rafmacalaba/datause-encoder-data huggingface dataset:
frequently associated with being part of global production chains that confer important benefits (Hoekman and Javorcik, 2007).

This paper attempts two things. First, we will present some new data on developing country exports of services, goods, and FDI, assessing the extent to which these are going to richer countries. Second, we will undertake a preliminary exploration of the consequences of these uphill flows of embodied skills in term of the impact on growth of the source country. Here, we will follow closely the work of Hausmann et al. (2007).

# **II. Data**

Our focus in this paper is on the direction of flows of embodied skills. In three different areas—FDI, goods, and services—for which we present some broad data, we need to explain how we define or illustrate the flow of skills.

Our FDI data comes from two sources. The Thomson Financial SDC Platinum database provides data on FDI taking the form of mergers and acquisitions. The Financial Times’ FDI Intelligence database provides similar data on Greenfield investments. These databases are described in detail in the appendix.

Our goods trade data comes from the WITS database of the United Nations. We collected data at the 5-digit level (largely because finer data say at the 6-digit level really become available only in the late 1980s and we were interested in checking whether the phenomenon of uphill flows was a feature of historical data). For computational reasons, we collected data for every 5-year interval and restricted the sample to countries that together accounted for about 90 percent of world trade.

We draw upon Hausmann et al. (2007) to characterize skill-intensive products. They calculate a measure called PRODY, which is a weighted sum of the per capita GDP of countries exporting a given product, and thus represents the income level associated with each of these goods. In this paper, we define—admittedly arbitrarily—skilled products that are either above the median level or in the top 25<sup>th</sup> percentile of PRODY for all products defined at the 5-digit level of aggregation for the year 1990.

Our services data come from the IMF’s Balance-of-Payments statistics and the U.S. Bureau of Economic Analysis.

# **III.  Stylized Facts on “Uphillness”**

We first present some basic facts about the flows of embodied skills.

# _Foreign Direct Investment_

In chart 3A, we plot the share of non-OECD countries in world FDI exports for the period 2003-2007 for which data are available. This share goes up from about 20 percent to 25 percent over the period under consideration.

While these charts show how developing countries are becoming increasing exporters of FDI, they do not give an indication of the direction of these flows. Chart 3B isolates the 4
_Table A1.3: Attrition ‐ Baseline difference between attritors from original control vs attritors original treatment group_

||Control g|roup|ITT gro|up|N|
|---|---|---|---|---|---|
||Mean|SD|Coeff|SE||
|Indi<br>|<br>vidual and ho<br>|<br>usehold char<br>|.<br>acteristics<br>|<br>||
|Age|27.226|5.186|0.387|0.363|769|
|Married|0.670|0.471|‐0.026|0.031|768|
|Employment status|0.644|0.479|‐0.050|0.036|769|
|Business ownership|0.670|0.471|‐0.022|0.031|769|
|Consumption food|5.223|1.334|0.136|0.086|769|
|Consumption nonfood|2.447|1.287|‐0.043|0.099|769|
|Formal bank account|0.322|0.468|0.016|0.030|769|
|(Log) amount formal loans|‐0.386|1.859|‐0.176|0.148|765|
|(Log) amount informal loans|‐1.017|2.913|‐0.220|0.212|758|
|Education<br>No education|0.190|0.393|‐0.023|0.024|769|
|level<br>Some Primary|0.340|0.474|0.034|0.031|769|
|Some|0.410|0.493|‐0.017|0.029|769|
|Secondary||||||
|Some University<br>or Higher|0.060|0.237|0.006|0.015|769|
|Literacy<br>No English|0.249|0.433|‐0.009|0.032|769|
|Some English|0.249|0.433|0.062**|0.030|769|
|Good English|0.501|0.501|‐0.052|0.033|769|
|Numeracy<br>Low|0.231|0.422|‐0.012|0.026|769|
|Medium|0.190|0.393|0.008|0.030|769|
|High|0.579|0.494|0.003|0.035|769|
|Household size|7.182|3.463|‐0.143|0.258|769|
|Number of children|3.026|2.301|0.069|0.168|769|
|Number of elderly|0.117|0.360|‐0.036|0.023|769|
|Number of rooms|3.091|1.784|‐0.070|0.099|769|
|Number of buildings|3.670|1.836|‐0.111|0.112|769|
|(Log) distance to KCB branch|0.174|0.380|‐0.012|0.011|769|
|Conflict exposure 2011‐2014|0.003|1.177|‐0.051|0.061|769|
|(300km buffer)||||||
|Conflict exposure 2015‐2017|‐0.004|1.130|0.011|0.068|769|
|(300km buffer)||||||
||State|at baseline||||
|Central Equatoria|0.169|0.375|‐0.000|0.000|769|
|Eastern Equatoria|0.164|0.370|0.003|0.003|769|
|Lakes|0.174|0.380|0.008|0.007|769|
|Northern Bahr El Ghazal|0.148|0.356|0.000|0.004|769|
|Western Bahr El Ghazal|0.171|0.377|0.000|0.004|769|
|Western Equatoria|0.169|0.375|‐0.000|0.000|769|

_Note: Differences between the original control vs ITT group in baseline characteristics estimated by an OLS on the ITT group dummy and strata fixed effects. Standard errors are clustered at boma level. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level._ 36
because the missing data are, by definition, not observed. Scheuren (2005) provides an interesting discussion on the prevalence of the different types of missing data in practice, and on the advantages and disadvantages of different approaches to dealing (or not dealing) with them. In empirical analyses, imputations procedures are often applied based on the assumption that the data are MAR, sometimes accompanied by sensitivity analysis aimed at gauging the impact that departure from the MAR hypothesis may have on the analysis (see Giusti and Little (2011) for an example).

The conditions under which valid inferences could be obtained from missing data is laid out in Rubin's (1987) seminal work on multiple imputation (MI), which is a Monte Carlo technique that replaces missing values for a given variable by _m_ >1 simulated alternatives. In repeated imputation inference, each of the _m_ imputed datasets are analyzed separately, and the results are combined so that the uncertainty associated with missing data is incorporated into the computation of estimates and confidence intervals. While MI had originally been deemed as a viable strategy to disseminate _complete_ datasets from sample surveys and censuses, it has evolved to be part of the toolkit of a diverse array of researchers from biomedical, social and behavioral sciences, whose analyses may otherwise be hindered by missing data.

MI assumes the missing data to be MAR, and consists of three steps: (i) _m_ imputations (i.e. _m_ complete datasets) are generated based on an _imputation model_ that encompasses a vector of observable covariates that predict the missingness in a given variable, (ii) an _analytical model_ is estimated separately with each of the _m_ complete datasets, and (iii) the results obtained from _m_ complete data analyses are combined into a single set of multiplyimputed parameter estimates and standard errors, in accordance with Rubin (1987). Multiply-imputed values are chosen to represent “both uncertainty about which values to impute assuming the reasons for nonresponse are known and uncertainty about the reasons for nonresponse.” (Rubin 1988: 79) MI also incorporates in the analysis the uncertainty associated with the imputation process itself, by incorporating in the statistical inference the variability across imputations.

A typical example in the literature that is relevant for our discussion of missing GPS-based land areas is missing income data in household surveys. Respondents often refuse to report personal/household income, and the likelihood of refusal tends to increase with wealth. The analogy with GPS-based land areas is clear: the larger the variable of interest (i.e. income, land area), the higher the probability that the data may be missing. Recent empirical work relying on MI to deal with missingness in income data clearly show that the mean estimate and the associated standard error for the imputed variable are affected by the choice of the imputation method, particularly when the proportion of missing values is large (Schenker et al., 2006; Zarnoch et al., 2010, Giusti and Little, 2011; Ahearn et al., 7
Table 8: Probability of being found in school at least once

|(7)<br>All<br>2001<br>0.119<br>(0.046)***<br>1.295<br>(0.072)***<br>-0.063<br>(0.101)<br>-0.029<br>(0.041)<br>-0.088<br>(0.069)<br>-0.005<br>(0.016)<br>0.3<br>(0.050)***<br>0.377<br>(0.051)***<br><br>RE Probit<br>4327<br>49<br>0<br>0<br> the post-<br>rms 2 and<br>s in total).<br>m presence<br> regression<br>f the total<br>cant at 10|
|---|
|(5)<br>(6)<br>All<br>All<br>2000<br>2001<br>0.264<br>0.038<br>(0.099)***<br>-0.027<br>2.279<br>(0.098)***<br>-0.053<br>(0.143)<br>-0.039<br>(0.047)<br>0.16<br>(0.080)**<br>-0.01<br>(0.020)<br>0.524<br>(0.058)***<br>0.764<br>(0.059)***<br>0.491<br>(0.019)***<br>RE Probit<br>RE<br>4197<br>4445<br>49<br>50<br>0.07<br>0.02<br>64.74<br>2<br>ound in school at least once in<br>ervation in columns (1)-(5) is te<br> of 2001 (at most 7 observation<br>ine (term 1, 2000). Pre-progra<br> of 2000 (1 observation). The <br>mn (5). Rho is the proportion o<br>ported in parentheses. * signi...|
|(4)<br>Not enrolled<br>Not present<br>2000<br>0.072<br>(0.016)***<br>0.021<br>(0.012)*<br>RE<br>1750<br>49<br>0.03<br>20.02<br>the child was f<br>on period of obs<br>erms 1, 2, and 3<br>ime of the basel<br>chool in term 1 <br> probit in colu<br>rd errors are re|
|(3)<br>Enrolled<br>Not present<br>2000<br>0.066<br>(0.057)<br>0.541<br>(0.041)***<br>RE<br>1337<br>49<br>0.12<br>1.36<br>kes value 1 if <br>post-interventi<br> (6)-(7) it is t<br> and 6 at the t<br>as found in s<br>random e¤ects<br>onent. Standa<br>1 percent|
|(2)<br>Enrolled<br>Present<br>2000<br>0.015<br>(0.020)<br>0.941<br>(0.014)***<br>RE<br>1132<br>49<br>0.04<br>0.61<br>iable that ta<br>erwise. The<br>; for columns<br>ge between 4 <br>if the child w<br> (1)-(4) and <br>ariance comp<br>igni...cant at|
|(1)<br>Sample<br>All<br>Year<br>2000<br>Treatment<br>0.072<br>(0.032)**<br>Present pre-program<br>Present pre-program<br>* Treatment<br>Girl<br>Female head of<br>household<br>Nr of children <age 6<br>in household<br>Age 5 in 2000<br>Age 6 in 2000<br>Constant<br>0.425<br>(0.022)***<br>Model<br>RE<br>Observations<br>4315<br>Number of panels<br>50<br>rho<br>0.04<br>chi2 Ho: rho=0<br>5.27<br>Notes:<br>The dependent variable is a discrete var<br>intervention observation period, and 0 oth<br>3 of 2000 (at most 6 observations in total)<br>The sample consists of all children with a<br>is a dummy variable that takes value 1 <br>model is linear random e¤ects in columns<br>variance contributed by the panel-level v<br>percent; ** signi...cant at 5 percent; *** s|

27
WPS4069

# Partially Awakened Giants: Uneven Growth in China and India

## Shubham Chaudhuri and Martin Ravallion<sup>*</sup>

**Abstract:** The paper examines the ways in which recent economic growth has been uneven in China and India and what this has meant for inequality and poverty. Drawing on analyses based on existing household survey data and aggregate data from official sources, the authors show that growth has indeed been uneven—geographically, sectorally and at the household level—and that this has meant uneven progress against poverty, less poverty reduction than might have been achieved had growth been more balanced, and an increase in income inequality. The paper then examines why growth was uneven and why this should be of concern. The discussion is structured around the idea that there are both “good” and “bad” inequalities—drivers and dimensions of inequality and uneven growth that are good or bad in terms of what they imply for both equity and long-term growth and development.  The authors argue that the development paths of both China and India have been influenced by, and have generated, both types of inequalities and that while good inequalities—most notably those that reflect the role of economic incentives—have been critical to the growth experience thus far, there is a risk that bad inequalities—those that prevent individuals from connecting to markets and limit investment and accumulation of human capital and physical capital—may undermine the sustainability of growth in the coming years. The authors argue that policies are needed that preserve the good inequalities—continued incentives for innovation and investment—but reduce the scope for bad ones, notably through investments in human capital and rural infrastructure that help the poor connect to markets.

### World Bank Policy Research Working Paper 4069, November 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*

Chaudhuri is with the World Bank’s East Asia PREM unit and Ravallion is with the Bank’s research group in DEC. This paper was written for the report “Dancing with Giants: China, India, and the Global Economy,” edited by L. Alan Winters and Shahid Yusuf. For their comments, the authors are grateful to Richard Cooper, David Dollar, Assar Lindbeck, Branko Milanovic, T.N. Srinivasan, Nicolas van de Walle, Alan Winters and participants at the Dragon and the Elephant Conference, Shanghai, 2006.
**Figure 2.9: Domestic indirect value‐added (VA) in gross exports by industry (2011)**

<!-- Start of picture text --> Indirect services VA in gross exports OECD Indirect services VA in gross exports Turkey<br>Food products 38.6 Textiles 34.6<br>Chemicals 36.4 Chemicals 28.5<br>Textiles 35.5 Non-metallic minerals 27.1<br>Paper, print, publish 35.5 Wood 26.1<br>Wood 35.4 Rubber & plastics 25.4<br>Rubber & plastics 34.7 Manufacturing nec 24.9<br>Motor vehicles 34.7 Coke, petroleum 23.9<br>Manufacturing nec 32.2 Food products 23.8<br>Non-metallic minerals 31.4 Other transport eq 22.2<br>Electrical machinery 31.0 ICT & electronics 22.1<br>Other transport eq 31.0 Paper, print, publish 21.5<br>Machinery 29.7 Electrical machinery 20.7<br>Fabricated metals 28.1 Basic metals 19.6<br>ICT & electronics 27.8 Motor vehicles 18.6<br>Basic metals 27.7 Machinery 18.5<br>Coke, petroleum 20.9 Fabricated metals 18.5<br>0 10 20 30 40 0 10 20 30 40<br>Percentage of total VA Percentage of total VA<br><!-- End of picture text --> Source: OECD TiVA 2015; author’s calculations.

**The bulk of services inputs in Turkish manufacturing exports come from transport and distribution; OECD firms, in contrast, rely on a much higher share of business services.** In Turkey, distribution and transport each account for about one‐third of services value added in manufacturing exports, while business services account for about 13 percent. The reverse is true in the OECD: business services account for about one‐third while transport services are much smaller. Distribution services account for a similar share in both Turkey and the OECD, as do financial services. As shown in Figure 2.10, the breakdown of types of services is roughly similar across manufacturing industries in Turkey.

**Figure 2.10: Indirect services value‐added in gross exports by manufacturing sector (2011)**

<!-- Start of picture text --> Indirect services VA in gross exports<br>Business Distribution Finance Other serv<br>Real est Rent mach Telecom Transport<br>TextilesNon-metallic mineralsChemicals Rubber & plasticsWoodManufacturing necCoke, petroleumFood productsOther transport eqICT & electronicsPaper, print, publishElectrical machineryBasic metalsMotor vehiclesMachineryFabricated metals<br>40<br>30<br>20<br>10<br>Percentage of total VA<br>0<br><!-- End of picture text --> Source: OECD TiVA 2015; author’s calculations.

12
<!-- Start of picture text --> "LINE "<br>OF BEST FIT: GUNTUR COTTON<br>700<br>600-<br>500-<br>V"400-<br>g300-<br>200-<br>100<br>0<br>1970/71 1973/74 1976/77 1979/80 1982/83 1985/86 1988/89 1991/92<br>o ACTUAL OUTPUT o PREDICTED OUTPUT<br><!-- End of picture text -->
|**Table 6: Descriptive Statistics**|**Workers**||||||||
|---|---|---|---|---|---|---|---|---|
||**Non-Progr**<br>**Construct**|**am**<br>**or**|**Progr**<br>**Constru**|**am**<br>**ctor**|**Non-Pro**<br>**Contra**|**gram**<br>**ctor**|**Prog**<br>**Contr**|**ram**<br>**actor**|
||**mean**|**Sd**|**mean**|**Sd**|**Mean**|**Sd**|**Mean**|**Sd**|
|Age|28.62|8.63|28.65|6.85|30.51|7.61|27.62|6.41|
|Sex|0.81|0.40|0.85|0.36|0.86|0.35|0.77|0.42|
|Monthly pay(log)|5.77|0.76|6.12|0.84|6.52|1.00|6.42|1.01|
|Days worked per month(log)|3.05|0.40|3.09|0.34|3.01|0.49|3.13|0.27|
|<br>casualdummie|0.23|0.42|0.22|0.42|0.27|0.45|0.29|0.46|
|**_(Highest level of ) Educational_**|**_Attainment_**||||||||
|Years of schooling|8.64|4.31|10.33|3.79|10.88|3.36|10.26|4.36|
|<br>Primary|0.43|0.50|0.31|0.46|0.24|0.43|0.33|0.48|
|Secondary|0.47|0.50|0.65|0.48|0.67|0.47|0.59|0.50|
|College|0.01|0.08|0.01|0.09|0.02|0.14|0.03|0.17|
|<br>TVETcomplete|0.15|0.35|0.35|0.48|0.27|0.45|0.39|0.49|
|**_Entire Employment History_**|||||||||
|<br>Apprenticeship completed in|0.25|0.44|0.46|0.50|0.35|0.48|0.39|0.49|
|the past|||||||||
|<br>Goverment employee|0.10|0.30|0.18|0.38|0.22|0.42|0.20|0.40|
|employee in a firm|0.42|0.49|0.44|0.50|0.51|0.51|0.44|0.50|
|<br>Domestic employee|0.06|0.23|0.09|0.29|0.12|0.33|0.15|0.36|
|Family worker|0.03|0.17|0.03|0.17|0.00|0.00|0.05|0.21|
|<br>Self-employed|0.07|0.26|0.06|0.24|0.08|0.28|0.05|0.21|
|<br>Employed in a cooperative|0.02|0.14|0.14|0.34|0.04|0.20|0.08|0.27|
|Casual workers|0.12|0.33|0.09|0.29|0.08|0.28|0.03|0.17|
|**_Prior Activity_**|||||||||
|<br>Unemployed|0.20|0.40|0.17|0.38|0.14|0.35|0.21|0.41|
|<br>Employee in a firm|0.25|0.43|0.31|0.46|0.41|0.50|0.36|0.48|
|Self employed|0.06|0.24|0.07|0.25|0.10|0.31|0.01|0.12|
|Casual workers|0.16|0.37|0.22|0.42|0.06|0.24|0.18|0.39|
|Student|0.22|0.41|0.16|0.37|0.24|0.43|0.15|0.36|
|Government Employee|0.04|0.21|0.04|0.19|0.02|0.14|0.07|0.26|
|<br>Inactive|0.01|0.08|0.00|0.00|0.02|0.14|0.00|0.00|
|**_Instruments for educational at_**|**_tainment_**||||||||
|<br>Distance to school (log)|<br>3.34|0.75|3.19|0.71|3.20|0.65|3.12|0.66|
|<br>Father was a farmer|0.37|0.48|0.24|0.43|0.20|0.41|0.32|0.47|
|Father was a civil servant|0.21|0.41|0.32|0.47|0.31|0.47|0.30|0.46|
|Mother was a farmer|0.15|0.36|0.11|0.31|0.10|0.31|0.06|0.24|
|Mother was a housewife|0.64|0.48|0.62|0.49|0.61|0.49|0.68|0.47|
|Complete observations|310||224||48||64||

Note: All monetary measures in Ethiopian Birr (ETB). 1 USD dollar is worth approximately 9.1 Ethiopian Birr (ETB)

-16-
households in treated villages.<sup>23</sup> Second, we use information from the operational followup survey in order to construct the share of program recipients in treated villages that received the E1 form as of May 1999.

In order to maintain full comparability with the estimates of program spillover reported in the rest of the paper, we estimate at the village-level the linear model reported in equation (1) using those program implementation measures as dependent variables on the sample of treated villages that we consider in the main empirical analysis.<sup>24</sup> The dependent variables are time-invariant, and, hence, we match this information with only the first round (1998) of the data of the program roll-out. As documented in columns 1–3 of table 9, these three measures of efficiency of program implementation are unrelated to the frequency of the treatment in the areas surrounding evaluation villages.

We next use the yearly secondary school census in order to construct school supply aggregate measures for the 358 evaluation neighborhoods in our sample (with both treated and control villages as centroid), and estimate at the neighborhoodlevel the linear model reported in equation (1) over the two years of the program roll-out (1998–1999). In all specifications, we control for the baseline (1997) value of the dependent variable. The OLS estimates are reported in panel B of table 9, which are very small in magnitude and not statistically different from zero.

23. While food stipends were distributed to all villages assigned to the treatment group at the same time in March 1998, only 56 percent of the those localities received the first scholarship transfer in March 1998; 36 percent received them two months later, and the remaining eight percent six months or more after incorporation into the program. These administrative delays appear concentrated in some regions, notably in the states of Queretaro and San Luis Potosi. As a further check, we have re-estimated equation (1) without those two states. Results (available upon request) are very similar to those reported in table 3.

24. Results (available upon request) barely change when we consider instead the entire sample of 320 treated villages. Due to a few missing values in the additional data sources that we employ, we lose some village-level observations in the regressions displayed in panel A of table 2: three villages for scholarship disbursements, four villages for school supply disbursements, and seven villages for the receipt of the school attendance (E1) form.

28
Policy Research Working Paper 8653

# **Abstract**

What goes on inside the classroom is central to student learning. Despite its importance, low and middle-income countries rarely measure teaching practices, in part due to a lack of access to adequate classroom observation tools and the high transaction costs associated with administering them. _Teach_ , a new, open-source classroom observation tool for primary classrooms, was developed to capture the quantity and quality of teaching practices in these settings with a simple, easy-to-administer tool. This paper validates the use of _Teach_ scores for system diagnostics by providing four types of evidence. First, it provides evidence that the practices included in the tool have a clear conceptual underpinning. Second, almost 90 percent of local observers in Mozambique, Pakistan, the Philippines, and Uruguay were highly accurate using _Teach_ after a four-day training. Third, using data from 845 classrooms in Pakistan, the paper shows that Teach scores are internally consistent, present moderate to high inter-rater reliability in the field (.75 intraclass correlation coefficient), and provide substantial information that allows to differentiate teachers, even those with similar but not equal scores. Finally, teachers who display effective practices, as measured by Teach, are associated with students who achieve higher learning outcomes.

This paper is a product of the Education. 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://www.worldbank.org/research. The authors may be contacted at molina@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