rafmacalaba/datause-encoder-data huggingface dataset:
**and time-consuming traditional cooking technologies and fuel sources.  In terms of sheer numbers of women, there are huge potential health gains from effective dissemination of improved cookstoves or other modern energy technology for cooking in rural South Asia and Sub-Saharan Africa, and of less polluting cooking and heating technology in China.**

According to the latest World Energy Outlook, 40% of the global population relies on ―traditional use of biomass for cooking‖ (i.e., 3-stone fires and unimproved stoves), and 20% of the global population (1.4 billion people) do not have access to electricity. The greatest number of people without access to electricity are in SSA (585 million), followed by India (404 million). There are 855 million people in India and 653 million people in SSA who cook with biomass using traditional methods (World Energy Outlook, Figure 9). In China, there are another 423 million, plus 400 million who rely on coal, in many cases for both cooking and heating; the need for heating in cold climates presents significant additional challenges both to households and to those seeking to promote new technology (Baris and Ezzati 2007; Lucas et al. 2003; Pokhrel et al. 2010). In all countries, the majority of people who rely on biomass for thermal energy and who lack access to electricity are in rural areas.

There are clear patterns in household energy use between rich and poor and rural and urban areas and households. Nevertheless, the ―energy ladder‖ hypothesis, which expected a sequential change of fuels as income rises, has been replaced by a ―fuel stacking‖ hypothesis given a number of studies that show that many households use multiple fuels and that the choice of fuel is affected by many more factors than income (see e.g. Masera et al. 2000; Heltberg 2004, 2005; Gupta and Köhlin 2006; Gundimeda and Köhlin 2008). The availability of multiple fuels enables policies encouraging fuel switching, e.g. from charcoal to electricity in urban areas. However, only few studies estimate the cross-price elasticities of demand for various fuels for larger samples that could shed light on the potential for policies to promote fuel switching. One example is Gundimeda and Köhlin (2008), using Indian NSSO data (110000 households). It reported for rural areas: cross price elasticity of wood with respect to kerosene ranging from 0.49 to 0.71<sup>6</sup> ; of wood with respect to electricity ranging from 0.38 to 0.45; and of wood to LPG ranging from 0.56 to 0.84. In urban areas: cross price elasticity of wood with respect to kerosene ranging from 0.43 to 0.54; of wood with respect to electricity ranging from 0.32 to 0.47; and of wood with respect to LPG ranges from 0.38 to 0.65. The cross-price elasticities are thus quite low, reducing the potential for sizable impact of tax and subsidy schemes.

> 6 Thanks to the large number of observations, the elasticities were calculated for low, medium and high income groups.

2
Policy Research Working Paper 6028

# **Abstract**

Procurement packaging has important effects on not only the bidders’ bidding behavior, but also contractors’ performance. By changing the size of public contracts, procurers can encourage (or discourage) market competition and improve contract performance, avoiding unnecessary cost overruns and project delays. In practice, there is no single solution about how to package public contracts. With procurement data from road projects in Nepal, this paper examines the optimal size of road contracts in rural areas. The optimum varies depending on policy objectives. To maximize the bidder participation, the length of road should be about 11 kilometers. To minimize cost overruns and delays, the contracts should be much larger at 17 and 21 kilometers, respectively. Compared with the current procurement practices, the findings suggest that procurers take more advantage of enlarging road packages, although contracts that are too large may increase the risk of discouraging firms from participating in public tenders.

This paper is a product of the Office of the Chief Economist, Sustainable Development Network; and Transport Unit, South Asia Region 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 author may be contacted at aiimi@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 3.  Nested Logit Model for Indonesia** 31

|1995-<br>1999|||||||
|---|---|---|---|---|---|---|
|Unexpected<br>rainfall in<br>2000|0.002|0.002|‐0.001**|0.0004|‐0.0003|0.0003|
|Log<br>likelihood|`-56.27`|N/A|`-1426`|N/A|`-1773`|N/A|
|Likelihood<br>ratio test for<br>model|`283.92***`|N/A|4744***|N/A|`3406***`|N/A|
|Chi squared<br>test for<br>unexpected<br>rainfall and<br>distance to<br>railroad|16.88***|N/A|303.69***|N/A|198.29***|N/A|
|Likelihood<br>ratio test for<br>IIA<br>hypothesis|36.75***||112.38***||861.80***||
|N|219|N/A|2583|N/A|2499|N/A|

Notes: Table reports the coefficients of a variable on the probability of household receiving international remittances in 2007. The tree structure assumes that households plan first whether or not to receive remittances in 2000, and then conditional on their choices in 2000, choose their remittance situation in 2007. It is assumed that all variables in the initial tree branch are the variables shown in the table plus a dummy for urban/rural areas and dummies for four Indonesia regions. The only variable in the bottom branch is the distance from kabupaten to railroad variable. The distance to railroad variable is adjusted in the following manner: for households that never receive remittances the variable is the simple distance to railroad; for households that receive remittances in 2007 but not in 2000, it adds 3 to the distance to railroad variable; for households that receive remittances in 2000 but not in 2007, it adds 2 to the distance to railroad variable; and for households that receive remittances both in 2000 and 2007, it adds 4 to the distance to railroad variable.

*** Significant at the 0.01 level. ** Significant at the 0.05 level.

*       Significant at the 0.10 level.
# In search of better opportunities: Sorting and agglomeration effects among young college graduates in Colombia

Marigee Bacolod<sup>*‡</sup> _Naval Postgraduate School_ Jorge De la Roca<sup>*§</sup> _University of Southern California_ María Marta Ferreyra<sup>*¶</sup> _The World Bank_ Key words: agglomeration effects, regional migration, spatial inequality jel classification: j15, r23

> *We thank Andrea Franco for her exceptional research assistance. Thanks to Harris Selod for helpful comments and discussions.

> ‡Corresponding author. Graduate School of Defense Management, Naval Postgraduate School, 555 Dyer Road, Monterey, ca 93943, (e-mail: `mbacolod@nps.edu` ; website: `https://sites.google.com/site/marigeebacolod/` ).

> § Sol Price School of Public Policy, University of Southern California, 650 Childs Way rgl 326, Los Angeles, ca 90089, usa (e-mail: `jdelaroc@usc.edu` ; website: `http://jorgedelaroca.name` ).

> ¶Office of the Chief Economist Latin America and the Caribbean, The World Bank, 1818 h Street, nw, msn I8-808, Washington, dc 20433 (e-mail: `mferreyra@worldbank.org` ; website: `https://sites.google.com/site/ mariamartaferreyraphd/` ).
One, Two, and Three 6

**Table S7.** Impact of Public Works on Skilled Wages

|||SGRY +|NREGA||Only NREGA|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_sinh_<sup>_−_1</sup>(_ed,t_)|-9.270<sup>_∗∗∗_</sup>|2.852<sup>_∗∗∗_</sup>|3.717<sup>_∗∗∗_</sup>|1.935<sup>_∗∗_</sup>|1.028|
||[1.992]|[1.188]|[1.390]|[0.885]|[0.889]|
|District effects|No|Yes|Yes|Yes|Yes|
|Year effects|No|Yes|Yes|Yes|Yes|
|Oth. controls|No|No|Yes|Yes|Yes|
|Het. trends|No|No|No|Yes|Yes|
|Observations|1313|1313|1313|1313|1313|

_Source_ : Author’s analysis based on AWI data (for skilled wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . The unit of observation is a district-year. The dependent variable in all the regressions is the log of skilled wages. Columns 1-5 have _sinh_<sup>_−_1</sup> ( _ed,t_ ) as the key regressor. In columns 1-4, program expenditure is for SGRY (b/w 2001 and 2007) and NREGA (b/w 2006 and 2010). In column 5, only expenditure under NREGA is considered (all expenditure values under SGRY are substituted by zero). The Inverse Hyperbolic Sine (IHS) transformation of the expenditure variable sinh<sup>_−_1</sup> ( _ed,t_ ) has the same interpretation as _log_ ( _ed,t_ ). All other controls are the same as in table 1 in the main article. All regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district level and are robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01
15 Table 2

# UNEMPLOYMENT RATES FOR POPULATION SUBGROUPS, 1988 URBAN AREAS (OVER 100,000 INHABITANTS)

||Men|Women|
|---|---|---|
|Age|||
|12-i5|4.2|4.8|
|16-20|8.4|14.0|
|21-25|5.3|8.9|
|26-30|3.0|4.0|
|3140|1.2|3.3|
|41-50|1.7|3.4|
|51-60|1.9|2.3|
|61-70|2.4|4.0|
|Education|||
|No educ|2.1|1.8|
|1-5 yrs|2.1|3.8|
|6 yrs|2.5|_5.2_|
|<br>7-8 yrs|4.8|_5.4_|
|9 yrs|4.7|10.6|
|<br>10-12 yrs|4.1|7.9|
|13 + yrs|3.4|5.7|
|Total|3.4|6.3|

Note: unemployed defined as active job seekers Source: own estimates from 1988 National Employment Survey, INEGI
|Remittances from abroad|-0.011|-0.116|-0.294***|-0.184*|-0.153|
|---|---|---|---|---|---|
||(0.074)|(0.115)|(0.111)|(0.111)|(0.118)|
|Remittances from abroad within the same||||||
|enumeration area (EA)|0.0102|0.108*|0.057|-0.086**|0.001|
||(0.037)|(0.065)|(0.071)|(0.041)|(0.058)|
|Remittances from family within the same||||||
|enumeration area (EA)|0.068**|-0.042|0.127***|0.116**|0.087|
||(0.031)|(0.053)|(0.048)|(0.046)|(0.056)|
|Observations|934|802|802|802|802|

Note. Results are presented in margins and clustered standard errors in parentheses. Individual-level survey weights used. Statistical significance at: *** p<0.01, ** p<0.05, * p<0.1. Enumeration areas fixed effect included. We also controlled for the following variables: 19-22 years, Currently neither working or attending school, Interesting job to do, STEM related work, Own account worker, Quintile 2, and Remittances from family within the country, Knowing someone in the community that has your dream job, and Urban area. Results for these variables were either not statistically significant or not discussed in the analysis. Full results are available under request.

# **5.2. Limitations of this study**

There are several limitations to our study. Firstly, aspirations are subjective measures and may be subject to bias. Individuals may have limited capacity to predict how their aspirations might change in response to success or failure. The process of revising aspirations can be slow, especially when social expectations and norms impede the flow of information and the revision of expectations (Favara, 2017). Furthermore, as discussed, measurement issues can pose challenges in comprehending youth aspirations, potentially affecting the quality of the collected data and subsequent policy recommendations. Different sets of questions and tools may yield different results. Lastly, we must acknowledge that youth aspirations are subject to change over time. This means that cross-sectional data cannot establish cause-and-effect relationships. Aspirations are endogenous, meaning they evolve as youths either achieve or fail to attain their goals, leading to the setting of new objectives.

# **6. Policy implications and conclusions**

This paper investigates the factors that influence the educational, career, and migration aspirations of young individuals in Ethiopia, Nigeria, and Malawi. We have used data from dedicated surveys designed to capture youth aspirations, along with information on their educational and employment backgrounds. Drawing from our findings, we present policy recommendations aimed at aligning youth’s educational and career aspirations with concrete strategies to mitigate youth unemployment and underemployment.

**Firs of all, we find that aspirations are context dependent** . Our study highlights that both career and academic aspirations are significantly shaped by interactions within the social environment, and the constraints and opportunities within the family context. Additionally, cultural values and social expectations play a crucial role in influencing youth’s aspirations, particularly impacting

24
# **A.6 Completion of SPs**

We completed SPs with 118 of the 143 providers sampled, primarily because providers had left the clinic or were away on “long leave” in the 6-month phase between the listing and the SP work. We attempted to complete these providers over a minimum of 4 attempts, and were forced to stop trying at that point due to the heightened risk of detection. Of the 49 providers without private facilities, we completed an SP with 30 providers (61.2 percent). Of the 94 providers with private practices, we were able to complete at least one SP with 88 providers (93.6 percent, either public or private). The number of dual practice providers for whom we have at least one observation in both public and private is 69. As discussed in text, and seen in Table A.10, it was difficult to complete a case in the public practice of the public MBBS doctors (because of high absence rates), and we had a lower completion rate in the public practices of these doctors than in the private practices (75 percent versus 93 percent). To the extent that absence is correlated with lower quality practice, this would suggest that we underestimate the public-private difference in our results.

# **B Standardized Patient Data Collection and Notes**

# **B.1 Description of tracer conditions and relevance for India**

SPs presented either a case of unstable angina, asthma, or dysentery of an absent child.

- Unstable Angina: A 45-year-old male complains of chest pain the previous night. Appropriate history taking would reveal classic signs (radiating, crushing pain) and risk factors (smoking, untreated diabetes, and family history of cardiac illness) of unstable angina or an imminent myocardial infarction.

- Asthma: A 25-year-old male or female presents with difficulty breathing the night before the visit. When questioned appropriately, the SP reveals that the episode lasted for 10 to 15 minutes and involved a “whistling” sound (wheezing) and that he or she has had similar episodes before, often triggered by house cleaning and cooking smoke. The SP also reports a family history of similar symptoms.

- Dysentery: A 26-year-old father of a 2-year-old complains that his or her child has diarrhea and requests medicines. When probed, the SP reveals details of their water source and sanitation habits, in addition to the presence of fever and the frequency and quality of the child’s stools.

For all cases, checklists of recommended history questions and examinations were developed together with an advisory committee and SPs were trained to recall the questions 44
# **Appendix 1: Electricity Subsidy Cases Included in Database**

|**Location**|**Existing subsidy cases**|**Simulated subsidy cases**|**Year**|**Base study for subsidy**<br>**analysis**|
|---|---|---|---|---|
|Argentina|Avg. of provincial means-tested<br>subsidy*|Discount for means-tested<br>households|2002|Foster 2004|
|Cape Verde|IBT with 40 kWh first block|VDT with 40 kWh<br>threshold; means-tested<br>discount|2001-02|Angel-Urdinola and<br>Wodon 2005a|
|Colombia<br>(Bogota &<br>urban areas)|Geographically defined tariffs<br>with IBTs|Geographically defined<br>tariffs with IBTs and means<br>testing|2003|Melendez et al 2004;<br>Melendez 2005|
|Croatia|Uniform volumetric tariff||1998|Shkaratan 2005|
|Georgia<br>(Tbilisi)|Means-tested free allowance;<br>means-tested discount||2001|Lampietti et al., 2003;<br>World Bank 2004.|
|Guatemala|VDT with 300 kWh threshold|VDT with 100 kWh<br>threshold|2000|Foster and Araujo 2004|
|Honduras|VDT with 300 kWh threshold||1999|Wodon et al 2003|
|Hungary||IBT|1997|Shkaratan 2005|
|India (urban<br>areas in each<br>state)|IBTs, with variations by each state||2001-02|Santhakumar 2004.|
|Mexico|Geographically defined tariffs<br>with IBTs||2002|World Bank 2004|
|Peru|IBT||2003|OSINERG 2005|
|Rwanda<br>(national &<br>urban areas)|Uniform volumetric tariff|VDT with 50, 40, and 20<br>kWh threshold; IBT with<br>50, 40, and 20 kWh first<br>block|2000-01|Angel-Urdinola et al<br>2005|
|Sao Tome<br>and Principe|IBT with 300 kWh first block|VDT with 300 kWh<br>thresehold; IBT with 200<br>kWh first block|2000-01|Angel-Urdinola and<br>Wodon 2005b|

23
on R&D and apply for novel patents less frequently (Dutz and Sharma 2012), offer in-service training more rarely (Riboud et al. 2007), and have lower levels of managerial skills and training than firms in higher-income countries (De Mel et al. 2009, and Bloom and van Reenen 2007), suggesting potentially distorted incentives for firms to innovate and significant room for ‘catchup’ growth. Firm decisions to undertake innovative, productivity-enhancing activities are likely to be jointly dependent on firm-level factors —such as managerial education and practices, workforce skills, and local spillovers— and business environment factors affecting incentives and risk-return considerations. In a sample of more than 26,000 manufacturing establishments across 71 developed and developing countries, Dutz et al. (2012b) find that product and process innovations are fostered by a pro-competitive business environment providing ready access to information, financing, export opportunities, and other essential business services that facilitate the entry and expansion of young firms. As highlighted in Box 1 for the case of China, increases in productivity and innovation are contingent on high domestic savings translated into high investment, coupled with reforms that spur global learning by firms from FDI, trade, and integration into global value chains, among others.

# **_Box 1: China’s sustained high growth driven by savings and endogenous TFP growth_**

According to Yusuf (2011), China’s growth since 1979 can largely be explained with reference to four factors: (i) a steep and sustained high level of capital investment in productive industrial assets and infrastructure, facilitated by high and rising national savings rates; (ii) a succession of reforms that gradually introduced market institutions and disciplines, and accelerated structural transformation of labor out of agriculture and its employment in urban industrial activities; (iii) technological catch-up and advances in many different fields, facilitated by FDI as a conduit for technologies from abroad; and (iv) the integration of trade, capital flows, and the emergence of international value chains that multiplied the opportunities for export-led growth. All four factors contribute to increases in TFP, as investments in capital often consist of imports that embody global technological advances that are new-to-the-firm.

A sources-of-growth decomposition analysis confirms the importance of capital investment and TFP for sustained high growth rates. Between 1978 and 2004, physical capital and TFP contributed 3.2 and 3.8 percent respectively to China’s annual GDP growth of 9.3 percent. And over the last decade of this period, physical capital and TFP shares were even higher, contributing 4.2 and 4.0 percent of annual GDP growth of 9.7 percent.

# **Sources of Growth (1978-2004)**

Annual percentage rate of change

|<br>Period|<br>Output|<br>Empt.|Output<br>per Worker|Contribution of:<br>Physical Capital|<br>Education|.<br>TFP|
|---|---|---|---|---|---|---|
|78-04|9.3|2.0|7.3|3.2|0.2|3.8|
|93-04|9.7|1.2|8.5|4.2|0.2|4.0|

Source: Bosworth and Collins (2008) 6
**TABLE A7: IRR and investment size.** Cash deployed is classified into investment size quartiles by decade as in Table 4. The IRR is undefined for 138 investments that were written off and have zero or trivial positive cash flows. For the purposes of this table the IRR is coded as -100 in these cases. The total column refers to the performance measure when pooling cash flows from all investments in the relevant vintage years.

|IRR by size quartile, vint|age pre|-2010 only||||
|---|---|---|---|---|---|
|Investment size|Total|Median|Average|Standard<br>error|Standard<br>deviation|
|Direct investments||||||
|1st quartile (smallest)|13.93|2.26|6,550.20|(6,495.08)|123,577.40|
|2nd quartile|14.07|1.85|25.67|(28.32)|535.93|
|3rd quartile|10.28|5.44|182.04|(184.89)|3,546.84|
|4th quartile (largest)|15.18|7.05|7.61|92.63)|50.10|
|Fund investments||||||
|1st quartile (smallest)|5.40|0.72|-7.39|(3.96)|31.41|
|2nd quartile|7.78|-0.61|-1.61|(2.34)|18.58|
|3rd quartile|10.56|4.70|15.45|(10.15)|78.60|
|4th quartile (largest)|12.19|7.75|7.45|(1.78)|14.68|

52
election periods with opposite trends in non-election periods. Our findings are important not only because they highlight CRA effectiveness in transnational fiscal oversight. Our results turn the tables on political budget cycle research that has largely treated CRAs as passive observers and ratings as a mere responses to election-period fiscal manipulations (Block and Vaaler, 2004). Our study positions political budget cycle research in line with other work demonstrating how private often foreign-domiciled financial actors prompt governments to build solid credit histories, perhaps under threat of future credit freezes (e.g., Eaton and Gersovitz, 1981). Governments in emerging democracies have incentives to build that history during elections. Forbearance from opportunistic borrowing to stay in office is part of a larger relationship with CRAs that also facilitates more borrowing in non-election periods.

# **2. Empirical Methodology**

## **2.1. Model Specification and Tests**

To test our proposition that ratings curb opportunistic borrowing by governments during election periods, we define the following statistical model: In (1), the dependent variable, _Budget Balance_ , is the central government budget balance as a percentage of GDP for country _i_ in year _t_ . A negative value of _Budget Balance_ represents a fiscal deficit which the government finances by issuing debt or other means. We regress _Budget Balance_ on: an intercept ( α ); a one-year lagged dependent variable (LDV), _Budget Balanceit-1_ ( β ); lead, current-year and lagged values of country _i_ ’s average rating on January 1 of year _t_ , 3