rafmacalaba/datause-encoder-data huggingface dataset:
tion and poor health associated with poverty. Grantham-McGregor et al. (1999) put “poor sanitation” at the top of a list of “community or ecological” barriers to early childhood development in low-income countries. Our paper joins a growing literature documenting effects of early life health, disease, and nutrition on later life cognitive abilities. Many of these papers, like ours, use an econometric identification strategy based on differences-in-differences or other fixed effects implementations of parallel trends assumptions.

For example, Barham (2012) estimates the effect on cognitive outcomes of a maternal and child health, family planning, and vaccination program in Bangladesh. Using differential timing in the implementation of the program, she finds that early life exposure to the program caused a 0.39 standard deviation in a measure of cognitive functioning when children were 8 to 14 years old. Vogl (2012) studying the Mexican labor market, finds that childhood conditions explain adult height and cognitive achievement. In turn, taller Mexican adults earn more by sorting into jobs requiring more cognitive skill. In particular, although this approach does not consider sanitation externalities, Vogl notes that children whose households had toilets or latrines at age 12 grew into taller adults with more education and higher cognitive test scores.

Perhaps the most complementary recent paper to this one is Shah and Steinberg’s (2013) analysis of the cognitive effects of exposure to drought in utero. They study the same ASER data on learning tests administered to Indian children that this paper uses. Using a similar fixed effects strategy, they find large effects: for example, they find that exposure to drought in utero is associated with being 2 percentage points less likely to recognize numbers in childhood.

Epidemiological literature suggests a large effect on early-life health and net nutrition of exposure to open defecation. One well-known mechanism is diarrhea (Guerrant et al., 1992). Checkley et al. (2008) pool data from several field sites to show an effect of diarrhea in children on subsequent physical growth. Another increasingly well understood possible mechanism is tropical or environmental enteropathy. Evidence is converging that this chronic 4
**Table 3** <u>.</u> _<u>Pantawid</u>_ <u>program impact on children's participation in economic activities</u>

||||Pay and|location||Typ|es of occupatio|ns|Days|worked|
|---|---|---|---|---|---|---|---|---|---|---|
|||||||||||Days|
||Any work|Work for<br>pay, outside<br>own<br>houshold|Work for<br>pay, inside<br>own<br>houshold|Work<br>without pay,<br>outside own<br>houshold|Work<br>without pay,<br>inside own<br>houshold|Laborers and<br>unskilled<br>workers|<br>Farmers,<br>forestry<br>workers, and<br>fishermen|Other|Days<br>worked|worked for<br>pay, outside<br>own<br>houshold|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|OLS only controlling for municipality and child age.|0.038|0.050**|-0.003|-0.007|0.010|0.045*|-0.005|0.004|1.812|1.728|
||(0.029)|(0.021)|(0.010)|(0.011)|(0.021)|(0.026)|(0.016)|(0.007)|(1.765)|(1.370)|
|Additional information:|||||||||||
|Number of observations|1,264|1,264|1,264|1,264|1,264|1,264|1,264|1,264|1,261|1,263|
|Observations in control group|627|627|627|627|627|627|627|627|625|626|
|Observations in treatment group|637|637|637|637|637|637|637|637|636|637|
|Mean in control group|0.201|0.116|0.032|0.038|0.088|0.144|0.078|0.008|5.906|2.851|
|Mean in treatment group|0.242|0.155|0.030|0.030|0.102|0.188|0.077|0.013|7.884|4.666|
|Conditional mean in control group|||||||||29.766|23.182|
|Conditional mean in treatment group|||||||||32.771|27.519|

Note. Estimates of program impact on work by children aged 10 to 14 from eligible households in the 12 months prior to the interview. Standard errors are clustered at the village level. Conditional means are means conditional on positive days worked or positive days worked for pay outside own household. *** p<0.01, ** p<0.05, * p<0.1 51
<!-- Start of picture text --> osy |<br>RaScoNa ve ree». S ::<br>» Rive os Oo ly | Ge<br><!-- End of picture text -->

<!-- Start of picture text --> “wey? and OER<br>* ACCA 1: sy<br>Private ay 4 . . & a t2f<br>Preschool eae a . iF : _ e<br>a a ne a<br>50.41 5.00 (195) | Pid<br><!-- End of picture text -->
# 1   Introduction

In most Sub-Saharan African countries, weak competition in the health sector creates a situation where the population is often faced with local primary health providers which are monopolies. Despite this, some individuals choose to be treated in health centers farther away from their homes, and thus incur greater time and transport costs in order to obtain services perceived as better adapted to their needs.

Such bypassing of health centers has been shown by Akin and Hutchinson (1999) as being an important phenomenon in Sri Lanka. In the rural district studied, they found that close to 2/3 of individuals seeking treatment bypassed nearer facilities and in particular that the more severely ill patients tend to be more likely to bypass health facilities. Leonard et al (2002) examined bypassing in 90 villages in Tanzania and found that patients seek higher quality providers with better staff and basic supplies and tend to understand the importance of these factors for their illness conditions.

In this paper, we examine bypassing of health centers by individuals in Chad and the determinants of individuals’ facility choice. We analyze how bypassing decisions are influenced by differences in quality, prices and competition among health providers across different socioeconomic groups. We make use of data collected in Chad as part of a Health Facility Survey organized by the World Bank in 2004 to examine empirically how bypassing decisions are influenced by differences in service quality, level of competition, and patients’ socio-economic characteristics. The survey covers 281 primary health care centers in 21 districts as well as 1,200 health care employees and 1,801 patients. The matching of facility data with personnel and patient data allows us to control for patients’ characteristics and incomes, types of illnesses, quality of facilities and price of services in determining facility choices.

Furthermore, we examine individuals’ decisions to seek health services among formal and informal health providers. Informal providers in Chad represent an important part of the health sector in rural and urban areas. Accounting for these providers allows a better understanding of individual choices with respect to health services.

1
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150‐3182, available for download at <u>www.ggdc.net/pwt.</u> Filmer, Deon, Halsey Rogers, Noam Angrist, and Shwetlena Sabarwal (2018). “Learning‐Adjusted Years of Schooling (LAYS): Defining a New Macro Measure of Education.” World Bank Policy Research Working Paper No. 8591 Gakidou, Emmanuela, Krycia Cowling, Rafael Lozano and Chris Murray (2010). “Increased educational attainment and its effect on child mortality in 175 countries between 1970 and 2009: a systematic analysis”. The Lancet. 376(9745):18‐24.

Galasso, Emanuela and Adam Wagstaff (2016). “The Economic Costs of Stunting and How to Reduce Them”. World Bank Policy Research Note 17/5.

- Gao, Qiufeng, Yaojiang Shi, Hongmei Yi, Cody Abbey, and Scott Rozelle (2017).  “Reading Achievement in China’s Rural Primary Schools: A Study of Three Provinces”.  Stanford University, Freeman Spogli Institute Working Paper, available at https://fsi.stanford.edu/publication/reading‐achievement‐ <u>chinas‐rural‐primary‐schools‐study‐three‐province.</u>

- Hall, Robert and Chad Jones (1999).  “Why Do Some Countries Produce So Much More Output Per Worker Than Others?”.  Quarterly Journal of Economics.  114(1):83‐116.

- Hanushek, Eric and Lei Zhang (2009). "Quality‐Consistent Estimates of International Schooling and Skill Gradients." Journal of Human Capital 3(2): 107‐143.

- Hanushek, Eric, Jens Ruhose, and Ludger Woessmann (2015).  “Human Capital Quality and Aggregate Income Differences:  Development Accounting for US States”.  Hoover Institutions Economics Working Paper No. 15112.

Horton, Sue and Richard Steckel (2011). Copenhagen Consensus Assessment Paper on Malnutrition.

- Hsieh, Chang‐Tai and Peter Klenow (2010).  “Development Accounting”.  American Economic Journal (Macroeconomics).  2(1):207‐223.

- Jedwab, Remi and Asif Mohammed (2018).  “Returns to Education and Experience”.  Background Paper for 2019 World Development Report, in progress.

- Jones, Benjamin (2014).  “The Human Capital Stock:  A Generalized Approach”.  American Economic Review.  104(11):3752‐3777.

- Jorgenson, Dale W., and Barbara M. Fraumeni (1989). “The Accumulation of Human and Nonhuman Capital, 1948‐1984. In Robert E. Lipsey and Helen S. Tice, eds. The Measurement of Savings, Investment and Wealth Studies in Income and Wealth. Chicago: University of Chicago Press.

- Kaarsen, Nicolai (2014).  “Cross‐Country Differences in the Quality of Schooling”.  Journal of Development Economics. 107: 215‐224.

- Klenow, Peter and Andres Rodriguez‐Clare (1997).  “The Neoclassical Revolution in Growth Economics: Has It Gone Too Far?”.  NBER Macroeconomics Annual.

56
<!-- Start of picture text --> District wise Surveyed Areas & Flooding Spots with 500m Buffer<br>Legend<br>$,!©* 500mFloodingBufferSpots for Flooding Spots<br><!-- End of picture text --> <!-- Start of picture text --> District Boundaries<br>Surveyed Areas<br>_)) 1<br>(| HM 2a<br>2B 2)<br>_)| Mis \<br><!-- End of picture text -->

<!-- Start of picture text -->
[| Water Bodies me MATTAKKULIYA<br>Rivers. i<br>Cj Ward Boundary , q<br>(_3 Colombo City Boundary |<br>Flooding Locations<br><!-- End of picture text -->

<!-- Start of picture text --> Bioemandhal- 04 Road Arther 5) 1<br>Madampitiya Road at Kimbula De Siva Ela Mawatha Junction 21 r e 11<br>Nagalagam<br>Kadvana Street at Garden 175 and 211 3 7 LJ<br>Oxygion Watta, Mattakkulya 5 -<br>SeaArmour(OpposteDeese!BeachStreet,FlatRoad,ParamanandaSangarajaMavatha,andRectamationMotor MawathaEng Road,PLCJethawanaandSt Anthony'sPrince Roadof MawathaWales Avenue)1159 2) MODERA *Py..<br>Green Lane, George R De Siva Mawatha ard ra<br>Rathnam Play Ground Area 12 ad<br><!-- End of picture text --> <!-- Start of picture text --> PAE UT VAWATA ie<br>Q 2:<br>e<br><!-- End of picture text --> <!-- Start of picture text --> iMAHAWATTE Ys<br>5Sek Green *,<br>eet 1 : 3) :<br>ary‘“ nNf aaSeeS! (i e :.1<br><!-- End of picture text --> <!-- Start of picture text --> 7 f= Pd<br>. tJ A Pl<br>a” a rn, 6PAs<br>o . Ca ty a<br>* *re 7 =3 BLOEMENDHAL 1* weeas®<br>. o* “* & ——<br>* oe) ae = P<br>Lore =<br><!-- End of picture text --> <!-- Start of picture text -->
*of ori * i<br>af * .= . * 4 3 ‘ *s i<br>y 9 “eS Ne - : * '<br>: ° to ¥ "1: :<br>. 5 “*, : Oo e ‘ un<br>‘ “ea Seg eeeet” gf in ne<br><!-- End of picture text -->

<!-- Start of picture text --> + Pu “9 yer? ~ \<br>wy *%* ** Sheaee®. * ’<br>“. -* * - ‘<br>8 weet” “se, - i<br>, ast 0 250 500 1,000°+<br>oe $m Meters<br>Cc O L O M B O<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 6<br>Average Contract Duration<br>14.0 -<br>12.0 -11.2 11.7 11.3<br>10.0 tO.0 | | | ~~~~~~~~~~~~~~~9.1<br>8.0 -<br>6.0 -<br>4.0 -<br>2.0 -<br>0.0<br>YPF EG3 ESSO SHELL<br><!-- End of picture text -->

Source: Resolution 25/2000, Secretariat of Energy, Argentina In order to analyze if the average duration of contracts increased since deregulation until 2000, the SDCyC divided the sample in two sub-periods, 1990-1995 and 1995-20002l. The distribution of contract duration in these periods<sup>can beseenin</sup> figure 7. It is clear that the distribution of contracts changed, being more skewed<sup>to the</sup> right the distribution of contracts signed between 1996 and 2000. For instance, 73% of the contracts signed between 1990 and 1995 had a duration of less than 10 years while only 55% of the contracts signed between 1996 and 2000 had the same duration. Besides, it is important to highlight the increase in the percentage of contracts with a duration between 14 and 16 years.

> 21 The total sample size is 1566. The sample for the sub-period 1990-1995 is 492 while the one for the subperiod 1996 - 2000 is 1074.

21
**Figure 12(c): Cambodia: Return to skill by sector over time Tertiary and above)**

<!-- Start of picture text -->
1.5<br>1<br>0.5<br>*<br>0<br>Manufacturing<br>1997 2003-4 2007<br>-0.5 Trade/Services<br>-1<br>Return<br><!-- End of picture text -->

**Year**

- Not enough observations.

**Figure 12(d): Cambodia: Proportion of skilled workers by sector over time (Tertiary and above)**

<!-- Start of picture text --> 16<br>14<br>12<br>10 *<br>8<br>Manufacturing<br>6<br>Trade/Services<br>4<br>2<br>0<br>1997 2003-4 2007<br>Year<br>% skilled<br><!-- End of picture text -->

- Not enough observations.

54
WPS3549

# **Electricity Sector Reform in Developing Countries: A Survey of Empirical Evidence on Determinants and Performance**

**_Tooraj Jamasb_**<sup>**ab**</sup> **Department of Applied Economics, University of Cambridge**

**_Raffaella Mota_ Faculty of Economics and Politics, University of Cambridge**

**_David Newbery_ Department of Applied Economics, University of Cambridge**

**_Michael Pollitt_ Judge Institute of Management, University of Cambridge**

World Bank Policy Research Working Paper 3549, March 2005 _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_ _<u>http://econ.worldbank.org.</u>_

> a This paper was prepared as part of the research program on Industrial Organization Policy for Development at the Development Research Group of the World Bank.  We also acknowledge the support of the CMI Electricity Project (IR-45). We thank Ioannis Kessides, Paul Joskow, Jon Stern, and John Bessant-Jones for their valuable comments and Beth Morgan for editorial assistance.

> b Corresponding author. Department of Applied Economics, University of Cambridge, Sidgwick Avenue, Cambridge CB3 9DE, United Kingdom. Telephone: +44-1223-335271, Fax: +44-1223-335299, Email: <u>tooraj.jamasb@econ.cam.ac.uk</u>
Table 9: 2SLS estimates for the interaction between productivity and technologies

||(1)<br>Ln(VAPW)|(2)<br>Ln(VAPW)|(3)<br>Ln(VAPW)|(4)<br>Ln(VAPW)|
|---|---|---|---|---|
|Female Top Manager|-0.431*|-0.372*|-0.575**|-0.576**|
||(0.230)|(0.212)|(0.278)|(0.282)|
|GBF EXT|0.154*<br>(0.0840)||||
|FTM*GBF EXT|0.776**<br>(0.343)||||
|GBF INT||0.177<br>(0.113)|||
|FTM*GBF INT||0.955***<br>(0.323)|||
|SBF EXT|||0.0173<br>(0.0920)||
|FTM*SBF EXT|||0.673*<br>(0.358)||
|SBF INT||||-0.0236<br>(0.110)|
|FTM*SBF INT||||0.774*<br>(0.444)|
|Controls|Yes|Yes|Yes|Yes|
|Sector|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes|Yes|
|Observations|6627|6627|4317|4309|
|_R_<sup>2</sup>|0.483|0.493|0.487|0.491|
|Shea’s R-squared|0.173|0.186|0.185|0.182|
|Shea’s R-squared|0.118|0.187|0.117|0.176|
|_J_|0.238|0.0589|0.0376|0.00309|

Note: This table presents the 2SLS regression results. Exclusion restrictions are the regional share of FTM and female ownership and the interaction of each variable with the technology indexes. All estimates control for capital stock, employment, sector, country, and additional controls, including formality and the management human capita index. FTM = Female Top Manager, GBF EXT = General Business Function Extensive Margin, GBF INT = General Business Function Intensive Margin, SBF EXT = General Business Function Extensive Margin, SBF INT = General Business Function Intensive Margin. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Estimates were performed using sample weights. Robust standard errors are in parentheses.

Figure 3 shows the marginal effects of technology on labor productivity by different levels of technology sophistication for women and men-managed businesses based on our 2SLS estimates. Panel (a) displays the marginal effects associated with the sophistication of GBF technologies, while Panel (b) shows the impact of the sophistication of SBF technologies. Our findings indicate that women-managed businesses tend to experience lower productivity returns from technologies than men-managed businesses when technology sophistication is 21
I POLICY RESEARCH WORKING PAPER 3013

# Abstract

The extent to which households should be allowed to transfer their land rights in post-socialist transition economies is of considerable policy interest. Deininger and Jin use data from Vietnam, a transition country that allows rental and sales of land use rights, to identify factors conducive to the development of land markets and to assess the extent to which land transfers enhance productive efficiency and transfer land to the poor. They find that activity in both rental _and_ sales markets has

> increased rapidly, enhanced by the possession of longterm use rights and off-farm employment, and contributing to greater equity and efficiency of land use.

> While there is evidence for distress sales by households that experience a shock (death), the scope for such sales is reduced by well-functioning credit markets. Welldefined land rights and appropriate safety nets will thus help transition economies to realize the benefits from the operation of land markets.

This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to assess the impact of land policy on equity and productive development. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Fernandez, room MC3-542, telephone 202-473-3766, fax 202-522-1151, email address mfernandez2@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. Klaus Deininger may be contacted at kdeininger@worldbank.org. April 2003. (30 pages)

> _The Policy Research Workig Paper Seres dissemiates the fiRemgs of work in progress adeas to encourage the exchange of about development Issties An objective of the series Is to get the findings ouit qutitkly, 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 couintrnes they represent._

Produced by the Research Advisory Staff
8 mean a fiscal range of uncertainty equivalent to almost 20% of GDP, or almost twice the level of ODA and far larger than the shock thresholds suggested as triggers for compensatory arrangements.<sup>6</sup>

**Table 1. Estimates of Oil Revenue for Uganda, 2015-34**

||2018-22|<br>2023-27|2028-32|2033-37|
|---|---|---|---|---|
|||(US$ million, a<br>|nnual averag<br>|e)<br>|
|Oil revenue|1,663|3,666|4,113|3,451|
|Royalty|501|675|675|562|
|Profit|865|2,441|2,798|2,314|
|Corporate Income tax|267|373|464|425|
|Dividend and interest|30|177|176|149|
|||(% of GDP, a<br>|nnual average<br>|)<br>|
|Oil revenue|5.1|7.2|6.2|4.0|
|Royalty|1.5|1.3|1.0|0.6|
|Profit|2.7|4.8|4.2|2.7|
|Corporate Income tax|0.8|0.7|0.7|0.5|
|Dividend and interest|0.1|0.3|0.3|0.2|
|Oil revenue|||||
|% of average revenue in 2011-12|63.4|139.7|156.7|131.5|
|% of total ODA disbursement in 2011|105.1|231.7|260.0|218.1|
|Memo items|||||
|Government revenue, FY 2011-12, (% GDP)|13.1||||
|Total ODA disbursement, 2011 (US$m)|1,582||||
|IDA disbursement, FY 2011-12 (US$m)|250.5||||

Sources: IMF, OECD/DAC; Loan kiosk, and World Bank staff estimates.

Moreover, efforts to predict price trends have an unsatisfactory record. Major turning points have not been generally identified, whether in an upward or downward direction. Influenced perhaps by the 1972 study “Limits to Growth”, analysts failed to anticipate the collapse in oil prices after their peak in 1981, when the consensus forecast was for a sustained rise at 3% in real terms. More recently there has been less of a tendency to extrapolate trends and more to project current price levels. This is consistent with the statistical evidence that the path of prices is close to a random walk, but is not particularly helpful for

> 6 Griffiths-Jones and te Velde consider the appropriate level of shock threshold.  They suggest a high of 3% of GDP and a low of 1% of GDP.
**Table S5. Behavioral change over time in Malawi and Uganda**

||Hand<br>washed|Malawi<br>Avoided physical<br>greetings|Avoided<br>crowds|Hand<br>washed|Uganda<br>Avoided physical<br>greetings|Avoided<br>crowds|
|---|---|---|---|---|---|---|
|Round 2|-0.090***|0.010|-0.263***|-0.164***|-0.107***|-0.141***|
||(0.022)|(0.027)|(0.029)|(0.014)|(0.013)|(0.017)|
|Observations|3,375|3,375|3,375|4,376|4,376|4,376|
|R-squared|0.017|0.000|0.069|0.068|0.035|0.037|

_<mark>Note</mark>_ <mark>: Each column reports results from a single regression of a binary variable equal to 1 if the respondent changed their</mark> behavior and 0 otherwise. The binary variable is then regressed on an indicator for phone survey round number, with the first round as the base case. Regressions are run for each country (Malawi and Uganda) separately. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

32