rafmacalaba/datause-encoder-data huggingface dataset:
For infrastructure (see Figure 14), seven out of 13 countries saw increases in shortages between two periods, with Cameroon and Zambia exhibiting the largest inter-period increase. From 2001 to 2007, Cameroon saw a 64.5 percent increase in infrastructure shortage, and from 2006 to 2010, Zambia saw a 65.5 percent increase. During those same time periods, however, Cameroon and Zambia had decreases in shelter shortages, suggesting that changes in types of housing shortages are distinct from one another. Similarly, infrastructure shortages differed from shelter shortages by exhibiting fewer small inter-period changes overall. For example, five of 13 countries examined had at least one inter-period change in infrastructure shortages of less than five percent compared to eight in shelter shortages.

The numerous changes in infrastructure shortages may be attributable to concurrent changes in the provision of basic services, which continues to be a challenge for urban SSA. The large decrease in infrastructure shortage in Uganda, for example, occurred at the same time as vast improvements in drinking water and the toilet access. From 2002 to 2006, access to improved drinking water increased from 54 percent to 70 percent nationally.<sup>23</sup> This improvement is attributed to the National Water and Sewerage Corporation’s explicit policy goal of providing water services to informal housing settlements. Likewise, by 2005, 88 percent of Ugandans had access to pit latrines and toilets, despite 31 percent lacking toilet and access to the sewerage system and 16 percent not having a toilet at all in 2002 (UNHabitat 2011c). Electricity, however, remains a significant issue for Uganda with only 9 percent of the country possessing electricity connections.

Senegal, on the other hand, which saw a significant increase in housing infrastructure shortage, grappled with basic service provision during the time period analyzed. In particular, although water access has increased since 2003, the quality remains poor according to reports from UN-Habitat (2012a). Similarly, the electricity network and connections have improved greatly, but service disruptions and unmet demand remain high (UN-Habitat 2012a).

**Figure 14. Changes in Urban Infrastructure Shortage over Time**

<!-- Start of picture text --> 100%<br>80%<br>64.5% 65.5%<br>60%<br>40% 31.0%<br>20% 12.7% 14.1%<br>8.2%<br>4.2%<br>0.9%<br>0%<br>-2.6% -17.1% -7.5% -6.7% -4.1% -17.5% -2.6%<br>-20%<br>-40% -55.7%<br>CIV CMR ETH GHA KEN MOZ MWI RWA SEN STP TZA UGA ZMB<br>-60%<br>percent difference from previous time period<br>2002 2008 2001 2007 2000 2004 1991 1998 2005 1997 2005 2002 2008 2004 2010 2000 2005 2010 2001 2005 2000 2010 2000 2007 2002 2005 2010 2006 2010<br>% of households facing infrastructure shortage<br><!-- End of picture text -->

> 23 This figure has held steady; in 2011, 71 percent of urban areas and 70 percent in rural areas in Uganda had access to drinking water. WHO/UNICEF Joint Monitoring Program, Estimates on the Use of Water Sources and Sanitation Facilities, updated April 2013.

20
# **3.4 Toll indexing and safety premium**

Tolls are differentiated by type of vehicle. The concessionaire is free to lower tolls provided that he does not price discriminate against users in the same category. The categories are:

- motorcycles;

- cars, vans and pick-up trucks;

- cars, vans and pick-up trucks with attachments;

- 2 axle buses;

- 2 axle trucks;

- buses with more than 2 axles;

- trucks with more than 2 axles.

Tolls are correspondingly higher for vehicles with 2 or more axles. Two axle vehicles pay a maximum of 1.8 times the normal car toll, while larger vehicles pay 3.2 times. Under certain conditions, namely when traffic diversion is not a problem, this charging structure is appropriate for an efficient pricing structure since it is well known that the deterioration of a highway is exponentially (to the 4t power) related to axle weight."<sup>0</sup> However, differentiating tolls by vehicle type may increase tolling costs for the operator. Furthermore, it imposes an additional monitoring burden on the regulator." Tolls are adjusted yearly for inflation or when accumulated monthly inflation surpasses a preestablished level, usually 15%. There is also a premium tariff increase for the concessionaire when road safety improves. Many concessions also have a peak pricing mechanism whereby tariffs are raised during peak demand periods.

# **3.5 Duration of the concession and the subsequent ransfer to public operation**

The concession period varies from project to project. Some-in particular the airport infrastructure concessions-can be as short as 10 to 12 years (including the construction stage). The usual duration of the major highway concessions is 22 to 25 years, with the longest being 28 years (including the construction stage).

Not all concessions have a fixed period duration. In early 1998 the Santiago-Valparaiso-Vifia del Mar inter-city highway project (Route 68) was tendered using the Least Present Value of Revenue 10 This is based on the research of the American Association of State Highway Officials (AASHO). See Armitage (1980) for a useful discussion of the AASHO results.

" In contrast, in the United Kingdom toll roads programme vehicles are differentiated into two categories based on length. However, in this case, the use of shadow tolls instead of real tolls allows the use of simple and inexpensive traffic counting machines. These can only reliably differentiate vehicles by length. For a recount of the UK toll road experience see Highways Agency and Private Finance Panel (1997).

13
# **_6.1 Cross-country Evidence on Resources_**

The easiest starting point is a simple picture of outcomes across countries. Figure 6.1 presents the international association between spending levels and math performance in the latest international test, the 2003 cycle of PISA conducted by the OECD.

# **Figure 6.1: Expenditure per Student and Student Performance across Countries**

Association between average math performance in PISA 2003 and cumulative expenditure on educational institutions per student between age 6 and 15, in US dollars, converted by purchasing power parities.

<!-- Start of picture text --> Math performance in PISA 2003550 Finland<br>Japan<br>Korea Netherlands Belgium<br>Canada Switzerland<br>Czech Rep. Australia Iceland Denmark<br>R 2 = 0.01<br>Sweden<br>France<br>Ireland<br>500 Slovak Rep. Germany Austria<br>Hungary Norway<br>Poland<br>Spain<br>USA<br>R 2 = 0.15<br>Portugal Italy<br>450 Greece<br>400<br>Mexico<br>350<br>0 10000 20000 30000 40000 50000 60000 70000 80000<br>Cumulative educational expenditure per student<br><!-- End of picture text --> Source: Organisation for Economic Co-operation and Development (2004), pp. 102 and 358; Wößmann (forthcoming-a).

The dark line in Figure 6.1 represents the regression line for PISA scores on cumulative expenditure (age 6-15).<sup>64</sup> Taken literally, this line indicates that $60,000 per student in additional expenditure (a quadrupling of spending in the low spending countries) is associated with about a half standard deviation improvement in scores. But, this relationship is almost entirely due to the two 64Organisation for Economic Co-operation and Development (2004), p. 101 uses this figure to conclude that there is a positive relationship between expenditure and student performance, because the correlation is weakly significant (in statistical terms) when considering all countries and ignoring other effects (corresponding to the weakly upward sloping regression line).

60
# **B. Additional tables**

Table B.1: Sample characteristics at baseline for attrition vs non-attrition groups

|||(1)||(2)||(3)|||||
|---|---|---|---|---|---|---|---|---|---|---|
|||Total|Non|-Attrition|A|ttrition|Difference|Co|hen’s D||
|Variable|N|Mean/SE|N|Mean/SE|N|Mean/SE|(2)-(3)|Effect size|95%|CI|
|_Individual-level variables_|||||||||||
|Last earnings|384|109,343<br>(2,891)|328|110,131<br>(3,086)|56|104,732<br>(8,182)|5,399|0.095|-0.188|0.379|
|Female|384|0.086<br>(0.014)|328|0.098<br>(0.016)|56|0.018<br>(0.018)|0.080**|0.285|0.001|0.569|
|Age|384|36.5<br>(0.459)|328|36.3<br>(0.484)|56|37.9<br>(1.360)|-1.570|-0.175|-0.458|0.109|
|Middle school completed|384|0.227<br>(0.021)|328|0.247<br>(0.024)|56|0.107<br>(0.042)|0.140**|0.335|0.051|0.620|
|First formal job|366|0.117<br>(0.017)|328|0.125<br>(0.018)|38|0.053<br>(0.037)|0.072|0.225|-0.112|0.561|
|Recent migrant|360|0.094<br>(0.015)|323|0.099<br>(0.017)|37|0.054<br>(0.038)|0.045|0.154|-0.187|0.494|
|Big 5: Conscientiousness|384|4.66<br>(0.035)|328|4.66<br>(0.039)|56|4.68<br>(0.080)|-0.020|-0.029|-0.312|0.255|
|Big 5: Extraversion|384|3.17<br>(0.057)|328|3.16<br>(0.061)|56|3.26<br>(0.160)|-0.103|-0.092|-0.375|0.192|
|Big 5: Agreeableness|384|4.68<br>(0.028)|328|4.66<br>(0.032)|56|4.8<br>(0.056)|-0.145*|-0.261|-0.545|0.023|
|Big 5: Emotional stability|384|4.17<br>(0.045)|328|4.18<br>(0.048)|56|4.17<br>(0.125)|0.006|0.006|-0.277|0.290|
|Big 5: Openness|384|3.28<br>(0.046|328|3.29<br>(0.050)|56|3.24<br>(0.117)|0.050|0.056|-0.228|0.339|
|_Household-level variables_|||||||||||
|# Beds in household|384|4.84<br>(0.139)|328|4.77<br>(0.150)|56|5.27<br>(0.360)|-0.500|-0.184|-0.468|0.100|
|# Children in household|384|4.46<br>(0.184)|328|4.45<br>(0.205)|56|4.55<br>(0.383)|-0.105|-0.029|-0.313|0.254|
|# Adults in household|384|6.42<br>(0.195)|328|6.35<br>(0.205)|56|6.86<br>(0.589)|-0.507|-0.133|-0.416|0.151|
|Household debt|384|28,255<br>(2,976)|328|26,372<br>(3,039)|56|39,286<br>(9,928)|-12,914|-0.222|-0.506|0.062|
|Household savings|384|78,385<br>(5,201)|328|83,841<br>(5,779)|56|46,429<br>(10,340)|37,413**|0.370|0.085|0.654|
|Household income|384|52,943<br>(1,491)|328|53,613<br>(1,650)|56|49,018<br>(3,324)|4,595|0.157|-0.126|0.441|

_Notes_ : Standard errors in parentheses, *, **, *** denote significance at 10%, 5% and 1% levels. The _Attrition_ group includes all individuals who missed at least one round (either the mid-line or end-line survey, or both). _Recent migrant_ is defined as living for three years or less in current location and moved from outside Dakar. _Middle school_ indicates whether an individual has completed middle school. Applying multiple hypothesis correction, any statistical significance in group difference disappears.

28
<!-- Start of picture text --> st<br>PAN<br>7°<br>B SLv<br>S<br>eo CRI<br>8 N CIM<br>oy NIC<br>o HND<br>a)<br>oO<br>2<br>a<br>°<br>1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> <!-- Start of picture text --> Worldorld (60p)(60)<br>World ex-HI (60p)<br>Costa Rica<br>World (70p)<br>World ex-HI (70p)<br>El Salvador<br>World (80p)<br>World ex-HI (80p)<br>World (90p)<br>Panama<br>World ex-HI (90p)<br>0 1 2 3 4<br><!-- End of picture text -->
<!-- Start of picture text --> 1 Productivity 0.3 a. Govt exp —,on,Output<br>—— Benchmark n Lo Ff Oo,<br>0.8} || ~---- Procyc 0.25+ #\ ¢ at<br>6 ='='="= Induced é H \ 6 i a<br>wo= 06 we OFF FN woe 1 j; ‘<br>> > 0.15) 5 x > *<br>ra® 0.4 QOo gil}5 NN ao<br>x sols A # Oe<br>0.2) 0.05} ¢ ‘ss<br>i<br>000<br>24 6 8 10 12 2 4 6 8 10 12 2 4 6 8 10 12<br>years years years<br><!-- End of picture text -->

<!-- Start of picture text --> Consumption Labor Investment<br>,TM. 4 ,TM t<br>15; } XNcy H S‘. Sr 1<br>5wo) 13: i i s x.7xMe 5w6 08;06} }i|i x.‘ . Sx. 5w)6\\L11<br>3 iin 3 TM 87h %<br>QO tog O 0.4 a ‘<br>x 0.5 Ts~~ x AS 2 .%<br>atc 0.2 Y=<br>000<br>24 6 8 10 12 2 4 6 8 10 12 2 4 6 8 10 12<br>years years years<br><!-- End of picture text -->
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- Attanasio, O., Cattan, S., Fitzsimons, E., Meghir, C., and Rubio-Codina, M. (2020). Esthe function for human Results from a randomized con-

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- Banerjee, A., Chandrasekhar, A. G., Duflo, E., and Jackson, M. O. (2019). Using gossips to spread information: Theory and evidence from two randomized controlled trials. _The Review of Economic Studies_ , 86(6):2453–2490.

- Barham, T., Macours, K., and Maluccio, J. A. (2013). Boys’ cognitive skill formation and physical growth: Long-term experimental evidence on critical ages for early childhood interventions. _American Economic Review_ , 103(3):467–71.

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- Bettinger, E., Cunha, N., Lichand, G., and Madeira, R. (2020). Are the effects of informational interventions driven by salience? Working Paper No. 350, Department of Economics - University of Zurich.

- Black, M. M., Walker, S. P., Fernald, L. C., Andersen, C. T., DiGirolamo, A. M., Lu, C., McCoy, D. C., Fink, G., Shawar, Y. R., Shiffman, J., et al. (2017). Early childhood development coming of age: Science through the life course. _The Lancet_ , 389(10064):77–90.

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27
**Table 10: Simple Logit regression of poverty exit and entry (1993-2000)**

||`(1)`<br>`exit`|`(2)`<br>`entry`|
|---|---|---|
|`Log real income 1993`|`0.119`|`-0.352`|
||`(1.61)`|`(7.01)**`|
|`Stay Urban`|`-0.768`|`0.317`|
||`(4.11)**`|`(2.28)*`|
|`Move to Rural`|`0.760`|`-0.333`|
||`(1.44)`|`(0.80)`|
|`Move to Urban`|`-0.280`|`0.395`|
||`(0.57)`|`(1.00)`|
|`Stay Non-farm`|`0.537`|`-0.385`|
||`(2.86)**`|`(2.46)*`|
|`Move to Non-farm`|`0.601`|`-0.336`|
||`(2.46)*`|`(1.47)`|
|`Move to farm`|`-0.513`|`0.079`|
||`(1.90)`|`(0.37)`|
|`Constant`|`-0.536`|`0.619`|
||`(1.34)`|`(2.11)*`|
|`Observations`|`1186`|`3690`|

``` Robust z statistics in parentheses
* significant at 5%; ** significant at 1%
``` Table 11 shows how this assessment changes when the impact of individual, household and geographical characteristics on poverty exit and entry are taken into account. Some of the determinants of income growth are not strongly associated with existing poverty. For example, neither age, nor sex, nor working hours are significantly associated with exit from poverty. The strongest effects come from education, which strongly increases the chances of exit and reduces those of entry, and from household size--large households are less likely to exit poverty and more likely to enter.

The demographic composition of the household also matters for poverty entry, with higher numbers of small children and men making falling into poverty more likely. It is not clear why having more males in the household should be associated with a higher chance of entering poverty. In most of the literature having more males is associated with higher rather than lower income, but multiple adult males in the household in this sample may reflect the impact of the crisis, as many urban construction workers returned to their village households, at least temporarily.

41
**Table A12: Differential Treatment - Matching Estimates**

|Befor|e delegated ra|ndomization of|inspector assign|ment||
|---|---|---|---|---|---|
|Dependent variable|**Time**|**Fraud**|∆**log value**|∆**log tax**|**Hyp. tax**<br>**revenue**<br>**losses**|
||**A**|**. Matched sa**|**mple**|||
||(1)|(2)|(3)|(4)|(5)|
|Excess interaction share|-2.178***|-0.260*|-0.080*|-0.075|0.280**|
||(0.609)|(0.138)|(0.044)|(0.056)|(0.119)|
|Declaration characteristics|Yes|Yes|Yes|Yes|Yes|
|Inspector fxed efects|Yes|Yes|Yes|Yes|Yes|
|Broker fxed efects|Yes|Yes|Yes|Yes|Yes|
|Source country fxed efects|Yes|Yes|Yes|Yes|Yes|
|HS2-product fxed efects|Yes|Yes|Yes|Yes|Yes|
|Month-year fxed efects|Yes|Yes|Yes|Yes|Yes|
|Observations|8,049|8,720|8,703|8,317|8,710|
|R-squared|0.387|0.268|0.204|0.184|0.333|
|**B. Pro**|**pensity scor**|**e weighted lea**|**st squares esti**|**mation**||
||(6)|(7)|(8)|(9)|(10)|
|Excess interaction share|-2.009***<br>(0.181)|-0.209***<br>(0.036)|-0.069***<br>(0.012)|-0.081***<br>(0.014)|0.388***<br>(0.058)|
|Declaration characteristics|Yes|Yes|Yes|Yes|Yes|
|Inspector fxed efects|Yes|Yes|Yes|Yes|Yes|
|Broker fxed efects|Yes|Yes|Yes|Yes|Yes|
|Source country fxed efects|Yes|Yes|Yes|Yes|Yes|
|HS2-product fxed efects|Yes|Yes|Yes|Yes|Yes|
|Month-year fxed efects|Yes|Yes|Yes|Yes|Yes|
|Observations|41,094|44,492|44,404|40,467|44,467|
|R-squared|0.319|0.183|0.134|0.117|0.208|

_Notes:_ Standard errors clustered two-way by inspector and by broker are presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by official rules, calculated using calibration methods (as explained in Section 4). Declaration characteristics include the tax rate, the risk score, a dummy for the red channel, the share of value accounted for by differentiated products, a dummy indicating whether the declaration was mixed, and a dummy indicating the declaration was subject to valuation advice. "Observations" refers to the number of non-singleton observations. Panel A restricts the sample to matched declarations selected using nearest neighbor propensity score matching on the basis of the risk score, the tax rate, a dummy for the red channel, a dummy for being a mixed shipment, the share of differentiated products, a dummy for GasyNet’s valuation advice, the inital weight (in logs), and the initial value (in logs). Panel B presents propensity score weighted regressions as proposed by Hirano et al. (2003). OLS estimation is used. The sample covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

72
Evaluating Workfare When the Work Is Unpleasant: Evidence for India’s National Rural Employment Guarantee Scheme Arthur Alik Lagrange and Martin Ravallion<sup>1</sup>

**Sector:** Poverty

**Keywords:** Labor supply, welfare measurement, targeting, India, National Rural Employment Guarantee.

**JEL:** H53, I38

> 1 Alik Lagrange is with the University of Toulouse and Ravallion is with the Development Research Group, World Bank. For their comments on an earlier version of this paper the authors thank Rinku Murgai and Dominique van de Walle. They also thank the India Research Group at the Paris School of Economics for providing access to their data while both authors were visiting PSE. Addresses: arthur.alik@midgard.univ-tlse1.fr  and <u>mravallion@gmail.com.</u>
56

# 3. Sketch Planning Models.

A final set of approaches are called sketch planning models in the transportation literature. These models are simplifications of network approaches offering some of their advantages without the cost of building or specifying a complex model but with the disadvantages of imprecision from the coarseness of analysis and reliance on the transfer of parameters and relationships from other studies.

One example of these models is UMOT (Unified Mechanism of Travel) (Zahavi, 1979) which produces as output car ownership by income group, aggregate modal choices, average travel time and speed, and total expenditures and travel time. It has been used to address many of the policies we are interested in, although some analysts have found it to be a poor predictor of travel behavior. Canned programs, such as EHME/2 are at the other extreme in terms of spatial detail. These programs follow the standard four step procedure used in the classic transportation model, using default parameters to drive much of the model.

A huge literature has developed on using traffic count data to estimate trip matrices and demand functions. Such data are easy to collect compared to origin-destination data, the latter, according to Ortuzar and Uillumsen, having a short shelf life in rapidly developing cities.

Finally, teaching models ara available. One model, called GUTS (Uillumsen and Ortuzar, 1985), is available on the PC. It has tvo transport modes operating in a circular-symmetric city and the user appears to have the ability to model many of the policies of interest to us (fares, level of service, parking fees, dedicated bus lanes, licensing schemes, and even highway investment projects). The model produces outputs for volume, speed, modal split, travel time, and expenditures by person-type.
Policy Research Working Paper 8893

# **Abstract**

Cambodia has recorded both rapid economic growth and macroeconomic stability in recent decades despite (or thanks to) high levels of dollarization. Previous studies on dollarization in Cambodia have largely focused on examining its causes and estimating seigniorage losses. As an attempt to further explore the effects of dollarization in Cambodia, this paper examines its impact on the competitiveness of the export sector. The main results, based on a vector autoregression estimation of quarterly data over 1994Q4–2016Q4, indicate that a positive US interest rate shock has a negative impact on Cambodia’s trade balance with the European Union, its main trading partner, as it leads to appreciation of the US dollar. Furthermore, this shock also leads to a significant decrease in Cambodia’s international reserve levels during the first two quarters following the shock. The surrendering of monetary and exchange rate independence seems to affect the competitiveness of the tradable sector negatively as well as exacerbate financial sector vulnerability to solvency and liquidity risks.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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/prwp. The authors may be contacted at msanchezmartin@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