rafmacalaba/datause-encoder-data huggingface dataset:
reflect levels of infrastructure which are high by global standards. Because comprehensive coverage of infrastructure data in the DDP only starts in the late 1980s/early 1990s, our analysis is restricted to a cross-sectional sample which covers the period 1992-2000. Although this rules out the use of panel data techniques, it does have the advantage of allowing us to further expand our sample, for **W3** , to 143 countries. Notably, we are now able to include the majority of nations which comprise the former Soviet Union. Many of these countries are landlocked. Indeed, while SSA has the greatest number of landlocked countries of all World Bank regions, ECA has the highest proportion (World Bank, 2008, p 101).

The regressions which we estimate take a similar form to those in section 5. In particular, we regress the annual average logarithmic growth rate of real GDP per capita on our neighbor growth variable ( **W** _y_ ) and a set of controls, again focusing on **W3** . This set of controls includes not only those that were considered in section 5, but also dummy variables for whether or not a country is landlocked ( _LL_ ), whether or not a country could be classified as resource rich in 1992 ( _RR92_ ) and whether or not a country not classified as resource rich in 1992 became resource rich during the sample-period ( _RRnew_ ). In addition, we include our measure of infrastructure development ( _Infra_ ) as a control, both by itself and interacted with _LL_ . However, with the exception of _Infra_ , our primary interest is not so much with these extra controls, as with the various interaction effects with **W** _y_ . Thus, we interact **W** _y_ with _LL_ and/or _Infra_ in various specifications.

27
Gruhle, T. and Harms, P., 2022. Producer Services and the Current Account. _Journal of International Money and Finance_ , 121, p.102514.

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Rebucci, A., Spatafora, N., 2006. Oil prices and global imbalances. IMF World Economic Outlook 4, 71–96.

20
# **Figure 15. Growth Decomposition**

<!-- Start of picture text --> (a) SCCEs<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>‐0.20<br>‐0.40<br>‐0.60<br>G EP EL DR<br><!-- End of picture text --> <!-- Start of picture text --> (b) WCCEs<br><!-- End of picture text --> <!-- Start of picture text --> 0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>‐0.20<br>‐0.40<br>‐0.60<br>G EP EL DR<br><!-- End of picture text --> Note: The variables G, EP, EL and DR are calculated according the growth decomposition method explained in Section 2a. All variables are computed taking the logs of the average yearly growth rates over a ten-year moving window. _Source: WDI._
Table F.11: Productivity and Technology Sophistication, GBF and SSBF MOST Technology, Disaggregated Sectors

||(1)|(2)|
|---|---|---|
|VARIABLES|ln(VAPW)|ln(VAPW)|
|GBF EXT<br>|1.70***<br>(0.32)||
|GBF EXT<sup>2</sup>|-0.20***||
||(0.05)||
|GBF EXT*AGRI||1.93***<br>(0.45)|
|GBF EXT*MANF||-0.28***<br>(0.09)|
|GBF EXT*SVC||0.37***|
|||(0.10)|
|SSBF MOST*Agriculture|0.55***|0.63***|
||(0.20)|(0.20)|
|SSBF MOST*Livestock|-0.05|0.04|
||(0.56)|(0.59)|
|SSBF MOST*Food Processing|0.36**|0.38**|
||(0.19)|(0.19)|
|SSBF MOST*Apparel|0.65***|0.60***|
||(0.15)|(0.15)|
|SSBF MOST*Motor Vehicles|0.90***|0.73***|
||(0.30)|(0.28)|
|SSBF MOST*Pharmaceuticals|0.26|0.21|
||(0.16)|(0.17)|
|SSBF MOST*Retail and Wholesale|-0.04<br>(0.15)|-0.14<br>(0.16)|
|SSBF MOST*Financial Services|2.42***|2.44***|
||(0.59)|(0.60)|
|SSBF MOST*Land Transport|0.44|0.47|
||(0.40)|(0.40)|
|SSBF MOST*Health Services|-0.56|-0.69**|
||(0.36)|(0.35)|
|SSBF MOST*Leather|-0.63***|-0.72***|
||(0.15)|(0.16)|
|SSBF MOST*Other Manufacturing|0.16*|0.13|
||(0.09)|(0.08)|
|SSBF MOST*Other Services|-|-|
|Var(ABF EXT)|0.30***|0.37***|
||(0.10)|(0.10)|
|Firm Characteristics|_~~√~~_|_~~√~~_|
|Observations|2,746|2,746|
|R-squared|0.51|0.51|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

104
in the ‘art market.’ (This appears to be the case, even if not all art pieces are, of course, valued in the millions or even thousands of dollars.) In the world of arts in education, Artsy has created “ _Teaching Art with Artsy_ , a simple PDF, which illustrates how high school teachers can use Artsy’s tools for lesson planning and classroom instruction.”<sup>32</sup> In partnership with the New York City Department of Education, _Teaching Art with Artsy_ can be used in conjunction with the Department’s “ _Blueprint for Teaching and Learning in Visual Arts_ ' 12th Grade Benchmarks.”<sup>33</sup> The new _Blueprint_ provides “sections that address students with special needs and English Language Learners. It includes a glossary of art terms, classroom supply lists, a discussion on planning and assessment, an annotated bibliography and webography, as well as suggestions for integrating the museum experience into classroom instruction.”<sup>34</sup> While _Teaching Art with Artsy_ is meant for New York City public schools, it can be a resource for students and teachers across the United States and abroad.<sup>35</sup> Other Artsy’s education initiatives include:

- Explore the World’s Art: “access to art from galleries, museums, institutions, and art fairs from around the world.”

- The Art Genome Project: a “discovery tool for art, architecture and design, powered by a team of in-house art historians.”

- Additional Features: “coverage of current art world events and artists biographies, to amazing zoom and view in room features.”<sup>36</sup>

# **The Negative Side**

_Technology is a useful servant but a dangerous master_ .

While technology is not entirely to blame, it has caused profound challenges in creative work. To start with books, as we have seen, there are several advantages to e-readers. But e-readers and tablets may not be a trigger of celebration. Scientific literature here provides a blurry picture. In “The Reading Brain in the Digital Age: The Science of Paper versus Screens,” Ferris Jabr makes a central observation:

> “[E]vidence from laboratory experiments, polls and consumer reports indicates that modern screens and e-readers fail to adequately recreate certain tactile experiences of reading on paper that many people miss and, more importantly, prevent people from

> 32 Artsy, “Teaching Art With Artsy,” accessed March 28, 2015, <u>https://www.artsy.net/post/artsyed-teaching-art-with-artsy.</u>

> 33 Ibid

> 34 Joel I. Klein, “Letter from the Chancellor,” New York City Department of Education, accessed March 28, 2015, <u>https://www.artsy.net/feature/artsy-education.</u>

> 35 - Artsy, “Artsy Education,” accessed March 28, 2015, <u>https://www.artsy.net/feature/artsy education.</u>

> 36 Ibid.

11
Policy ReseaRch WoRking PaPeR 4793

# **Abstract**

Using nationally representative household surveys, this paper examines the trends in attainment, enrollment, and access to tertiary (higher) education in India from 1983 to 2005. The findings suggest that there has been considerable progress in attainment and participation; however, they remain low. Important gaps exist in enrollment between rich and poor, rural and urban areas, men and women, disadvantaged groups and the general population, and states. Analysis of transition rates from secondary education to tertiary education and regression analysis indicate that inequality in tertiary education between disadvantaged groups and the general population is explained by low completion rates of secondary education. Inequality in tertiary education related to income, gender, rural residence, and between states is explained by: (i) differences in completion rates of secondary education, and (ii) differences in the probability of transitioning from secondary education to tertiary education. In particular, the importance of household income has grown markedly. Equitable expansion of secondary education is therefore critical for improving the equity of tertiary education. There is also a need to help qualified youth from low-income families and rural backgrounds to attend tertiary education, in particular the technical and engineering streams, in which participation is lower.

This paper—a product of the Human Development Department, South Asia Region—is part of a larger effort in the department to analyze progress in education in South Asia to support planned investments in the education sector in the region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ablom@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
information on the skills measures used in this study were also removed from the sample. In keeping with standard practice, individuals reporting zero earnings were assigned a small value of 0.0001 before this was transformed to the logarithmic scale.<sup>13</sup> Finally, both individuals in the top 1 percent of the earnings distribution and employers were excluded from the sample to avoid potential outliers and to minimize bias due to measurement error in earnings. The total proportion of missing data comprised less than 0.05 percent of the sample for each country. The resulting sample size for each country includes between 480 and 1,400 men and women.

# **_Descriptive Statistics_**

We begin by comparing men and women, by country, on key variables of interest. As mentioned before, the sample includes a substantial number of self-employed workers in full-time employment. Table 1 shows that among men and women both, a larger proportion report being employed in wage work than in self-employment. More women than men belong to the selfemployed group in Vietnam and Kenya. The opposite is true in Armenia and Ukraine, where a larger proportion of men than women are self-employed.

**Table 1. Self-employed and Wage Workers as Proportions of All Workers, by Gender and** **<u>Country</u>**

||Self-e|mployed|Wage|workers|
|---|---|---|---|---|
||Men|Women|Men|Women|
|Armenia|0.05|0.02|0.44|0.48|
|Bolivia|0.17|0.19|0.37|0.26|
|Colombia|0.16|0.15|0.37|0.32|
|Georgia|0.05|0.06|0.41|0.48|
|Kenya|0.16|0.21|0.40|0.23|
|Ukraine|0.06|0.01|0.47|0.46|
|Vietnam|0.11|0.23|0.31|0.35|

_Note_ . The sample includes 25- to 64-year-olds in self-employment or wage work. Employers, unpaid workers, and part-time workers are excluded from the sample. _Source_ : STEP Surveys (2014).

Average hourly earnings for men and women are presented in Table 2. There is substantial variation in the earnings distributions across all countries, which is also evident in graphs of the earnings distributions (see Figure A1 in the Appendix). The distributions for most countries skew to the left, indicating that the average earnings in these countries are below the median. Women, on average, earn less than their male counterparts across the board. This

> 13 The proportion of workers reporting zero earnings was within 1 percent of the sample.

12
# **Table A.15. Predicted Poverty Rates Based on Imputation, from TA 1 to TA 3 using 2019/20 Poverty Line, Tanzania (percentage)**

|<br>**Mthd**||||**T**<br>**Mdl**|**A 3**|**Mdl**|||
|---|---|---|---|---|---|---|---|---|
|**eo**|**Model 1**|**Model 2**|**Model 3.1**|**oe**<br>**3.2**|**Model 4.1**|**oe**<br>**4.2**|**Model 8**|**Model 9**|
|1) Nl li i dl|**15.9**<sup>**a**</sup>|**16.1**<sup>**a**</sup>|35.7|**15.0**|31.7|**15.9**<sup>**a**</sup>|**16.0**<sup>**a**</sup>|**16.1**<sup>**a**</sup>|
|orma near regresson moe|(2.1)|(2.1)|(2.4)|(1.8)|(2.8)|(2.1)|(2.1)|(2.1)|
|2) Emirical distribution of the error terms|**15.9**<sup>**a**</sup>|**15.8**|36.3|**14.6**|31.6|**15.6**|**15.7**|**16.1**<sup>**a**</sup>|
|p|(2.1)|(2.1)|(2.3)|(1.8)|(2.8)|(2.1)|(2.0)|(2.1)|
|_Control variables_|||||||||
|Food expenditures|||Y|Y|||||
|Non-food expenditures|||||Y|Y|||
|Utilities: water, kerosene, lighting|||||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.27|0.43|0.89|0.89|0.69|0.69|0.47|0.35|
|N1(base survey)|711|711|711|711|711|711|711|711|
|N2 (target survey)|698|698|698|698|698|695|698|698|
|**True poverty rate in TA 1**<br> <br>**Note**: The standard errors in parentheses are adjust<br>linear regression model with the theoretical distri|ed for comple<br>bution of the e|x survey desi<br>rror terms an|gn. All estimat<br>d Method 2 us|1<br>(<br>es are obtain<br>es the empir|7.7<br>1.9)<br>ed with the pop<br>ical distribution|ulation weigh<br>of the error|ts. Method 1<br>terms. Both m|uses the normal<br>ethods employ|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Models 3.1 and 4.1 employ non-standardized variables. Models 3.2 and 4.2 employ variables in TA 3 standardized by those in TA 1. Food and non-food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA 3 use the estimated parameters based on the TA 1. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.7.

59
_t_ ( _t_ =1988....1994, 1997) by an indicator of specialization, noted as _ISi_ , _j_ , _t_ , as follows: <!-- Start of picture text --> vai , j , t<br>∑ vai , j , t<br>ISi , j , t = j ∑ vai , j , t<br>i<br>∑∑ vai , j , t<br>i j<br><!-- End of picture text --> In a relative sense, the more specialized a province _j_ is in the industry _i_ in time _t_ , the higher is the value of this indicator. We examine the impacts of the FDI, the level of the economic development, the dynamism of the neighbor regions, and the population growth on this specialization.

To capture the effects of the FDI on the concentration of the industries, we introduce the indicator “openness ”, noted as _FDIOVERj_ , _t_ , which is measured by the ratio of the FDI to the GDP of the province _j_ in time _t_ in logarithmic form.<sup>15</sup> If we follow the hypotheses that the Chinese provinces are at different stages of development, we may expect that, the development level of one province may condition the types of industries it hosts. To capture the role of the stage of development, we introduce a variable “initial development level”, noted as _yt j_ , _t_ − 1 , measured by the real GDP per capita of province _j_ in time _t_ − 1 in logarithmic form.

The location choice of firms does not only depend on conditions of the hosting province, but also those of the neighboring provinces. For example, a firm may choose to establish in Zhejiang in order to have good access to the market of Shanghai. A province surrounded by dynamic neighbors may be more attractive to some industries. To measure the influence that one province may receive from its partners, in other words, the growth spillover effects that the immediate neighbor<sup>16</sup> have on the performance of the province in question, we construct our indicator “neighbor performance”, noted as _GAWi_ , _t_ , which is defined as the weighted average of the growth rate of the neighbor provinces.<sup>17</sup>

> 15 Because the information of the FDI by sector in each province of the period in question is not available, we have to use the total volume of FDI without distinguishing the hosting sectors.

> 16 For the sake of simplicity, we limit the “neighbors” as the provinces that share common borders, and do not take into account the linkages through inland waterway. See Poncet (2003) and Amiti and Javorcik (2004) for discussions on the strong border effect among the Chinese provinces. And see, for example, Evenett and Keller (1998), Bergstrand (1985), and Deardorff (1995), for further discussions on the significant negative role of the distance on bilateral trade.

> 17 See Luo (2005).

14
31 Appendix Table 1. Effect of Remittances on Poverty for Non-Remittance and Remittance-Receiving Households, Guatemala, 2000

||**Receive no**<br>**remittances**<br>**(1)**|**Receive**<br>**internal**<br>**remittances**<br>**(from**<br>**Guatemala)**<br>**(2)**|**Receive**<br>**international**<br>**remittances**<br>**(from USA)**<br>**(3)**|**Receive**<br>**internal and**<br>**international**<br>**remittances**<br>**(4)**|**Percent**<br>**change (No**<br>**remittances**<br>**vs. internal**<br>**remittances)**<br>**(5)**|**Percent**<br>**change (No**<br>**remittances**<br>**vs.**<br>**international**<br>**remittances)**<br>**(6)**|**Percent**<br>**change (No**<br>**remittances**<br>**vs. internal**<br>**and**<br>**international**<br>**remittances**<br>**(7)**|
|---|---|---|---|---|---|---|---|
|Poverty||||||||
|headcount|58.03|57.34|56.69|56.19|(-1.19)|(-2.31)|(-3.18)|
|(percent)||||||||
|Poverty gap<br>(percent)|24.60|23.90|23.17|22.62|(-2.85)|(-5.82)|(-8.05)|
|Squared<br>poverty gap|14.06|13.47|12.15|11.69|(-4.20)|(-13.59)|(-16.86)|
|(percent)<br>Gini|0.484|0.484|0.477|0.476|--|(-1.45)|(-1.66)|
|coefficient||||||||
|Observed||||||||
|mean per<br>capita||||||||
|household<br>expenditure<br>(including<br>remittances)<br>(quetzals)<br>N|7,362.76<br>7,276|7,563.40<br>7,276|7,556.22<br>7,276|7,721.43<br>7,276|+2.72|+2.63|+4.87|

Notes: All values based on observed – not predicted – per capita household expenditure. Poverty calculations made using 2000 World Bank poverty line of 4,319 quetzals/ person/ year. All values are weighted. In 2000, 1 Guatemalan quetzal = US $0.128.

Source: Calculated from Guatemala 2000 ENCOVI Survey
As indicated in equations (5.a) and (5.b), we cannlot identify the own- and cross-price elasticities of demand since the functions yo(w,,q,,r,;f3) and y0(w,,q,,r,;f3) do not vary across cross-sectional units. Therefore we focus on estimating the effect of the houselhold specific variablesXi, and Zi on demand for inputs so that we can compute their overall expected effect on farm output. For instance, the total impact of ox ownership on output is given by: where estimates of ox;, Pi, and P32, are obtained tlhrough the estimation of the production function, and estimates of _aT_ and _(F_ ) are obtained through the estimation of the input demand _DxJ Dx.j_ equations (5.a) and (5.b). Note however, that the estimates of _a_ and _(F_ ) are themselves _axJ ax._ functions of the production parameters P3i, and the total factor productivity parameters (I ,.

# **_Variahles_** _included_ **_in_** _tlte estimation_

Time-varying explanatory variables included in the production function are cultivated area, fertilizer used, household population, the value of cattle owned by the household, the stock of draught animals (oxen) and farm equipment, the amount of credit received, and a "weather shock" variable. "Weather shocks" are defined as the percentage deviation of precipitation from its 30-year average during the planting season, two growing periods, and one harvesting season. The implicit assumption that too much rain can be as harmful as too little of it seems to be quite realistic under Zambian conditions. Higher order terms (i.e. the weather shock variable squared, cubed and raised to the forth power) are introduced to account for possible non-linearities. Animal-owners' ability to perform farming operations in a more timely manner is likely to be of particular relevance under abnormal climatic conditions. To account for this possibility, we interact the weather shock with the value of the farm's stock of draught animals and farm equipment. Of the time varying variables, -household population, the non-interacted weather variables, the time trend and district dummies-time trend interactions are assumed to be exogenous while the remainder are assumed to be endogenous (i.e., correlated with unobserved individual effects).

Time-invariant explanatory variables are the sex of the household head, access to extension, primary and secondary education dummies and sixty district dummies. Extension and the education dummies are assumed to be endogenous while the remaining time-invariant