rafmacalaba/datause-encoder-data huggingface dataset:
||Model 6|Model 7|Model 8|Model 9|
|---|---|---|---|---|
|Intercept|0_._10|0_._16|0_._16|**0**_._**19**|
||[_−_0_._11; 0_._32]|[_−_0_._02; 0_._34]|[_−_0_._01; 0_._34]|[0_._00; 0_._38]|
|EA: Mean War Experience|0_._06<br>|0_._06<br>|0_._07<br>|0_._06<br>|
||[_−_0_._06; 0_._17]|[_−_0_._06; 0_._18]|[_−_0_._06; 0_._18]|[_−_0_._06; 0_._18]|
|EA: Distance to Chiefdom HQ|0_._00|0_._00|0_._00|0_._00|
||[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|
|EA: Distance to District HQ|0_._00|0_._00|0_._00|0_._00|
||[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|
|EA: Distance to Freetown|0_._00|0_._00|0_._00|0_._00|
||[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|
|EA: 2008 % of Gov. Health Care Use|**0**_._**32**|**0**_._**32**|**0**_._**32**|**0**_._**32**|
||[0_._24; 0_._39]|[0_._24; 0_._39]|[0_._24; 0_._39]|[0_._24; 0_._39]|
|EA: 2008 % of Gov. School Use|0_._02|0_._02|0_._01|0_._02|
||[_−_0_._10; 0_._13]|[_−_0_._10; 0_._13]|[_−_0_._10; 0_._12]|[_−_0_._09; 0_._13]|
|EA: 2008 ELF score|0_._07|0_._07|0_._07|0_._08|
||[_−_0_._06; 0_._20]|[_−_0_._06; 0_._20]|[_−_0_._06; 0_._20]|[_−_0_._05; 0_._21]|
|EA: 2008 Mean Poverty Score|_−_0_._04|_−_0_._03|_−_0_._03|_−_0_._03|
||[_−_0_._17; 0_._08]|[_−_0_._16; 0_._08]|[_−_0_._16; 0_._08]|[_−_0_._16; 0_._09]|
|EA: 2008 Dist. to Passable Road|0_._00|0_._00|0_._00|0_._00|
||[0_._00; 0_._01]|[0_._00; 0_._01]|[0_._00; 0_._01]|[0_._00; 0_._01]|
|EA: 2008 Mean Agricultural Work|0_._07|0_._05|0_._06|0_._03|
||[_−_0_._06; 0_._23]|[_−_0_._07; 0_._19]|[_−_0_._06; 0_._20]|[_−_0_._11; 0_._17]|
|EA: 2008 Mean No. of School-Age Children|0_._00|0_._00|0_._00|0_._00|
||[_−_0_._01; 0_._00]|[_−_0_._01; 0_._00]|[_−_0_._01; 0_._00]|[_−_0_._01; 0_._00]|
|EA: 2007 Malaria Treatment|0_._05<br>[_−_0_._09; 0_._17]|0_._05<br>[_−_0_._08; 0_._17]|0_._05<br>[_−_0_._09; 0_._17]|0_._05<br>[_−_0_._08; 0_._17]|
|EA: 2007 PC Visit|0_._05|0_._04|0_._05|0_._04|
||[_−_0_._01; 0_._10]|[_−_0_._01; 0_._10]|[_−_0_._01; 0_._10]|[_−_0_._01; 0_._10]|
|EA: 2007 LC Visit|0_._00|0_._00|0_._00|0_._00|
||[_−_0_._05; 0_._05]|[_−_0_._06; 0_._05]|[_−_0_._06; 0_._05]|[_−_0_._06; 0_._05]|
|EA: 2007 Well in Community|0_._00|0_._00|0_._01|0_._01|
||[_−_0_._05; 0_._06]|[_−_0_._05; 0_._06]|[_−_0_._05; 0_._06]|[_−_0_._05; 0_._06]|
|LC: % Female|0_._17||||
||[_−_0_._42; 0_._78]||||
|LC: % CDF Participation||_−_0_._18|||
|||[_−_0_._81; 0_._45]|||
|LC: % SLPP|||0_._00||
||||[_−_0_._01; 0_._00]||
|LC: Competition||||_−_0_._05|
|||||[_−_0_._13; 0_._03]|
|BIC|426.78|426.74|435.95|429.70|
|Num. obs.|607|607|607|607|
|Num. groups: Local Council|19|19|19|19|
|Variance: Local Council (Intercept)|0.01|0.01|0.01|0.01|

Coefficients with _p <_ 0 _._ 05 in **bold** Table 8: DV: 2011 EA-level percentage of households with school age children that report that their children attend government schools. Multi-level linear models with random intercepts at the council level level.
- 30 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

the one reported by Fallah et al (2010), 0.61 vs. 0.63; as well as their standard deviation 0.18 vs. 0.12.

Finally, surprisingly and as shown in the results summarized in Table 7, we do not find evidence of the existence of economies of agglomeration or congestion costs, since the variables population and population<sup>2</sup> are not significant. After performing an outlier analysis,<sup>10</sup> we found that these specific results were driven by eight extreme outliers: Ciudad de México (México), São Paulo (Brazil), Buenos Aires (Argentina), Rio de Janerio (Brazil), Santiago (Chile), Lima (Peru), Bogotá (Colombia), and Guadalajara (México), which populations are extremely high compared to the rest of the cities. After dropping these outliers from the sample, the coefficient of _population_ becomes positive and statistically significant, showing that higher levels of population are associated to higher levels of productivity; and the coefficient of _population_<sup>_2_</sup> becomes negative and significant, indicating the presence of congestion effects on productivity. The conclusions regarding hypotheses **H1** to **H6** remain unchanged. The results of the estimations without outliers are presented in Table A3.

**<u>Table 6.</u>** <u>Correlation matrix among urban form variables</u>

||<br>(1)|<br>(2)|<br>(3)|<br>(4)|<br>(5)|<br>(6)|(7)|
|---|---|---|---|---|---|---|---|
|1996 norm. exchange (1)|1|||||||
|1996 norm. perimeter (2)|0.7453*|1||||||
|1990 fullness (3)|-0.1573*|-0.3100*|1|||||
|1996 intersection density (4)|0.1427*|-0.0309|0.3429*|1||||
|1996 street density (5)|0.1527*|-0.0465|0.4361*|0.9470*|1|||
|1996 circuity (6)|-0.1993*|-0.1172*|-0.0586|-0.3901*|-0.4351*|1||
|1990 sprawl (7)|0.0422|0.2216*|-0.7122*|-0.5454*|-0.5939*|0.2032*|1|

Note: * Denotes significance at 5%

# _5.2 Strategy 2: Instrumental variables_

The implementation of instrumental variables techniques was carried out using the two-stage least squares estimator (2SLS), allowing intra-group correlation at the country level. In Table 8 we report the 2SLS estimations, which use the log of normalization of potential urban form

> 10 For the outliers identification, we used the algorithm BACON (Blocked adaptive computationally efficient outlier nominators) devised by Billor et al (2000) and implemented in STATA by Weber (2010).
# **Figure 1**

## **Gross capital flows by country group (percent of trend GDP)**

<!-- Start of picture text --> (a) All countries<br>CIF<br>COD<br>1978 1983 1988 1993 1998 2003 2008 2013 2018<br>year<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br><!-- End of picture text -->

## **(a) Advanced countries**

<!-- Start of picture text --> CIF<br>COD<br>1978 1983 1988 1993 1998 2003 2008 2013 2018<br>year<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br><!-- End of picture text --> 44
# Contents

|1. Introduction .............................................................................................................................................. 3|
|---|
|1.1 What are global value chains? Why are they important for development? ...................................... 3|
|1.2 A new dataset for the study of archetypal value chains ..................................................................... 3|
|1.3 Principal findings ................................................................................................................................. 4|
|2. Construction of the MC‐GVC database ..................................................................................................... 5|
|3. Analysis of Trends in GVC Trade Participation .......................................................................................... 7|
|3.1 Trends in GVC Participation by Product Categories, World Regions and Income Groups ................. 7|
|3.2 Trends in Intra‐regional and Inter‐regional GVC Trade .................................................................... 10|
|3.3 Trends in GVC and Non‐GVC Imports and Exports by World Regions, Product Categories and<br>Stages of Production ............................................................................................................................... 10|
|4. Analysis of Trends in GVC Trade Sophistication and Concentration ...................................................... 11|
|4.1 Sophistication Analysis ...................................................................................................................... 11|
|4.2 Concentration Analysis ..................................................................................................................... 13|
|5. Conclusions ............................................................................................................................................. 13|
|References................................................................................................................................................... 14|
|Annex 1 Description of the Dashboard Approach and Its Integration into the Online GVC Module on WITS<br>.................................................................................................................................................................... 37|

2
Zilibotti, 2017).<sup>5</sup> We also contribute to a broader literature across the social sciences about the importance of early childhood and the long-term impact of parenting practices (Schady, Galiani, & Souza, 2006). For example, the Lancet series on child development assessed a variety of early interventions to improve equity in child development. These reviews find that improvements in parenting practices (such as more responsiveness in feeding infants and young children; encouraging learning, book reading, play activities; using positive discipline; and problem-solving related to children’s development, care, and feeding) had positive effects on development (M. M. Black et al., 2017; Engle et al., 2007; S. P. Walker et al., 2011).

Our results on child health outcomes fit well within the literature focused on maternal education in developing countries (Desai & Alva, 1998) as well as with the evidence uncovered in Akresh et al. (2018) and Mazumder et al. (2019) on the same INPRES program. The findings on performance on standardized tests in language and sections of the one in math conform with findings presented in Andrabi et al. (2012) for the Pakistani context, even though our results on child time use (or daily activities) are not exactly in line with the reasoning presented there and also those resulting from a direct early-childhood intervention (Attanasio, Cattan, Fitzsimons, Meghir, & Rubio-Codina, 2020). The findings regarding socio-emotional development and school-readiness are novel in this literature and provide strong causal evidence of the intergenerational impact of educational interventions on children’s developmental landmarks.

# **2 Data and context**

## **2.1 Data sources and sample restrictions**

The data used in this paper draw on information collected in the course of an impact evaluation of the Indonesia Early Childhood Education and Development (ECED) Project. The project expanded access to early childhood education and the evaluation focused on the effects on children’s school

> 5Economics of parenting includes seminal work by Weinberg (2001), which focuses on the use of physical punishment, and provides stylized facts and argues that parenting should be factored into economic models of child development. More recent empirical work includes Jenkins and Handa (2019).

5
Table 1: Adolescent girls’ characteristics at baseline

||Control|T1|T1 + Leaders|T2|T2 + Leaders|T3|T3 + Leaders|T4|F-test|
|---|---|---|---|---|---|---|---|---|---|
|Adolescent’s age|18.98|18.81|19.07|19.20|18.73|18.85|18.46|18.93|0.94|
||[0.15]|[0.27]|[0.18]|[0.18]|[0.21]|[0.21]|[0.26]|[0.13]||
|Above 18 years old|0.67|0.67|0.71|0.72|0.66|0.65|0.62|0.66|1.48|
||[0.02]|[0.03]|[0.02]|[0.02]|[0.03]|[0.02]|[0.03]|[0.02]||
|Above 15 years old|0.87|0.84|0.88|0.89|0.84|0.86|0.83|0.86|1.25|
||[0.01]|[0.03]|[0.02]|[0.02]|[0.02]|[0.02]|[0.01]|[0.01]||
|Level of education [0–4]|0.34|0.27|0.32|0.38|0.32|0.36|0.28|0.34|0.27|
||[0.04]|[0.08]|[0.08]|[0.12]|[0.07]|[0.07]|[0.06]|[0.06]||
|Ever schooled|0.23|0.16|0.22|0.23|0.20|0.24|0.20|0.22|0.74|
||[0.02]|[0.03]|[0.03]|[0.06]|[0.03]|[0.04]|[0.04]|[0.03]||
|Ever pregnant<br>13|0.63|0.65|0.62|0.67|0.60|0.62|0.58|0.61|0.83|
||[0.02]|[0.03]|[0.03]|[0.03]|[0.03]|[0.02]|[0.04]|[0.02]||
|Ever married or cohabited|0.62|0.57|0.59|0.63|0.62|0.57|0.54|0.57|1.02|
||[0.02]|[0.04]|[0.04]|[0.04]|[0.03]|[0.03]|[0.05]|[0.04]||
|Engaged in IGA|0.66|0.67|0.65|0.71|0.77|0.77|0.81|0.68|2.37<sup>_∗∗_</sup>|
||[0.03]|[0.04]|[0.05]|[0.05]|[0.06]|[0.05]|[0.04]|[0.04]||
|Remunerated IGA|0.18|0.15|0.14|0.22|0.20|0.18|0.17|0.18|0.49|
||[0.02]|[0.03]|[0.03]|[0.03]|[0.04]|[0.04]|[0.03]|[0.03]||
|Reported profts|0.30|0.28|0.28|0.33|0.38|0.36|0.38|0.31|0.97|
||[0.02]|[0.06]|[0.05]|[0.05]|[0.05]|[0.05]|[0.06]|[0.03]||
|Monthly profts (FCFA)|5514.19|5130.08|4961.36|5634.14|7614.85|6646.75|6140.00|5556.68|1.01|
||[569.51]|[1106.44]|[826.86]|[870.78]|[1142.35]|[1053.19]|[840.23]|[538.89]||
||Control|T1|T1+Leaders|T2|T2+Leaders|T3|T3+Leaders|T4|F-test|
Figure 6: Pre- and post-crisis growth in U.S. trade (B) Import growth 12

# (A) Export growth

<!-- Start of picture text --> Percent 2002-08 2010-14 Percent 2002-08 2010-14<br>18 18<br>16 16<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>Arm's- Intra- Arm's- Intra- Arm's- Intra- Arm's- Intra-<br>length firm length firm length firm length firm<br>AEs EMDEs AEs EMDEs<br>(C) Contribution to average annual (D) Contribution to average annual<br>export growth import growth<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points AEs EMDEs Percentage points AEs EMDEs<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>2002-08 2010-14 2002-08 2010-14 2002-08 2010-14 2002-08 2010-14<br>Arm's- Intra- Arm's- Intra-<br>length firm length firm<br>(E) Contribution to deviation from (F) Contribution to deviation from<br>annual average export growth, change annual average import growth, change<br>2002-08 to 2010-14 2002-08 to 2010-14<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points Percentage points<br>4<br>Country composition 3 Sectoral composition<br>3 Sectoral composition Other<br>Other<br>2<br>2<br>1<br>1<br>0<br>0<br>-1<br>-1<br>-2<br>-2<br>Arm's length Intra-firm<br>Arm's-length Intra-firm<br><!-- End of picture text --> Source: U.S. Census Bureau.

Note: AE stands for advanced economies.

C.D. Average annual contribution of export (C) and import (D) transactions with EMDEs and AEs to total U.S. merchandize exports (C) or imports (D).

E. “Country composition” measures the extent to which growth in arm’s-length or intra-firm exports exceeded that of total exports due to a higher initial share of fast-growing countries. It is defined as the difference between hypothetical arm’s-length (intra-firm) export growth, had arm’s-length (intra-firm) exports to each country grown at the same rate as total exports to each country, and actual total export growth. “Sector composition” measures the extent to which growth in arm’s-length or intra-firm exports exceeded that of total exports because of a higher initial share of fast-growing sectors. It is defined as the difference between hypothetical arm’s-length (intra-firm) export growth, had arm’s-length (intra-firm) sectoral exports grown at the same rate as total sectoral exports, and actual total export growth. “Other” is the residual. The figure shows the change in these contributions between the 2002-08 average and the 2010-14 average.

F. The definitions are as in E. Country composition is omitted since the destination country of all imports is the United States.
infrastructure has witnessed far-reaching reforms over the past years, including a modern new framework for secured transactions in both movable and immovable assets, the establishment of a new, private-led credit bureau collecting both positive and negative information from a variety of providers as well as a new land registry, enhanced enforcement of claims (out of court enforcement of contracts) and a new insolvency law. These reforms were regarded as positive developments by the banking sector and led to an increase in the use of movable assets as security for SME loans and to a general expansion in lending to the sector. Both the registration and enforcement of collateral have become more efficient, lowering the costs of lending. In addition, a unique ID was introduced, allowing for easy identification and tracking of borrowers.

**Table 3: Enabling environment indicators across countries**

||**Kenya**|**Nigeria**|**Rwanda**|**South Africa**|**Tanzania**|
|---|---|---|---|---|---|
|Private credit bureau<br>coverage (% of adults)*|4.9|4.1|7.1|54.0|0.0|
|Positive and<br>negative data<br>shared?|No|Yes|Yes|Yes|n/a|
|Depth of credit<br>information index*<br>(out of 6)|4|4|6|6|0|
|Unique ID perceived as<br>reliable?|Yes|No|Yes|Yes|No|
|Movable collateral<br>registry functional?|Yes|No|Yes|No|No|
|Days required to<br>enforce a contract*|465|457|230|600|462|

Source: SME finance surveys. *Data from Doing Business 2013.

The enabling environment is equally conducive in South Africa, but lags far behind in Nigeria, Tanzania, and even in Kenya. While Nigerian credit bureaus have been building up their credit records considerably in recent years, only commercial banks submit credit information and that irregularly. There is no movable collateral registry in Nigeria and the enforcement of contracts takes time and is costly, with legal proceedings potentially taking many years to complete. Creditor information in Tanzania is weak, with no functional credit referencing system in place, no movable collateral registry, and no reliable national ID. The difficulties associated with the collateralization and realization of security in Tanzania were perceived by banks as one of the most constraining factors for an expansion of SME lending.

12
an interdistrict ("between") component and an intradistrict ("within") component. Galasso and Ravallion show that the national targeting coefficient can be decomposed as follows, <!-- Start of picture text --> Tnpd (npd IT ((nd-n)(nd-<br>d=1 N np n P d=1 N npn-p<br><!-- End of picture text -->

# Within Between District District

where _Nd_ is the number of households in district _d, N_ is the overall number of households, and _ND_ is the number of districts. Thus the intradistrict contribution to overall targeting is simply the weighted average of the district targeting coefficients where the weights are the product of the district population share, the share of all the poor in the country who live in the district, and the share of all the nonpoor in the country who live in the district. To interpret these weights, consider the extremes. If the entire population of a particular district is poor, then the weight place on this district is zero (the share of the nonpoor is zero, _n-pd_ = 0) because intradistrict targeting is irrelevant to overall targeting. The targeting of the district itself is what matters. Conversely, if the entire population in a district is nonpoor, the weight place on the district is also zero _(np,d =_ **_O)_** by the same reasoning.

These targeting coefficients and decompositions are estimated using both poverty and malnutrition as targeting criteria. The poverty targeting coefficients are estimated using the ENNIV and ENAHO data where households are defined as poor if their incomes fall below the official poverty line. The malnutrition targeting coefficients are estimated using the ENNIV and DHS data where children are defined as malnourished if their height-for-age z- scores are less than -2.

The Galasso and Ravallion targeting coefficient, however, is defined over the shares of households/individuals participating in the VL program, not over the shares of benefits accruing to the different groups. This follows from their assumption of equal transfer values to all recipients, an assumption in keeping with the program they were investigating. When we have differential transfer values for each of the recipient households, we need to redefine the targeting measure and how to decompose it. We thus develop an extension to the Galasso and Ravallion methodology, and apply it to the 1997 ENNIV data. Begin by defining the average transfer received by the poor, <!-- Start of picture text --> VLp= VLp<br>Np i=1<br><!-- End of picture text --> where, _VLp,,_ is the value of the VL transfer received by a poor household i, and _Np_ is the total number of poor households. The average transfer received by the nonpoor is defined analogously as, 6
The vertical size of the rectangle for each country in Figure 4 is proportional to the size of the population. Madagascar has the largest estimated extreme poverty rate for children, followed by the Democratic Republic of Congo, Malawi, Guinea-Bissau, and Zambia. At the bottom are the high income countries included in the sample for ‘developing countries’. India stands out due to its large population and high estimated incidence of extreme and moderate poverty rates for 2013 – respectively 22 percent and 39.6 percent – among children.

Poor children tend to live in large households, as two out of three extremely poor children live in households with six or more members; more than one-third in large households of 8 or more (Table 11). The poor are overrepresented in the latter group, which contains less than 20 percent of the child population.

**Table 11: Extreme child poverty by household size**

|**Household size**<br>**(number of**<br>**members)**|**Extreme poor**<br>**children**<br>**(millions)**|**Children**<br>**headcount**<br>**poverty rate**|**Share of**<br>**extremely**<br>**poor children**<br>**(%)**|**Share of**<br>**child**<br>**population in**<br>**sample (%)**|**Percentage**<br>**of children in**<br>**group**|
|---|---|---|---|---|---|
|Three or less|13.9|5.5|4.2|14.9|15.5%|
|Four|33.7|9.0|10.2|22.3|33.8%|
|Five|53.2|16.2|16.2|19.5|36.7%|
|Six|59.4|23.5|18.1|15.0|41.2%|
|Seven|49.1|30.3|15.0|9.6|45.2%|
|Eight|39.3|36.1|12.0|6.5|47.1%|
|Nine|26.3|38.6|8.0|4.1|48.2%|
|Ten or more|53.7|38.8|16.3|8.2|49.1%|
|Total|328.7|19.5|100.0|100.0|32.1%|

Source: GMD, UNDESA, WDI, PovcalNet The increased risk of extreme child poverty from larger household size is however not clearly associated with ‘multi-generational’ households in which elderly people co-reside with their adult children and grandchildren, a far more common practice in developing countries. Table 12 shows that extreme child poverty rates do not differ greatly according to whether children live solely with prime age adults (often their parents), or in three-generation households, or live solely with elderly people. However, over threequarters of extreme child poverty (78.5 percent) is in two-generation households, suggesting that large households in this group are a major factor in extreme poverty risk. Only a fifth of extremely poor children live in three-generation households. Overall, these patterns highlight the importance of the sensitivity tests of our results to assumptions about economies of scale of households’ expenditure in the next section.

14