rafmacalaba/datause-encoder-data huggingface dataset:
**External Validity** The IFC’s long history and broad geographical diversification enable us to paint an unusually rich picture of emerging market private equity investment. But we acknowledge several caveats.

First, the IFC, although a single investor, generally co-invests with private sector investors, rarely taking more than 25 percent of the value of a project. We therefore view its portfolio as informative about the returns available to private investors in the markets in which it operates. Moreover, over the past three decades, the IFC has increasingly invested indirectly through private equity funds, which are also marketed to institutional investors. Though IFC’s direct investments may not be available to all investors, the returns to the IFC’s fund investments provide a credible benchmark of what could be obtained by a diversified investor in emerging market private equity funds.

Second, although its charter prohibits it from taking government guarantees, it is possible that the IFC’s status as part of the World Bank Group provides additional protection from expropriation. Realized returns may not always therefore be representative of what is available to independent investors.

Third, if the IFC had special immunity to capital controls, its returns would be less representative of the private investment market. Our understanding is that the IFC does not have special status vis-à-vis capital controls, nor do its co-investors. From this perspective, the relationship between capital controls and investment performance we observe in the dataset is representative of the opportunity set available to other foreign investors.

Fourth, as can be seen in Figure 1, Panel C, the IFC’s operating expenses have declined from approximately 2 percent of assets from 1964 to 1988 to 1.4 percent of assets at present. These costs include public policy work and technical assistance for investments as well as investment costs associated with the debt portfolio, which may be lower than for the equity portfolio. Because it is not possible to accurately apportion fixed costs to each investment, and because the IFC engages in significant non-investment activities like research, we assess individual investment performance on a gross basis, without subtracting operating expenses. When assessing the performance of the complete portfolio, we report additional results which reduce returns by subtracting a hypothetical “two and twenty” management fee to direct investments.

# **3.2 Private Equity Investment Data**

**Cash Flows.** Our primary data are the complete set of cash flows to and from all 2,509 equity investments (in companies or funds) from the IFC’s founding in 1956 until June 30th, 2019.<sup>7</sup> The data include the month of each cash flow, the exact value in U.S. dollars, and the most recent mark-to-market valuation of investments still held in the portfolio. Each investment’s “vintage 7Because we focus on cash flows exclusively related to equity investments, we do not include investments that include both equity and debt components (e.g., convertible loans). We leave analysis of the IFC’s debt investments to future research.

8
governance reforms are yet to be fully designed in terms of their exact content and implementation strategy.

**_The government has rightly acknowledged that “building service-oriented government” is a comprehensive undertaking_** . It has been recognized that this undertaking needs to involve conceptual changes and fundamental transformations in laws, rules, public resource allocation and management, accountability mechanisms, institutional arrangements and capacities, and administrative systems.  Making these changes will require defining concrete tasks with clear objectives, technically sound approaches, and practical measures.

**_Reflecting China’s strategic priority to create a Harmonious Society and build serviceoriented government, this study was launched to assist the Government of China in improving governance for public service delivery_** . The study concentrates on urban services in the education, health and water sectors.  It employs innovative approaches, including a citizens’ scorecard survey to gauge citizens’ satisfaction with public services, and case studies to explore the related governance issues, emphasizing the financing mechanisms and accountability relationships for public service delivery.

# **Citizen scorecard survey – rationale and international experience**

**_The citizen scorecard consists of a carefully designed questionnaire, which is used in a survey to gather citizens’ feedback_** .<sup>3</sup> In partnership, Chinese and international experts designed the questionnaire and sampling framework in a scientific manner, using rigorous techniques as well as sound technical expertise and knowledge of sector-specific issues.

**_With respect to public services, the citizen scorecard survey explores citizens’ perceptions to pinpoint good and bad practices in the surveyed sectors and localities, expose problems in policy implementation, and identify any related factors contributing to social harmony or sources of social tension_** .  The survey also helps raise citizens’ awareness with respect to public services and the related policies, and can serve as a channel for citizens to communicate their experience to the decision makers.  International experience suggests that when used by a government or civil society, a citizen report card survey helps assess and enhance governance for public service delivery (see Box 1).

**_The next section presents the main findings of the citizen scorecard survey conducted in year 2006 in Chengdu, Dalian, Shanghai-Pudong, Shenzhen, and Xi’an_** .  In these five cities, the survey covered about 5,000 households, and gathered their experience and opinion on public services.  The National Bureau of Statistics jointly with several academic and research institutions took the lead on the survey.

> 3 In essence, citizen report card survey is similar to an investment climate survey.  While investment climate survey collects feedback from firms, citizen report card survey interviews citizens.

12
# _1. Introduction_

In 1948, Kosovo was among the poorest regions of the newly established Socialist Former Republic of Yugoslavia (SFRY) in more than one dimension. Agriculture accounted for over 50 percent of GDP. Average illiteracy rates were in excess of 70 percent, and over threequarters for the Albanian population, which is the predominant ethnic group in Kosovo'. Even as recent as 1988, although industry and mining comprised nearly half of the total national product with agriculture down to 20 percent, output per head in Kosovo was only 28 percent that of the SFRY (World Bank, 2001). Health care statistics with the exception of life expectancy were also among the lowest in Europe (World Bank, 2001b).

During the period of relative autonomy and increased access to Albanian language education from 1975, Kosovo made large advances in the area of education. There is evidence that, in the ten years beginning in 1968, the proportion of Kosovar Albanians in the education system increased from 38 to 72 percent. Moreover, illiteracy dropped to below 10 percent (Malcolm, 1998). The suspension of autonomy in Kosovo in 1989 brought an end to these improvements in the education sector. The consequential imposition of the Federalist government's control on the governance and curriculum of the education system resulted in a majority of the Kosovar Albanians leaving the formal education system instead enrolling into the "parallel education system" from 1991.

The 1999 conflict between the Serbs and Albanians further affected the living standards of the population. Industrial output collapsed, agricultural activities came to a halt and livestock

> ' Individuals of Albanian and Serb etmnicity make up a majority of Kosovo's population with Albanians comprising 90 percent of the population (UNHCR, 2000). Other minority groups include Roma, Turks and other Slavic groups. 1
<!-- Start of picture text --> IDEB Elementary & Single-Shift<br>Diff in Diff<br>Oo<br>ee aww---<br>ia) =<br>5 et<br>= wank<br>5 _leee=t<br>as<br>=<br>on<br>2005 2007 2009 2011 2013 2015<br>Year<br>----XK---- Control (448 schools)<br>———__ Became Single-Shift in 2011 (3 schools)<br>——®—_ Became Single-Shift in 2012 or 2013 (74 schools)<br><!-- End of picture text -->
WPS4258

# **The Cost of Being Landlocked: Logistics Costs and Supply Chain Reliability**

Jean-François Arvis, Gael Raballand, Jean-François Marteau The World Bank<sup>1</sup>

## **Abstract**

A large proportion of the least developed countries are landlocked and their access to world markets depends on the availability of a trade corridor and transit systems. Based on empirical evidence from World Bank projects and assessments in Africa, Central Asia and elsewhere, this paper proposes a microeconomic quantitative description of logistics costs. The paper theoretically and empirically highlights that landlocked economies are primarily affected not only by a high cost of freight services, but also by the high degree of unpredictability in transportation time. The main sources of costs are not only physical constraints but widespread rent activities and severe flaws in the implementation of the transit systems, which prevent the emergence of reliable logistics services. The business and donor community should push towards implementation of comprehensive facilitation strategies, primarily at the national level, and the design of robust and resilient transport and transit regimes. A better understanding of the political economy of transit and a review of the implementation successes and failures in this area are needed.

**Keywords:** transportation and logistics costs, trade, transit, landlocked, logistics, supply chain, Africa, Central Asia.

World Bank Policy Research Working Paper 4258, June 2007 _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 http://econ.worldbank.org._

> 1 1818 H Street NW, Washington, DC 20433, <u>jarvis1@worldbank.org, graballand@worldbank.org, jmarteau@worldbank.org</u> The authors would like to thank Robin Carruthers, Simon Thomas and Paul Brenton for comments and Ranga Krishnamani and Ann Raynal May for the editing.

1
# **Table A17. Survey Methods’ Effects on Employment Across Economic Sectors**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
||Agric<br>v<br>No W|ulture<br>s.<br>orking|Agric<br>v<br>Other|ulture<br>s.<br>Sectors|Agric<br>v<br>Ser|ulture<br>s.<br>vices|Agric<br>v<br>Manu|ulture<br>s.<br>facture|Agri<br> <br>Com|culture<br>vs.<br>merce|
|LOA|0.010|-0.022|0.018|0.036|0.006|0.009|0.021|0.014|0.025|0.075|
||(0.028)|(0.056)|(0.022)|(0.033)|(0.036)|(0.047)|(0.043)|(0.050)|(0.040)|(0.054)|
||[0.708]|[0.682]|[0.369]|[0.228]|[0.858]|[0.815]|[0.591]|[0.778]|[0.480]|[0.139]|
|ESR|-0.003|-0.050|0.038+|0.018|0.047|0.002|0.061|0.001|0.048|0.038|
||(0.030)|(0.058)|(0.024)|(0.033)|(0.037)|(0.046)|(0.046)|(0.054)|(0.043)|(0.055)|
||[0.906]|[0.382]|[0.087]|[0.571]|[0.175]|[0.956]|[0.165]|[0.979]|[0.212]|[0.503]|
|LOA x Female||0.051||-0.039||-0.008||0.024||-0.110|
|||(0.063)||(0.043)||(0.069)||(0.094)||(0.068)|
|||[0.419]||[0.329]||[0.903]||[0.796]||[0.110]|
|ESR x Female||0.072||0.044||0.129 *||0.153+||0.021|
|||(0.064)||(0.044)||(0.072)||(0.102)||(0.071)|
|||[0.272]||[0.317]||[0.069]||[0.103]||[0.775]|
|LOA + LOA x Female||0.029||-0.004||0.001||0.038||-0.034|
|||(0.029)||(0.028)||(0.052)||(0.080)||(0.051)|
|||[0.306]||[0.910]||[0.984]||[0.607]||[0.496]|
|ESR + ESR x Female||0.022||0.062**||0.131**||0.154**||0.059|
|||(0.031)||(0.032)||(0.057)||(0.085)||(0.056)|
|||[0.440]||[0.034]||[0.022]||[0.036]||[0.271]|
|Observations|1,085|1,085|1,530|1,530|885|885|535|535|696|696|
|Outcome(Control Group)Mean|0.241|0.241|0.174|0.174|0.314|0.314|0.515|0.515|0.380|0.380|

_Notes:_ This table presents the treatment effects of including the LOA module (LOA) or enforcing self-reporting (ESR) by sex for the extensive and intensive margins in the reporting of employment in the agricultural sector. Columns (1) and (2) present the results on an indicator that takes the value of 1 if the individual is working in agriculture and 0 if the individual is not working. Columns (3) and (4) show the results for an indicator equal to 1 if the individual reports being working in agriculture and 0 if the individual is working in other sectors (e.g., manufacture, services, and industry). The outcome in Columns (5) through (10) is measured as an indicator that takes the value of 1 if the individual reports being working in agriculture and 0 if the individual is working in services (Columns [5] and [6]), manufacture (Columns [7] and [8]), or commerce (Columns [9] and [10]). The vector includes the indicator and the list of control variables selected using a double-LASSO procedure for each outcome. All estimations include EA (stratification variable) fixed effects. Standard errors clustered at the household level are iihss iihss presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** XX _p_ <0.01, ** _p_ <0.05, * DD _p_ <0.1, + _p_ <0.102 54
Urdu (the vernacular) and Mathematics. We then matched the children who were tested in the school to children in our household survey, eventually yielding a sample of 676 children for whom we have both testscores and household survey data. We use item-response scaled scores as our measure of learning achievement.<sup>7</sup> Table 2, Panel C shows a strong correlation between maternal education and child test-scores for this smaller sample. The difference of 0.43 standard deviations in English, 0.25 in Mathematics and 0.35 in Urdu corresponds to roughly 1 additional year of learning in these villages. This is a first indication of a link between maternal education and child cognitive outcomes.

# **Section 2: Econometric Specification and Identification strategy**

To establish causal links between maternal education, time-use and test-scores, we require variation in maternal education that is arguably exogenous to her ability. Our instrumentation strategy follows an established literature first proposed by Card (1999) that uses maternal access to a school in her _birth_ village at the time of her enrollment decision as an instrument for educational attainment. We obtain the birth village using verbal recall in interviews with mothers, and then match this with the census directory of villages, the national census of schooling conducted by the Government of Pakistan and the Educational Management Information System data collected by the Government of Punjab and the National Education Census 2005. This allows us to obtain the year of formation of schools in all villages in Pakistan.

We match 85 percent of all mothers (1,437 of 1,697) with their birth village information. The primary reason for missing mothers was that in the data entry process, the English spelling of the village name was often spelt in different ways making it harder to match to the census directory. In addition, some mothers give their village name as a residential location that is smaller than the official census village designation.<sup>8</sup> While there is some possibility that “more able” mothers are able to provide better verbal recall information, we do not find any correlation between the probability of a match and village or maternal characteristics. Table A1 in the Appendix provides the means of important variables for the matched and unmatched sample. For all the variables, the differences are negligible. Only differences in age are significant at the 10 percent

> 7 IRT scores ensure that change in one part of the distribution is equal to a change in another, in terms of the latent trait captured by the test. All items were modeled using the three parametric logistic (3PL) item response function and estimated using BILOG-MG. 8 The Surveyor General of Pakistan mapping information on localities does not follow the census village designation and has many localities marked that are not in the census list. A digital village area map for Pakistan does not exist!

12
# **Poisoning the Mind: Arsenic Contamination and Cognitive Achievement of Children**<sup>∗</sup>

Mohammad Niaz Asadullah<sup>1234</sup> Nazmul Chaudhury<sup>5</sup>

- 1: Department of Economics, University of Reading Business School, UK

2: ESRC Centre on SKOPE, Oxford, UK.

3: QEH Centre for International Development, University of Oxford, UK.

4: CSAE, University of Oxford, UK.

5: The World Bank

**Key words** : Arsenic, gender, schooling, subjective well-being, water pollution. **JEL classification** : I21, Z12, O12, O15.

The authors would like to thank Michelle Riboud for her valuable comments. The authors would also like to thank Sayed Rashed Al-Zayed Josh for his assistance with data processing. Note that this study does not represent the views of the World Bank or the Governments they represent.

> ∗ The authors would like to thank Michelle Riboud for her valuable comments.  The authors would also like to thank Sayed Rashed Al-Zayed Josh for his assistance with data processing.  The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.  They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Table 6: Balance: Most important plot characteristics

||Full sample||R|D sample|||
|---|---|---|---|---|---|---|
||Coef. (SE) [p]|Dep. var.||Coef.|(SE) [p]||
||(1)|(2)|(3)|(4)|(5)|(6)|
|log area|-0.108|-2.381|0.094|0.074|||
||(0.068)|(1.041)|(0.128)|(0.136)|||
||[0.114]|784|[0.460]|[0.588]|||
|Own plot|0.025|0.875|0.040|0.033|0.039|0.029|
||(0.019)|(0.331)|(0.033)|(0.039)|(0.032)|(0.037)|
||[0.174]|784|[0.226]|[0.392]|[0.232]|[0.436]|
|Owned plot _>_5 years|0.005|0.960|0.012|0.033|0.011|0.030|
||(0.014)|(0.197)|(0.024)|(0.024)|(0.023)|(0.025)|
||[0.728]|585|[0.617]|[0.175]|[0.617]|[0.233]|
|Rented out, farmer|0.013|0.033|-0.026|-0.040|-0.029|-0.041|
||(0.010)|(0.179)|(0.022)|(0.025)|(0.023)|(0.026)|
||[0.224]|784|[0.249]|[0.114]|[0.222]|[0.116]|
|Slope|0.024|0.268|0.011|-0.004|0.011|-0.006|
||(0.009)|(0.144)|(0.016)|(0.018)|(0.015)|(0.018)|
||[0.012]|784|[0.497]|[0.806]|[0.496]|[0.730]|
|Command area|0.187|0.399|-0.053|-0.079|||
||(0.032)|(0.491)|(0.058)|(0.059)|||
||[0.000]|784|[0.360]|[0.183]|||
|Omnibus F-stat [p]|7.1||0.7|1.4|0.9|1.3|
||[0.000]||[0.627]|[0.220]|[0.458]|[0.279]|
|Site FE|||X||X||
|Distance to boundary|||X|X|X|X|
|log area|||||X|X|
|MIP log area|||||X|X|
|MIP CA|||||X|X|
|Spatial FE||||X||X|

_Notes:_ Column 2 presents the mean of the dependent variable and the standard deviation of the dependent variable in parentheses, for sample plots in the main discontinuity sample that are outside the command area, and the total number of observations. Columns 1 and 3 through 6 present regression coefficients on a command area indicator, with standard errors in parentheses, and p- values in brackets. Column 1 uses the full sample, while Columns 2 through 6 use the discontinuity sample. Columns 5 and 6 use specifications (3) and (4), respectively.

significant effects on MIPs, either in our main specifications or for heterogeneity.

# **5.2 Results**

## **5.2.1 A test for separation failures**

We now present results on separation failures, demonstrating that the sample plot shock causes farmers to substitute away from their most important plot. First, we present graphical evidence of this substitution in Figure 5. As in earlier figures, distance of the sample plot to the canal in meters is represented on the horizontal 33
|TOTAL<br>20<br>28<br>28<br>20|
|---|

Table 8 shows the spread of participants with differing current property values. These broadly match by gender, with slightly more females with houses in the INR 5 million to 7 million bracket and slightly fewer in the INR 7 million to 10 million bracket.<sup>23</sup> Table 8: Achieved FGD sample by gender (F=females, M=males), value of the property and city

|**City**|||**Curre**<br>|**nt value of**<br>|**property (IN**<br>|**R**)<br>|||
|---|---|---|---|---|---|---|---|---|
||**5 million**|**or less**|**Between 5**<br>**and 7 mi**|**million**<br>**llion**|**Between 7**<br>**and 10 m**|**million**<br>**illion**|**10 million**|**or above**|
||**F**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|
|Bengaluru|2|2|2|1|4|3|0|1|
|Ahmedabad|7|5|1|0|0|0|0|2|
|Mumbai|2|3|2|2|1|3|3|0|
|Pimpri-<br>Chinchwad*|5|6|1|2|1|1|1|0|
|Bhubaneswar*|5|4|1|1|2|3|0|0|
|Delhi|0|3|4|0|1|2|3|3|
|TOTAL|21|23|11|6|9|12|7|6|

*For these cities, since a majority are under INR 5 million, a more detailed breakdown of pricing brackets under INR 5 million is given in Appendix 3.

In parallel with the focus group discussions, in-depth interviews were held with government officials from land administration authorities and tax authorities. These interviews collected insights from the officials on the impact of property-tax-related incentives on female property ownership overall and on tax revenues. The opinions shared by FGD participants were cross-referenced and contextualized using information reported by government officials.

Table 9 shows the total participation by city and designation of the officials. As can be seen, it was not possible to secure an interview with revenue officials and land administrators in Mumbai and Pimpri-Chinchwad. It was also not possible to secure an interview with a land administrator

> 23. It should be noted that current property prices and related demographics may be influenced by length of property ownership. The final sample had a good distribution of ownership length, but it had slightly more females than males with a lower monthly household income and overall skews toward a younger demographic. A full breakdown of purchase price of property, length of property ownership, average monthly household income, and age of participants appears in Appendix 3 for reference.

17