_|
|China IP Growth*Late||0.211***|0.148***||-0.072|-0.100***||
|||_0.000_|_0.000_||_0.000_|_0.000_||
|CRB Commodity Index Growth|||0.077***|||0.034***||
||||_0.000_|||_0.010_||
|Constant|0.031***|-0.034***|-0.018***|0.008***|0.032***|0.039***|-0.227***|
||_0.000_|_0.000_|_0.003_|_0.000_|_0.000_|_0.000_|_0.000_|
|Observations|4360|4360|4360|2661|2661|2661|212|
|R-squared|0.350|0.384|0.388|0.465|0.472|0.473|0.552|
|G7 Growth+G7 Growth*Late|0.935***|0.742***|0.653***|0.739***|0.810***|0.771***||
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_||
|China Growth+China Growth*Late||0.519***|0.361***||-0.190***|-0.259***||
|||_0.000_|_0.000_||_0.000_|_0.000_||
In the balance of this paper, we will discuss six developments that are changing the industrial landscape in Japan. In this context, we explore the nature of the responses required of firms and in the concluding sections, examine some implications of the measures adopted for the Japanese economy as a whole.

# **Intensifying Competition**

There can be little doubt that the dismantling of trade barriers and the falling costs of transport and communication by integrating international markets are intensifying global competition.<sup>5</sup> But fewer impediments to trade are only one of the factors that has affected the tempo of market competition. Equally important is the codification and diffusion of production technologies, as well as the adoption of uniform standards with foreign direct investment (FDI) serving as an important channel, the accumulation of labor skills in many countries, and in the case of the burgeoning electronics sector, the dominance of digital technologies. All of these have lowered the entry barriers to firms in emerging economies and allowed them to quickly acquire the skills and manufacture products of the quality needed to penetrate global markets. In several countries, these developments have been abetted by the deepening of financial systems that have provided entrepreneurs with the funds from local and foreign sources to harness the available technologies and invest in production facilities. Thus, East Asian economies for example, have been gripped by an industrial revolution that has enabled them to overtake

> investment would support faster productivity growth along with a parallel shift in labor to subsectors with potential for such growth.

> 5 This process of globalization is described in World Bank (1999), Wolf (2004), Bhagwati (2004),

> O’Rourke and Williamson (1999) and  Micklethwait and Wooldridge (2000).

4
Thus if asset j has bjl = 1.0 and b,2 = 0.5, then Pi. = 39.20. Now let's assume that the market is opened to global investors and asset prices are determined by the preferences of a globally = diversified representative consumer. The new parameters are p = 0.98, E,(61.,u'(c,)/u'(c) -0.10 and E,(bz,u'(ct,)/u'(cj) = 0.0. For example, the covariance between the "domestic" factor and the global representative investor's marginal utility might be zero since the small economy's domestic factor risk is diversifiable across economies. The unexpected shift from a segmented to an integrated economy leads to a change in price from $39.20 to $44.10 an immediate return of 12.5%. If the parameter p were to simultaneously change from 0.98 to 0.99 the price of asset j would jump to $89.10, an immediate return of 127%.

While the numerical results are clearly dependent on the numbers picked for the example, the fact still remains that shifts across pricing regimes are likely to cause large measures of mispricing in the short run.

# _4._ **Data Sources and Summary Statistics**

Historical monthly equity returns data for individual stocks trading in twenty emerging markets are from the _Emerging Markets Database_ provided by the International Finance Corporation. The countries covered by the database are Argentina, Brazil, Chile, Colombia, Greece, Indonesia, India, Jordan, Korea, Malaysia, Mexico, Nigeria, Pakistan, Philippines, Portugal, Taiwan, Thailand, Turkey, Venezuela, and Zimbabwe. The set of emerging markets are geographically diverse as well as diverse in the severity of capital controls.

The sample of developed equity markets includes stocks from Australia, Japan, the United Kingdom, and the United States. A summary of the developed markets equity data sources is presented in Table 1. The sample includes all assets traded on the Australian Stock Exchange, the 9
We also specifically analyze the differences in electricity usage patterns that exist between various industries in India and those that are especially intensive in energy. World Bank and Government of India (2011) broadly classify the industrial sector as energy-intensive industries (e.g., iron and steel, fertilizer, petroleum refining, cement, aluminum, and pulp and paper) and light industries (e.g., food processing, textiles, wood products, printing and publishing, and metal processing). The energy-intensive industries account for a bulk of the energy consumed in the sector. Our results mirror the findings in several studies that industry has recorded greater energy efficiency improvement since the late 1980s (e.g., Roy, 2007; Sengupta, 2012). Many factors account for this trend, including greater competition following the liberalization of the economy in the early 1990s, rising energy prices starting in the late 1990s, and the promotion of energy efficiency schemes since the introduction of the Energy Conservation Act in 2001 (World Bank, 2013). Nonetheless, various industries are inefficient by either national or international standards. Gupta and Ramprasad (2012) find that there is substantial potential for energy saving in energy intensive industries, ranging, for example, from 46%-88% for textiles, 43%-94% for paper and pulp, to 51%-92% for iron and steel.

With the background of these baseline estimates and trends, we next use empirical decomposition techniques to assess the degree to which the various spatial transformations noted above have influenced the aggregate energy consumption of the sector. Following the productivity growth decomposition work of Baily et al. (1992), Griliches and Regev (1995), and Foster et al. (2001), we decompose the observed changes in the aggregate electricity consumption per unit output from 1989 to 2010 and from 2000 to 2010. Decomposing the observed trends in aggregate electricity usage from 2000 onwards, we find that most reductions in usage came from lower usage in existing sites of activity (defined through state-industryurban/rural cells). The second biggest factor for promoting lower usage was the lower usage levels among fast-growing sectors. By contrast, spatial movements of manufacturing activity across India did not significantly reduce electricity consumption per unit of output and may have even increased it. This appears in part due to the split nature of the mobility, with organized and unorganized sectors migrating in opposite directions.

Given multiple challenges in India’s energy sector and limited resources, a better understanding of the spatial patterns of energy usage can help in defining a focused and targeted energy policy.

6
# **ABSTRACT**

This is first of six background papers which are intended to bring together some of the principal empirical results of research on trade policies and incentives in Indian agriculture which has been under way for a number of years. The background papers will provide details of the incentive indicators which have been estimated for thirteen major crops, as well as background data on India's role in the world production, consumption and trade of each of these commodities. Each paper also includes a description of the general methodology, and a research project bibliography which lists studies which were completed as part of the project or which were closely related to it. The background papers are intended as a reference point for a forthcoming book by Ashok Gulati and Garry Pursell entitled _Trade Policies and Incentives in Indian Agriculture._ The data and results generated in the research are much too extensive to be documented or even properly summarized in the book, so it was decided to make them available separately as background papers, so that researchers can check, correct or extend them. Eventually it is intended to make the data available in electronic form, perhaps on a CD-ROM.

The starting point for the research summarized in this background paper was an original study of incentives for sugar and sugarcane production by B. N. Goldar and Ashok Gulati, which covers the period 1981 to 1989 (see the research project bibliography).

v
Policy Research Working Paper 8175

# **Abstract**

Data from 919 household surveys conducted between 1960 and 2012, spanning 147 economies, are used to evaluate the relationship between rising life expectancy at birth and lifetime years of schooling for successive birth cohorts between 1905 and 1988. The study finds significant positive effects of increased life expectancy at birth on lifetime completed years of schooling in 95 percent of the surveys, with significant negative effects found in only 2.3 percent. Rising life expectancy at birth for a birth cohort has intergenerational benefits in that the cohort’s children’s schooling also increases. Rising life expectancy at birth since 1905 can explain 70 percent of the rising completed years of schooling for those birth cohorts.

This paper is a product of the Social Protection and Labor Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at cmontenegro@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
# **Table A3. Excises for passenger cars and other motor vehicles intended primarily for the transport of** **<u>people by car displacement and years of operation</u>**

|**Classification**<br>|**Name of the good**|**Term of**|**vehicle o**|**peration,**|**years**||||
|---|---|---|---|---|---|---|---|---|
|**code**||from 0<br>to 2|<br>from 3<br>to 4|from<br>5 to 6|7|8|9|10|
||**–**<br>**vehicles**<br>**with**<br>**internal**<br>**combustion engine with spark**<br>**ignition**<br>**with**<br>**reciprocating**<br>**piston, other:**||||||||
|**870321**|– – with engine cylinder capacity<br>not exceeding1000 cm3|9.56|10.00|10.23|10.45|11.50|12.19|12.89|
|**870322**|– – with a cylinder capacity of more<br>than 1000 cm3, but not more than<br>1500 cm3|12.23|12.67|12.90|13.12|14.49|15.17|16.33|
|**870323**|– with a cylinder capacity of more<br>than 1500 cm3, but not more than<br>2000 cm3|18.90|19.34|19.57|19.79|21.62|23.23|24.39|
|**870323**|– – with a cylinder capacity of more<br>than 2000 cm3, but not more than<br>3000 cm3|31.14|31.58|31.81|32.03|35.20|37.03|38.88|
|**870324**|– – with a cylinder capacity of more<br>than 3000 cm3|55.60|56.04|56.27|56.49|58.66|58.89|59.13|
||**– vehicles with reciprocating**<br>**internal**<br>**combustion**<br>**engines**<br>**with**<br>**compression**<br>**ignition**<br>**(diesel or semi-diesel) other:**||||||||
|**870331**|– – with a cylinder capacity of the<br>engine is not more than 1500 cm3|12.23|12.67|12.90|13.12|14.49|15.17|16.33|
|**870332**|– – with a cylinder capacity of the<br>engine more than 1500 cm3 but not<br>more than 2500 cm3|31.14|31.58|31.81|32.03|35.20|37.03|38.88|
|**870333**|– – with a cylinder capacity of the<br>engine more than 2500 cm3|55.60|56.04|56.27|56.49|58.66|58.89|59.13|
|**8703**|**Retro-car**|||222400|MDLpe|r car|||

_Source: Fiscal Code of the Republic of Moldova._

# Import tariff

Moldova has a relatively low import tariff. Its total MFN average applied in 2017 was 5.3%, here including 11.1% for agricultural goods and 4.4% for non-agricultural goods. The Table A4 highlights the tariffs and imports by key product groups.

38
16 Greek research suggests that the weaker students are off task more often than the better students. The better students in a class were engaged in learning 76% of the time, average students 73% of the time, and weaker students 57% of the time. Off task time (absentmindedness, social interaction) amounted to 9.7 minutes for the better students, 10.8 minutes for the average students, and 17.4 minutes for the weaker students.<sup>78</sup> This research suggests that the students who are more knowledgeable can construct knowledge networks more efficiently.

# **TIME SPENT LEARNING PRESCRIBED CURRICULA (ACADEMIC LEARNING TIME)**

Ideally students should not just be engaged in any learning activity, but should spend their time in activities that teach the prescribed curriculum. However, the degree to which this happens in lower-income countries is uncertain. Little systematic information exists regarding the amount of time schools actually spent presenting new material and progressing with the specified curriculum. Empirical information suggests considerable deviation. For example, in Ghana, a large portion of rural schoolteachers did not follow the prescribed weekly timetable.<sup>79</sup> This may happen because the teacher is not keeping a good track of the time that should be spent in various topics throughout the year or does not know certain topics well, such as math. Also teachers teaching multiple sections may be confused and present topics that have already been covered.

Teachers in low-income areas often do not know how much time to devote to certain topics or how to budget their time throughout the year. Teacher training and supervision rarely focuses on the use of allocated time or on planning to ensure that curricula are covered during the year. One means for doing so is giving teachers curricular calendars to help them easily visualize where they should be and to prevent classes from falling behind.

Students who learn the prescribed curriculum are most likely to score well in achievement tests, so academic learning time may be a more useful predictor of learning outcomes than mere engagement in learning activities.<sup>80</sup> However, measuring the time spent teaching the relevant curricula may be most advantageous to middle-income countries that already use time well in class and need feedback for fine-tuning. In lowerincome countries or areas, just increasing the instructional time devoted to the curricula of any previous grade might improve learning outcomes.

# **RELATIONSHIPS BETWEEN INSTRUCTIONAL TIME, POVERTY, AND ACHIEVEMENT**

Due to the issues discussed above, the schools of the poor may have less time for their students. US studies have found socioeconomic differences in the amount of time students spend learning to read, with classroom interruptions and disruptions a salient problem in schools attended by pupils from low-income families.<sup>81</sup> One longitudinal study found that high socioeconomic status students engaged in writing, reading and 78. Matsagouras 1987.

79. EARC 2003 80. http://education.calumet.purdue.edu/vockell/edpsybook/Edpsy2/edpsy2_intro 81. Stevens 1993
# **7. Tables**

## **<u>Table 1 Panel A: Gender Differences in LFP and Earnings in Burkina Faso (2019)</u>**

||(1)<br>Paid LFP|(2)<br>All LFP|(3)<br>Wage<br>Earnings|(4)<br>Paid Wage<br>Earnings|(5)<br>Business<br>Revenues|(6)<br>Harvest<br>Values|(7)<br>Crop Sales|
|---|---|---|---|---|---|---|---|
|Female|-0.203***|-0.101***|-31,850.73***|-35,310.99***|-0.936***|-0.949***|-0.965***|
||(0.009)|(0.008)|(2,128.868)|(4569.264)|(0.124)|(0.033)|(0.049)|
||||39,014.52***|115,483.9***|11.849***|11.983***|11.776***|
|Constant|||(2,056.267)|(2475.621)|(0.105)|(0.015)|(0.022)|
|**% gap**|**20 pp**|**10 pp**|**82%**|**30%**|**61%**|**61%**|**62%**|
|Observations|22,666|22,666|10,616|2,206|4,646|13,578|4,952|

_Note: Standard errors are in parenthesis. ***/**/* indicate statistical significance at the 1/5/10 percent level respectively. % gap values for business revenue, harvest value and crop sales are obtained with the exp(b)-1 transformation (Halvorsen and Palmquist (1980)), while that of LFP were obtained from the solving for the logit equation. Wage earnings was in CFAs, so the wage gap was calculated by dividing the female dummy coefficient with the constant. For the paid LFP and All LFP results, the marginal effects are reported, so they do not have a constant._

**<u>Table 1 Panel B: Summary Statistics (2019)</u>**

||**A**|**ll**|**M**|**en**|**Wo**|**men**|
|---|---|---|---|---|---|---|
|**VARIABLES**|**mean**|**sd**|**mean**|**sd**|**mean**|**sd**|
|_Variables used in Labor Force Regression Decomposition_|||||||
|Overall LFP|0.798|0.402|0.853|0.354|0.782|0.432|
|LFP (Paid Work Only)|0.615|0.487|0.726|0.446|0.523|0.500|
|Individual Lives in an Urban Area|0.310|0.463|0.325|0.468|0.298|0.457|
|Individual Age|32.93|13.32|33.04|13.67|32.84|13.02|
|Individual Age Squared|1,262|984.9|1,279|1,011|1,248|962.6|
|Number of Adults in HH|4.465|2.816|4.490|2.822|4.444|2.810|
|Number of Children Aged 0-5 in HH|1.154|1.328|1.071|1.254|1.223|1.384|
|Individual own a Financial Account|0.275|0.446|0.404|0.491|0.166|0.372|
|Individual is Married|0.661|0.473|0.588|0.492|0.723|0.448|
|Individual is Divorced/Married/Separated|0.048|0.214|0.014|0.116|0.077|0.267|
|Individual Completed Secondary Schooling|0.079|0.270|0.106|0.308|0.057|0.232|
|Individual owns a Vehicle|0.514|0.500|0.649|0.477|0.401|0.490|
|Wealth PCA - Based on Home Condition|-0.156|1.596|-0.143|1.615|-0.167|1.581|
|Wealth PCA - Based on Appliances Ownership|0.008|2.033|0.030|2.019|-0.011|2.044|
|Has a Disability|0.0008|0.0275|0.0005|0.0227|0.0009|0.031|
|Is Female|0.545|0.498|||||
|**Number of Observations**|**22,666**||**10,270**||**12,396**||
|_Variables used in Monthly Wage Decomposition_|||||||
|Monthly Income (In CFA)|19,573|59,167|39,029|82,790|7,174|31134|
|Individual Lives in an Urban Area|0.237|0.425|0.368|0.482|0.154|0.361|
|Individual Age|29.95|12.06|26.48|10.71|32.16|12.35|

31