rafmacalaba/datause-encoder-data huggingface dataset:
agencies do not generally track the domestic value added of goods that their countries trade, nor do they track the use of these traded goods, that is, whether they are used for sales to final consumers, whether they are used for further processing in a specific industry, and what share of this industry’s output is exported.

In the field of international economics, scholars have used a variety of approaches to gain insights into the structure of GPNs. One method has been to rely on the highly disaggregated product codes and descriptions in international trade statistics to classify traded goods according to their main use. Yeats (2001) and Ng and Yeats (2001), for example, categorized intermediate goods as those products whose description include the words “parts” or “components”. Lemoine and Ünal-Kesenci (2004) and Zebregs (2004) used the United Nation's "Broad Economic Categories" (BEC) classification to distinguish between intermediate and final goods. While this approach has been useful to demonstrate the large and growing role of GPNs in international trade, it faces two important shortcomings. First, classifying goods according to their product codes is somewhat arbitrary since product descriptions provide insufficient information to identify a product’s main use (Hummels, Ishii and Yi, 2001). Indeed, some goods, such as _tires,_ can be used both as a final good by consumers and as an intermediate good by car manufacturers. Second, even if traded goods were correctly classified as intermediate or final goods, international trade data do not identify in which sector intermediate goods are used, if it is processed for domestic consumption, or if it is used for export purposes. This makes it difficult to accurately link a trade flow with other trade flows within the same GPN.

An alternative approach used to map activities within GPNs is to combine international trade data with input-output (IO) table data. The advantage of IO data is that – for domestic activities – it unambiguously defines intermediate inputs by their use, i.e., in which industry they are put to use and what share of the industry’s output is exported. This information on main use, however, is not available for imported goods in many input-output tables . Lacking this information, researchers have adopted the purpose, they have relied on lists of components and their factory prices from industry analysts’ “teardown” reports, which capture the composition of the product at a specific point in time.

6
**Figure 1.10. Short-Term Income Growth Rate for Immobile Individuals, Russia 1994-2015**

<!-- Start of picture text --> 170<br>145<br>120<br>95<br>70<br>45<br>20<br>First quntile<br>-5 Second quntile<br>Third quntile<br>-30<br>Forth quntile<br>-55 Fifth quntile<br>1994-1998 1998-2004 2004-2009 2009-2015<br>Growth rate(%)<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita. The distribution by 5 quantiles is based on the cross-sectional sample for each year, then the quintile thresholds are assigned to panel sample. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year.

69
risk-sharing mechanisms (which typically operate through gift giving or interest-free loans) are less available to landless households than to other households. Finally, the last four rows of Table 1 show that landless households are also economically disadvantaged in terms of their ownership of productive assets aside from land, and have lower material well-being in terms of ownership of durable consumption goods, housing quality, and consumption expenditures.

# _4.2 Balance at baseline_

In order to establish the validity of the randomized assignment to early rollout, we test for pretreatment balance across treatment and control groups in the primary outcomes of interest, as specified in the pre-analysis plan, and other important household characteristics. Differences in means for each variable are estimated through linear regressions in which controls for stratification variables are included and standard errors are clustered at the panchayat level. Results, shown in table A1 (column 1), indicate that households in treatment areas are 2 percentage points more likely to include an SHG member. This difference, while small, is significantly different from zero at the 5% level. Relatedly, the mean outstanding SHG debt is also higher in treatment areas at the 5% level, though the difference is small in economic terms (50 rupees). Monthly interest rates measured at the household level are higher in treatment areas, and this appears to be driven by informal lending rates. Informal lending rates measured in the FGD data, on the other hand, are well balanced. An index of productive assets, constructed according to the method proposed in Filmer and Pritchett (2001), is 0.1 points lower (0.06 of a standard deviation) in treatment areas as well. We also test for differences among landless and landed households in treatment versus control areas (columns 2 and 3). Again, we find small differences in SHG enrollment and debt, and in asset holdings across treatment and control groups. The difference in interest rates reported at the household level appears to be driven by landless households. Attrition, at 3%, is low given the three-year interval between surveys, and does not differ between treatment and control areas.

# _4.3 Direct impacts: SHG membership and borrowing_

Table 2 presents estimates of the impact of access to Jeevika on SHG membership and outstanding debt to SHGs; overall loans taken, amount borrowed and interest rates. Results shown in columns 1 and 2 of Panel A indicate that over 60% of households in treatment areas had at least one member in a self-help group by follow-up, compared to 10% of households in control areas. Given the
|P050668|2002|2006|12/2/2005|7 Closed|India|
|---|---|---|---|---|---|
|P050668|2002|2006|2/28/2006|8 Closed|India|
|P050668|2002|2007|10/4/2006|9 Closed|India|
|P050668|2002|2007|4/11/2007|10 Closed|India|
|P050668|2002|2008|8/20/2007|11 Closed|India|
|P050668|2002|2008|4/4/2008|12 Closed|India|
|P050668|2002|2009|9/18/2008|13 Closed|India|
|P050668|2002|2009|3/12/2009|14 Closed|India|
|P050668|2002|2010|9/17/2009|15 Closed|India|
|P050668|2002|2010|4/15/2010|16 Closed|India|
|P050668|2002|2011|11/21/2010|17 Closed|India|
|P050668|2002|2011|5/27/2011|18 Closed|India|
|P050668|2002|2011|6/27/2011|19 Closed|India|
|P050668|2002|2021|7/9/2020|20 Closed|India|
|P050671|2002|2002|6/27/2002|2 Closed|Nepal|
|P050671|2002|2003|5/23/2003|5 Closed|Nepal|
|P050671|2002|2004|3/10/2004|8 Closed|Nepal|
|P050671|2002|2005|5/25/2005|11 Closed|Nepal|
|P050671|2002|2006|10/5/2005|12 Closed|Nepal|
|P050671|2002|2006|6/20/2006|13 Closed|Nepal|
|P050671|2002|2007|11/3/2006|14 Closed|Nepal|
|P050671|2002|2007|5/3/2007|15 Closed|Nepal|
|P050671|2002|2008|11/27/2007|16 Closed|Nepal|
|P050671|2002|2008|3/31/2008|17 Closed|Nepal|
|P050671|2002|2008|6/30/2008|18 Closed|Nepal|
|P050671|2002|2009|11/6/2008|19 Closed|Nepal|
|P050671|2002|2009|6/30/2009|20 Closed|Nepal|
|P050671|2002|2010|8/31/2009|21 Closed|Nepal|
|P050706|2000|2000|6/17/2000|1 Closed|Yemen,Republic of|
|P050706|2000|2001|2/28/2001|2 Closed|Yemen,Republic of|
|P050706|2000|2002|4/10/2002|5 Closed|Yemen,Republic of|
|P050706|2000|2003|6/16/2003|9 Closed|Yemen,Republic of|
|P050706|2000|2004|6/30/2004|11 Closed|Yemen,Republic of|
|P050706|2000|2005|5/4/2005|13 Closed|Yemen,Republic of|
|P050706|2000|2006|6/26/2006|14 Closed|Yemen,Republic of|
|P050706|2000|2007|6/22/2007|15 Closed|Yemen,Republic of|
|P050706|2000|2008|12/3/2007|16 Closed|Yemen,Republic of|
|P050706|2000|2009|9/23/2008|17 Closed|Yemen,Republic of|
|P050706|2000|2010|2/2/2010|18 Closed|Yemen,Republic of|
|P050706|2000|2011|12/18/2010|19 Closed|Yemen,Republic of|
|P050713|1998|1998||1 Closed|Argentina|
|P050713|1998|1999||4 Closed|Argentina|
|P050713|1998|2000|3/8/2000|6 Closed|Argentina|
|P050713|1998|2001|6/27/2001|9 Closed|Argentina|
|P050713|1998|2002|6/25/2002|13 Closed|Argentina|
|P050713|1998|2003|6/19/2003|16 Closed|Argentina|
|P050713|1998|2004|6/21/2004|18 Closed|Argentina|
17 Hertel, T.W., W.A. Masters and A. Elbehri (1998), ‘The Uruguay Round and Africa: A Global General Equilibrium Analysis’, _Journal of African Economies_ 7(2): 20834, June.

Hossain, F., C.E. Pray, Y. Lu, J. Huang, C. Fan and R. Hu (2004), ‘Genetically Modified Cotton and Farmers’ Health in China’, _International Journal of Occupational and Environmental Health_ 10: 307-14.

- Huang, J., R. Hu, H. van Meijl and F. van Tongeren (2004), ‘Biotechnology Boosts to Crop Productivity in China: Trade and Welfare Implications,’ _Journal of Development Economics_ 75(1): 27-54, October.

- ICAC (2005), _TheOutlook for Cotton Supply in 2005/06_ , Secretariat of the International Cotton Advisory Committee (ICAC), Washington DC, September.

James, C. (2005), _Global Review of Commercialized Biotech/GM Crops: 2005_ , Brief No.

- 34, International Service for the Acquisition of Agri-biotech Applications, Ithaca NY.

- King, D.K. (2003). _GM Science Review: First Report._ Prepared by the GM Science Review Panel under the chairmanship of Sir David King for the UK Government, London.

- Marra, M., P. Pardey and J. Alston (2002), ‘The Payoffs to Agricultural Biotechnology: An Assessment of the Evidence’, _AgBioForum_ 5(2): 43-50. Downloadable at -

- <u>http://www.agbioforum.org/v5n2/v5n2a02 marra.pdf</u>

Masters, W.A. (2005), ‘Paying for Prosperity: How and Why to Invest in Agricultural R&D for Development in Africa’, _Journal of International Affairs_ 58(2): 35-64, Spring.
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 14. Operational indicators for the NEC**

||Net generation|System<br>losses|Implicit<br>collection ratio|Average total<br>cost|Average<br>effective tariff|Total hidden<br>costs|Total hidden<br>costs|
|---|---|---|---|---|---|---|---|
||(GWh/year)|(%)|(%)|($/kWh)|($/kWh)|($ million/year)|(% revenues)|
|2003|3,560|34.8|100|0.16|0.08|306|175|
|2004|3,749|33.4|100|0.16|0.09|293|129|
|2005|4,124|26|100|—|—|_|—|
|2006|4,521|24|100|0.16|0.092|340|96|
|2007|5,021|22|100|0.20|0.092|528|113|
|2008|5,506|22|100|0.19|0.098|484|96|

_Source:_ AICD analysis based on World Bank (2007) and Sudan Central Bureau of Statistical Handbooks. — = Not available.

Some progress has been made, however. Hidden costs as a share of utility revenue fell substantially from 175 percent of NEC’s revenues in 2003 to 96 percent in 2008 (figure 22). This is largely attributable to significant reduction in network losses, which were as high as 35 percent in 2003 but were reduced to 22 percent by 2008. While tariffs have risen slightly, they have not kept pace with the rising costs of production Nevertheless, a 60 percent expansion of power production all sold at a loss, has inflated absolute hidden costs from $306 million in 2003 to $484 million in 2008 Expressed in terms of the size of the economy, these hidden costs shaved off 2.2 percent of GDP in 2003, falling to 1.5 percent of GDP in 2008.

**Figure 22. Large hidden costs at the NEC due to underpricing and unaccounted losses**

<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>2003 2004 2006 2007 2008<br>Unaccounted Losses UnderPricing Collection Inefficiencies<br>(%)<br>Hidden costs as a share of revenue<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009) using data from World Bank (2007) and Sudan Central Bureau of Statistics handbooks.

The NEC’s hidden costs look high when compared to that of other African utilities (figure 23). They represent about 100 percent of the NEC’s revenues and are double that of the top-performing countries such as Senegal, Côte d’Ivoire, and Benin. Nevertheless, they are far from being the worst in Africa.

> <sup>32</sup>
interest is that “play-time” for both enrolled and out-of-school children is the single largest time-use category of these three in the children’s daily lives. Out-of-school children report 278 minutes per day but even enrolled children report more than 150 minutes per day of play time. This play-time for enrolled children leaves plenty of “idle” time that can be spent in extra educational work outside the school, without eating into either their house or paid-work commitments. This is very much in line with Bacolod and Ranjan’s (2008) emphasis on idle time as a third category to be taken into account when discussing the tradeoffs between child labor and enrollment.

The age of the child has a clear association with time-use patterns in these data and Figure 1 explores the variation in time-use by the child’s age and the educational status of the mother. There are several noteworthy patterns. First, consistent with other studies of time-use (deTray, 1983) children spend more time on housework and on paid-work as they age; they also spend less time on play so that, by the time they are 15 years old, play-time has dwindled to less than 60 minutes compared to 300 minutes when they were 5. For children in their teenage years, the burden of housework is quite high. In our companion work, Andrabi and others (2009), we discuss the issue of housework and show that this increase in housework is largely concentrated among teenage girls who are out-of-school. This issue demands a separate, more focused explanation. Second, children of educated mothers spend less time on housework, paid-work and play-time, largely because the gradient of time spent in housework and paid-work with age is lower for them. Third, these children spend significantly more time across all ages on educational work outside the school. The difference is consistent across ages averaging about 40 more minutes a day up to age 10 and more so after that—for children with uneducated mothers, homework time declines quite sharply after age 10, while it remains constant for children with educated mothers. These data on the time that children spend in study at home combined with the relatively small direct involvement of mothers’ time on their children’s education suggest that mothers with some level of education, even when they are not directly involved in a child’s home study, are creating a space for these children to focus on their schoolwork.

To assess the link between maternal education and child outcomes, we also used school-based testing to assess all enrolled children in Grade 3 in the village. These children were tested in the subjects of English, 11
at a rate of 37 percent of mean expenditure as the country grows richer.<sup>7</sup> Atkinson and Bourguignon suggest that this line has greater relevance for poorer countries than a relative line like 50 percent of median income or consumption (as used in much of the European Union and proposed for global poverty measurement by Garroway and de Laiglesia, 2012) because the floor set at $1 prevents the poverty line from taking values that are much lower than observed for poor countries of the world. And, it is also the case that their line has greater relevance than the absolute international poverty line for richer countries.

Ravallion and Chen (2011) offer a relative poverty line, similar to Atkinson and Bourguignon, that sets _Z*_ =$1.25 in 2005 PPP terms, _α=$0.60, k_ =0.33 and _M_ = mean per capita expenditure from national accounts. Chen and Ravallion (2013) propose a variant of this defining _M_ in terms of mean consumption (or income) from household survey data, and setting _α=$0.625_ and _k_ =0.5. The key distinction of both of these proposals from the Atkinson and Bourguignon line (and the relative line used by EU/OECD and Garroway and de Laiglesia) is that the elasticity of the poverty line to growth in mean expenditure or consumption is less than one. The importance of this distinction is that it implies that if everyone gets better off by some fixed proportion, then the aggregate measure of poverty must fall. Ravallion and Chen (2011) refer to global poverty lines that respect this property as being _weakly-relative_ poverty lines. This attribute is in contrast to a _strongly-relative_ poverty line such as that used by the European Union where _α_ =0 and _k_ is frequently equal to a half.<sup>8</sup>

> 7 They base this parameterization on examining the 33 national poverty lines reported in Ravallion, Datt and van de Walle (1991).

> 8 Ravallion and Chen (2011) note that the Atkinson and Bourguignon relative poverty line will also not satisfy their weakly relative axiom.

7
**Table 3: Effort in the public and private sectors**

|**(1)**<br>**Repre**|**(2)**<br>**sentative sa**|**(3)**<br>**mple**|**(4)**<br>**Dual**|**(5)**<br> **practice sa**|**(6)**<br>**mple**|
|---|---|---|---|---|---|
||Percentage|||Percentage||
|Time Spent<br>(mins)|<br>of checklist<br>items|IRT score|Time Spent<br>(mins)|<br>of checklist<br>items|IRT score|
|**Panel A: SP and case fixed effects**||||||
|Is a private provider<br>1.222***|6.758***|0.512**|1.471***|8.888***|0.729***|
|(0.250)|(2.488)|(0.211)|(0.267)|(1.762)|(0.178)|
|R-squared<br>0.305|0.160||0.237|0.219||
|Number of observations<br>662|662|233|331|331|138|
|Mean of public<br>2.388|15.287||1.562|17.677||
|Mean of private<br>3.703|22.302||2.965|28.223||
|Mean of sample<br>3.603|21.764||2.274|23.030||
|**Panel B: SP, case and market/district fixed ef**|**fects**|||||
|Is a private provider<br>1.486***|7.352***|0.608**|1.475***|8.882***|0.729***|
|(0.244)|(1.948)|(0.273)|(0.259)|(1.762)|(0.180)|
|R-squared<br>0.391|0.259||0.258|0.233||
|Number of observations<br>662|662|233|331|331|138|
|**Panel C: SP, case and market/district fixed ef**|**fects**|||||
|Is a private provider<br>1.246***|5.999**|0.500*|1.452***|9.414***|0.770***|
|(0.319)|(2.338)|(0.301)|(0.268)|(1.827)|(0.190)|
|Has MBBS<br>-0.156|3.285|0.043||||
|(0.568)|(2.940)|(0.257)||||
|Has some qualification<br>-0.131|2.518|0.157||||
|(0.299)|(1.716)|(0.151)||||
|Age of provider<br>-0.004|-0.046|0.000|0.005|-0.064|0.004|
|(0.012)|(0.071)|(0.008)|(0.015)|(0.102)|(0.101)|
|Gender of provider (1=Male)<br>0.653|-0.949|0.212|-0.077|-1.383|-0.288|
|(0.544)|(3.529)|(0.327)|(0.386)|(2.639)|(0.309)|
|Patient load during visit<br>-0.096*|-0.144|0.082**|-0.106*|-0.283|0.013|
|(0.052)|(0.554)|(0.040)|(0.062)|(0.424)|(0.517)|
|R-squared<br>0.399|0.259||0.275|0.233||
|Number of observations<br>638|638|221|302|302|126|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. All regressions include a constant. Observations are standardized provider-patient interactions, except in IRT score column where each observation is a provider. The score is computed using all cases, plausible values scores are used. Market fixed effects are used for the representative sample, and district fixed effects for dual practice sample.

35
<!-- Start of picture text --> Figure 5<br>Per pupil expenditure by province<br>1993<br>1200<br>1000<br>800<br>600<br>+ ~~~~~~~~~~Secondaryi<br>400<br>200<br>1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 19 20 2122 23 24 25 26 27 28 29<br>Poorest province Richest<br><!-- End of picture text -->
expenditures because they could not find consistent data on these expenditures for a large sample of countries in their panel.<sup>17</sup> Public infrastructure is measured with the number of telephone mainlines per capita in a country (Canning, 1998). Alternative proxies of public infrastructure are energy generation capacity and transport facilities. Loayza _et al_ . (2005) found these to be highly correlated with each other and concluded that results would be quantitatively similar for any of them.

Governance is measured with the first principal components of four indicators from the International Country Risk Guide (ICRG): prevalence of law and order, quality of bureaucracy, absence of corruption, and accountability of public officials. All of them enter with approximately identical weights in their first principal component. There are other measures of governance but these are typically highly correlated with each other. Loayza _et al_ . determined that the correlation coefficients between the ICRG index used in their study and the Gastil’s index of civil liberties and the Business Environment Risk Intelligence index were 0.79 and 0.85, respectively.

The set of long-run growth determinants _X_ ’ includes the following stabilization indicators: inflation, cyclical volatility, real exchange rate overvaluation, and the number of years in which a country underwent a systemic banking crisis. Inflation – an indicator of macroeconomic stability in many cross-country growth studies<sup>18</sup> – is measured by the consumer price index (World Bank 2002). The inflation rate is positively correlated with other indicators such as fiscal balance (0.24) and black market premiums (0.26) (Loayza _et al._ 2005). Cyclical volatility, which reflects the lack of output stability, is computed as the standard deviation of the output gap for the period. Real exchange rate overvaluation – a measure for external imbalances and the risk of balance-of-payments crises – is given by the

> 17 Loayza _et al_ . note that the presence of initial GDP per capita and the governance indicator in regression model (4) help to control for the fact that not all government consumption can be regarded as an obstacle to growth. 18 See Fischer (1993), Easterly, Loayza, and Montiel (1997), Barro (2001), Bekaert, Harvey and Lundblad (2001).

16
# **Lock-in Effects of Road Expansion on CO2 Emissions: Results from a Core-Periphery Model of Beijing**<sup>**1**</sup>

Alex Anas Department of Economics State University of New York at Buffalo Amherst, New York 14260, USA Govinda R. Timilsina<sup>2</sup> Senior Research Economist Environmental & Energy Unit, Development Research Group The World Bank, 1818 H Street, NW Washington, DC 20433, USA

**Key Words:** infrastructure lock-in effects; climate change mitigation; urban transportation planning

> 1 We sincerely thank Shomik R. Mehndiratta, Carter J. Brandon and Mike Toman for their valuable comments. The data was supplied by Siqi Zheng of Tsinghua University.  We acknowledge the Knowledge for Change Program (KCP) Trust Fund for the financial support. The views expressed in this paper are those of the authors and do not necessarily represent the World Bank and its affiliated organizations.

> 2 Corresponding author; Tel: 1 202 473 2767; fax: 1 202 522 1151; e-mail:gtimilsina@worldbank.org