rafmacalaba/datause-encoder-data huggingface dataset:
Economic Parameters of Deforestation 13 _fixed logging technology. The additional conditions requiredfor the result to hold with variabl logging technology are skown to be very likely. Under any circumstances, lower timber prices increase the area of uxmanagedforest that wil remain after a steady-state is reached in which al timberproductiox has skjfted to managedfeoreits. On tAe other A and, lower tib6erprices kad to a reduced area ofmanagedforests. Te change in total forest area as a result of a timber price drop is undetermined._ <!-- Start of picture text --> The Log Market<br>Price<br>02 ~QI<br>Timber Quantity<br>Log Demand Log Demand Log Supply<br>without LEB with LES<br><!-- End of picture text --> Figure 2 Repetto, 1988, advances a second argument against log-export bans, which is also echoed in many World Bank documents (see Braga, 1992, and references above). The claim is that lower domestic log prices encourage wasteful logging and processing techniques and can, therefore, increase logging. Undoubtedly, lower log prices lead to less efficienL logging and processing. The second part of the argument, however, is flawed. This can be shown in Flgure 2, which depicts the domestic market for logs. The supply
crisis year, implying a collapse of flows of about 11.5 percentage points. In middle-income countries, _COD_ declines around 5 percentage points of trend GDP during the crisis year if compared with the previous two years. Once more, a milder decline of 2 percentage points over the same period is observed in low-income countries.

Figure 5 also suggests that the reaction of domestic and foreign agents might be stronger during severe crisis episodes. _More than one crisis_ episodes lead to significant retrenchment in capital flows by foreign and domestic agents during the crisis year and a significant fall by foreign agents during the following two years. Wald tests reported in Table 7 shows that this graphical evidence is statistically significant for middle- and low-income countries.<sup>26</sup> Overall, the results reported in Table 7 and Figure 5 show that the retrenchment in gross capital flows takes place not only around severe crises but also around mild ones. Furthermore, these estimations suggest that such a retrenchment by domestic and foreign agents is indeed a stylized fact regarding the dynamics of gross capital flows during crises.

# **4.3 The Dynamics of the Subcomponents of Gross Capital Flows**

In this section, we analyze whether the patterns of gross capital flows documented above are widespread across flow types or driven by a single type of flow. This is important because some types of flows might behave in particular ways. For example, reserves are likely to play an important role, especially in developing countries trying to stabilize their exchange rates. We show that the observed patterns of gross capital flows are indeed present in most subcomponents of gross capital flows.

We start by discussing the relative size and evolution of the different subcomponents of

> 26 The test results for high-income countries are less robust probably because of the low number of severe episodes, only five in our sample.

23
<!-- Start of picture text --> Employment in subsidiary firms<br>i<o]<br>n 2 _o--- 77’, an<br>® a N<br>ov yo ¢ a NL<br>® 7 ~“<br>a Z<br>= Z<br>2X 7<br>4<br>coe<br>1990 1995 2000 2005 2010<br>Year<br>——*——_ Informal tradables ——@€—- Informal non-tradables<br>@:- Informal apparel<br><!-- End of picture text --> <!-- Start of picture text --> Subsidiary firms<br>cD - == LLL e_<br>/ ~~<br>Fa/ sw ~S<br>EN fo -*<br>S /<br>S- /<br>4oa<br>1990 1995 2000 2005 2010<br>Year<br>——*——_ Informal tradables ——®-—- Informal non-tradables<br>“-@-o- Informal apparel<br><!-- End of picture text --> <!-- Start of picture text --> Informal sector tradable firms<br>oO 2<br>: 7<br>:<br>o= + =ao<br>iS) -<br>2 ct _-—*<br>= ae Le ee Oe _—<br>= — eee ~<br>——— ~~@<br>1990 1995 2000 2005 2010<br>Year<br>——*—_ 1-person subsidiary —-—@®-—- Larger subsidiary<br>ee 1-person non-subsidiary — @—-- Larger non-subsidiary<br><!-- End of picture text --> <!-- Start of picture text --> Informal sector non-tradable firms<br>oOo _._<br>eo —~<br>ae e-— — — — ae<br>a oN<br>so a ~<br>2%<br>=“a Oe ~~<br>1990 1995 2000 2005 2010<br>Year<br>——*— 1-person subsidiary —-—®-—- Larger subsidiary<br>ee 1-person non-subsidiary — ®— - Larger non-subsidiary<br><!-- End of picture text -->
||**Table 1: **M|ain features of|structural refo|rms to old-ag|e, disability an|d deathsystem|sin reformedL|AC countries<br>**El**||**Dominican**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Feature**|**Chile**|**Peru**|**Colombia**|**Argentina**|**Uruguay**|**Mexico**|**Bolivia**|**Salvador**|**Costa Rica**|**Republic**|
|Year(s) of reform|1981|1992-1993|1994|1994|1996|1997|1997|1998|1995/2000<sup>e</sup>|2001|
|Contribution-related|||||||||||
|PAYG system|Closed|Remains|Remains|Remains|Remains|Closed<sup>c</sup>|Closed|Closed|Remains|Closed|
|Total payroll tax rate,<br>pre-reform (%)|33|18|17.8|42|40|20|19|11.8|22|9.25|
|Total payroll tax rate,<br>post-reform (%)|20|20.5/22.0<sup>a</sup>|33.8|46<sup>f</sup>|40|26|24|13.5|26|20|
|IRA contribution (%)|10|8|10|7.72|12.27|12.07|10|10|4.25|10|
|Participation of new<br>workers|Mandatory|Voluntary|Voluntary|Voluntary|Voluntary|Mandatory|Mandatory|Mandatory|Mandatory|Mandatory|
|Participation of self-|||||||||||
|employed workers|Mandatory|Voluntary|Voluntary|Mandatory|Mandatory|Voluntary|Voluntary|Voluntary|Voluntary|Mandatory|
|Separate system for|||||||||||
|civil servants|No|No|Yes|No|No|Yes|No|No|N/A|Yes|
|Payout options|Annuity or<br>scheduled<br>withdrawal|Annuity or<br>scheduled<br>withdrawal|Annuity or<br>scheduled<br>withdrawal|Annuity or<br>scheduled<br>withdrawal|Annuity<br>only|Annuity or<br>scheduled<br>withdrawal|Annuity and<br>scheduled<br>withdrawal|Annuity or<br>scheduled<br>withdrawal|Annuity or<br>scheduled<br>withdrawal|Annuity or<br>scheduled<br>withdrawal|
|Minimum return on|Relative to|Relative to|Relative to|Relative to|Relative to|||Relative to||Relative to|
|investments|average|average|average|average|average|Unregulated|Unregulated|average|Unregulated|average|
|Minimum contributory<br>pension|Yes|Yes<sup>b</sup>|Yes|Yes|Yes|Yes|No|Yes|Yes|Yes|
|Social assistance<br>pension|Yes|No|No|Yes|Yes|No|Yes<sup>d</sup>|No<sup>g</sup>|Yes|Yes|

Notes:<sup>a</sup> 20.5 percent for private pension system, 22.0 percent for national system.<sup>b</sup> Only for affiliates born before 1945.<sup>c</sup> In Mexico, the PAYG scheme still works as insurance for disability as well as life and labor risks.<sup>d</sup> Only affiliates born before 1974.<sup>e</sup> Costa Rica introduced voluntary retirement accounts in 1996 but made private individual retirement saving mandatory as a complement to the DB system in 2000.<sup>f</sup> The payroll tax rate was subsequently reduced during the crisis of the late 1990s and early 2000s.<sup>g</sup> Mexico has offers some non-contributory schemes at the subnational level. N/A means not available. Source: Adapted from Gill et al. (2005) and updated by the authors.

7
# Infrastructure and Finance: Evidence from India's GQ Highway Network<sup>*</sup>

ABHIMAN DAS EJAZ GHANI ARTI GROVER IIM Ahmedabad World Bank World Bank WILLIAM KERR RAMANA NANDA Harvard Business School Harvard Business School

> *Author   institutions   and contact details: Das: Indian Institute of Management Ahmedabad, abhiman@iima.ac.in; Ghani: World Bank, eghani@worldbank.org; Grover Goswami: World Bank, agrover1@worldbank.org; Kerr: Harvard University, Bank of Finland, and NBER, wkerr@hbs.edu; Nanda: Harvard University and NBER, RNanda@hbs.edu. Acknowledgments: We are grateful to seminar participants for helpful suggestions/comments. We are indebted to Louis Maiden, Katie McWilliams, Sarah Elizabeth Antos, and Henry Jewell for excellent data work and maps. Funding for this project was graciously provided by a Private Enterprise Development in Low-Income Countries grant by the Centre for Economic Policy Research, Harvard Business School, and the World Bank's Multi-Donor Trade Trust Fund. Das was with the Reserve Bank of India (RBI), when this project was initiated. The views expressed in the paper are not the views of the RBI, or of any institution the authors are currently associated with.
Policy Research Working Paper 4993

# **Abstract**

The Egyptian Social Fund for Development was established in 1991 with a mandate to reduce poverty. Since its inception, it has disbursed about $2.5 billion, of which nearly two-fifths was devoted to supporting microcredit and financing community development and infrastructure. This paper investigates the size of the impact of the Fund’s interventions, whether the benefits have been commensurate with the costs, and whether the programs have been targeted successfully to the poor. The core of the impact evaluation applies propensityscore matching to data from the 2004/2005 national Household Income, Expenditure and Consumption Survey.

The authors find that Egypt’s Social Fund for Development programs have had clear and measurable effects, in the expected direction, for all of the programs considered: educational interventions have reduced illiteracy, health and potable water programs have lowered household spending on health, sanitation interventions have cut household spending on sanitation and lowered poverty, and road projects have reduced household transportation costs by 20 percent. Microcredit is associated with higher household expenditures in metropolitan areas and urban Upper Egypt, but not elsewhere.

The Social Fund for Development’s road projects generate benefits that, by some estimates, exceed the costs, as do health and potable water interventions; this is less evident for interventions in education and sanitation. The Fund argues that its mission is primarily social, and so should not be judged using a cost-benefit analysis. The Fund support for microcredit is strongly pro-poor; the other programs analyzed have a more modest pro-poor orientation.

This paper—a product of the a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the cost-effectiveness of rural development interventions such as social fund of Egypt. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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
# **App. Table 3: Separate analyses of Table 7 by split samples**

||Initial<br>contracts|Initial<br>restricted to<br>repeat users|Subsequent<br>contracts|2008 and<br>prior|2009 and<br>later|High-end<br>contracts|Low-end<br>contracts|Excluding<br>employer<br>searches|Only<br>employer<br>searches|Workers with<br>good English<br>skills|Workers with<br>poor English<br>skills|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
||||Es|The<br>timates include<br>P|sample is cont<br>fixed effects f<br>anel A: DV is|racts formed wi<br>or year x job ty<br>log hourly wag|th workers in<br>pe x country o<br>e paid to work|India<br>f company cont<br>er|act|||
|Probability that contract|-0.040**|-0.045*|-0.029|-0.027|-0.031|-0.039*|-0.0105|-0.019|-0.049|-0.026|-0.054|
|is ethnicity-based|(0.020)|(0.028)|(0.024)|(0.027)|(0.023)|(0.021)|(0.037)|(0.020)|(0.030)|(0.019)|(0.055)|
|Observations|11,489|6,656|34,166|14,099|31,557|32,628|13,028|26,681|18,975|38,462|4,526|
|Mean of DV|2.218|2.185|2.086|2.375|2.005|2.392|1.438|2.010|2.274|2.145|2.038|
|Relative effect|-0.018|-0.021|-0.014|-0.011|-0.015|-0.016|-0.007|-0.009|-0.022|-0.012|-0.026|
||||Panel|B: DV is perce|ntage different|ial between acc|epted contract|and median pr|oposal|||
|Probability that contract|-0.014|-0.022*|-0.011|-0.011|-0.014*|-0.019***|-0.001|-0.028***|0.004|-0.012*|-0.019|
|is ethnicity-based|(0.009)|(0.012)|(0.008)|(0.008)|(0.008)|(0.007)|(0.013)|(0.007)|(0.010)|(0.007)|(0.015)|
|Observations|11,488|6,656|34,166|14,099|31,555|32,626|13,028|26,681|18,973|38,462|4,526|
|Mean of DV|-0.018|-0.024|-0.009|-0.006|-0.014|-0.009|-0.017|-0.023|0.005|-0.009|-0.024|
|||Panel|C: DV is a (0,1|) "good perfor|mance" indicat|or from public|feedback score|s (feedback sco|re greater tha|n 4.5/5)||
|Probability that contract|-0.005|0.009|-0.003|-0.032|0.007|-0.000|-0.016|-0.009|-0.002|-0.003|0.016|
|is ethnicity-based|(0.024)|(0.031)|(0.021)|(0.033)|(0.019)|(0.020)|(0.029)|(0.021)|(0.024)|(0.018)|(0.045)|
|Observations|9,181|5,727|26,879|12,956|23,084|25,672|10,368|20,971|15,069|30,844|3,474|
|Mean of DV|0.466|0.511|0.566|0.425|0.605|0.532|0.562|0.516|0.574|0.546|0.482|
|Relative effect|-0.011|0.018|-0.005|-0.075|0.012|0.000|-0.028|-0.017|-0.003|-0.005|0.033|
||||Pan|el D: DV is a (|0,1) "good per|formance" indi|cator from priv|ate post-job sur|vey|||
|Probability that contract|0.008|0.017|-0.002|-0.014|0.010|0.007|-0.006|-0.000|0.002|0.004|0.049|
|is ethnicity-based|(0.024)|(0.031)|(0.021)|(0.029)|(0.020)|(0.022)|(0.025)|(0.021)|(0.022)|(0.017)|(0.049)|
|Observations|9,091|5,692|26,699|12,862|22,928|25,483|10,307|20,842|14,948|30,644|3,438|
|Mean of DV|0.596|0.604|0.628|0.582|0.640|0.616|0.628|0.589|0.662|0.623|0.600|
|Relative effect|0.013|0.028|-0.003|-0.024|0.016|0.011|-0.010|0.000|0.003|0.006|0.082|

Notes: See Table 7.
**The Impact of the Business Environment on Young Firm Financing**

# **Larry W. Chavis ***

Kenan-Flagler Business School University of North Carolina at Chapel Hill Phone: (919) 962-8215 E-mail: larry_chavis@unc.edu Webpage: www.unc.edu/~chavisl

# **Leora F. Klapper**

Development Research Group The World Bank Phone: (202) 473-8738 Email: LKlapper@Worldbank.org Webpage: econ.worldbank.org/staff/lklapper

# **Inessa Love**

Development Research Group The World Bank Phone: (202) 458-0590 Email: ILove@Worldbank.org Webpage: econ.worldbank.org/staff/ilove

# **Keywords** :  Entrepreneurial Finance, Financing Constraints, Business Environment

**JEL Classification** : D21, G30, O12

> * Corresponding author.  The opinions expressed do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.  The authors thank the participants of the Kauffman Foundation Conference on Entrepreneurial Finance, the Cleveland Fed-Kauffman Foundation Conference on Entrepreneurial Finance, and The Second Annual Searle Center Research Symposium on the Economics and Law of the Entrepreneur.
Policy Research Working Paper 7470

# **Abstract**

The last few years have seen a growing commitment worldwide to universal health coverage (UHC). Yet there is a lack of clarity on how to measure progress towards UHC. This paper proposes a ‘mashup’ index that captures both aspects of UHC: that everyone—irrespective of their ability-topay—gets the health services they need; and that nobody suffers undue financial hardship as a result of receiving care. Service coverage is broken down into prevention and treatment, and financial protection into impoverishment and catastrophic spending; nationally representative household survey data are used to adjust population averages to capture inequalities between the poor and better off; nonlinear tradeoffs are allowed between and within the two dimensions of the UHC index; and all indicators are expressed such that scores run from 0 to 100, and higher scores are better. In a sample of 24 countries for which there are detailed information on UHC-inspired reforms, a cluster of high-performing countries emerges with UHC scores of between 79 and 84 (Brazil, Colombia, Costa Rica, Mexico and South Africa) and a cluster of low-performing countries emerges with UHC scores in the range 35–57 (Ethiopia, Guatemala, India, Indonesia and Vietnam). Countries have mostly improved their UHC scores between the earliest and latest years for which there are data—by about 5 points on average; however, the improvement has come from increases in receipt of key health interventions, not from reductions in the incidence of out-of-pocket payments on welfare.

This paper is a product of the Human Development and Public Services Team, Development Research Group; and the Health Nutrition and Population Global Practice Group. 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 authors may be contacted at awagstaff@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
||_~~x~~_|σ|S<sup>a</sup>|K<sup>b</sup>|SW<sup>c</sup>|Min.|Max.|N|
|---|---|---|---|---|---|---|---|---|
|US$/MXP|-0.0061<sup>d</sup>|0.0026|-1.6018|19.43|0.8285<sup>*</sup>|-0.0243|0.0155|1,282|
|US$/TL|-0.0969<sup>d</sup>|0.0119|5.6253|67.41|0.6564<sup>*</sup>|-0.1454|0.0546|317|
|BRADY<sup>e</sup>|-0.0033|0.3229|-0.1581|17.16|0.7933<sup>*</sup>|-3.0400|2.3700|1,282|
|ON<sup>f</sup>|-12.525|164.31|16.0202|267.88|0.0802<sup>*</sup><br>|-2,823.3|4.0000|317|
|Short<sup>g</sup>|-130.40|128.00|-0.6873|-0.6063|0.8362<sup>*</sup>|-400|0|1,282|

*Reject the null at the 1% level. a S=Skewness; b K=Kurtosis; c SW= Shapiro-Wilk test for normality; d Numbers multiplied by 100; e BRADY is the first difference of the Mexican brady bond;<sup>f</sup> ON is the first difference of the overnight Turkish interest rate and<sup>g</sup> Short is the Target for Cumulative Balances in Mexico in millions of Pesos.

Log-returns present excess kurtosis and significant departures from normality as indicated by the Shapiro-Wilk test. The distribution of the Turkish lira is biased to the right, and the peso to the left.

Figure 3: US$/MXP exchange rate<sup>a</sup> Figure 4: US$/MXP exchange rate returns<sup>a</sup> <!-- Start of picture text --> 0.16<br>0.15<br>0.14<br>0.13<br>0.12<br>0.11<br>0.1<br>0.09<br>8/1/1996 8/1/1997 8/1/1998 8/1/1999 8/1/2000 8/1/1996 3/1/1997 10/1/1997 5/1/1998 12/1/1998 7/1/1999 2/1/2000 9/1/2000 4/1/2001<br>0.03<br>0.02<br>0.01<br>0.00<br>-0.01<br>-0.02<br><!-- End of picture text --> Figure 5: US$/TL exchange rate<sup>b</sup> Figure 6: US$/TL exchange rate returns<sup>b</sup> <!-- Start of picture text --> 22.02.2001 22.05.2001 22.08.2001 22.11.2001 22.02.2002 22.05.2002 22.02.2001 22.05.2001 22.08.2001 22.11.2001 22.02.2002 22.05.2002<br>0,05<br>1,4<br>0,00<br>1,2<br>-0,05<br>1,0<br>0,8 -0,10<br>0,6 -0,15<br><!-- End of picture text --> a: August 1996–June 2001, contingent sales in vertical lines; b: 22 February 2001– 29 May 2002.

16
# **Annex H – Regression Model Details**

## **Table 6: Fay-Herriot Model for Poverty Rate at $5.5/day and $3.2/day**

|Poor<br>$5.5/day||Poor<br>$3.2/day|
|---|---|---|
|(1)||(2)|
|No local service road max value<br>1.0000***|No local service road max value|1.0000***|
|(0.0000)||(0.0000)|
|Grid Mean GDP<br>0.9559***|Minimum air temperature|1.0088***|
|(0.0103)||(0.0012)|
|Grid Mean Max<br>0.9981***|Maximum elevation slope|1.0024***|
|(0.0005)||(0.0006)|
|Air temperature max<br>1.0115***|Grid minimum GDP|1.4635***|
|(0.0017)||(0.1916)|
|Grid Minimum GDP<br>1.7417***|500m Elevation|1.0001***|
|(0.3025)||(0.0000)|
|Minimum population count<br>1.0002**|Grid mean GDP|0.9702***|
|(0.0001)||(0.0083)|
|500m Elevation<br>1.0003***|Constant|1.0155|
|(0.0001)||(0.0155)|
|Constant<br>1.1588***<br>(0.0373)|||
|Observations<br>331|Observations|331|
|R-squared<br>0.6984|R-squared|0.4538|
|*** p<0.01, ** p<0.05, * p<0.1; selected<br>country/region dummy variables not shown|*** p<0.01, ** p<0.05, * p<0.1; selected<br>country/region dummy variables not show|n|
report GIS codes for primary sampling units (PSUs). This permits us to create a more precise measure of the density of open defecation to which an individual child is exposed than we were able to use in the international data, and to control for fixed effects at the district level, which is a much smaller geographic area than the region that was used in the international analysis. Second, Bangladesh experienced a rapid decline in open defecation over the period we study. According to WHO-Unicef statistics, national open defecation declined from 20.6 percent in 1999 to 3.9 percent in 2011 (UNICEF & WHO, 2012). As a result, much of the variation we use to identify the effect of the interaction of sanitation and population density on child height results from a reduction in the density of open defecation over time.

# **4.1 Data and summary statistics**

We combine data from the 1999, 2004, and 2011 Bangladeshi DHS surveys, as well as from two Bangladesh censuses, to investigate the relationship among open defecation, population density, and child height. To do this, we match the PSUs of children in the DHS to political boundaries using GIS codes.

There are three levels of political disaggregation within Bangladesh. Most coarsely, Bangladesh is divided into 7 divisons. Divisions are the sub-national region coded in DHS data; we refer to these as regions for consistency with section 3. Regions are divided into districts, which are not reported in the DHS. There are a total of 64 districts in Bangladesh; the average district has a population of about 2 million people. Districts are divided into subdistricts, which are then divided into Unions (rural), Wards (parts of cities), or Pourashava (towns), which we abbreviate UWP. The average UWP had 339,906 people in the 2011 census.

Each PSU in the Bangladesh DHS surveys is accompanied by a GIS code, which is publicly available on request, which includes the latitude and longitude of the PSU.<sup>10</sup> We used ArcGIS

> 10We drop two PSUs from the 2004 DHS where GIS information was not reported.

18