rafmacalaba/datause-encoder-data huggingface dataset:
Tables 1 and 2 contain some descriptive information on the distribution of export flows across exporters and importers at the beginning and the end of our sample period. It is immediately obvious from Table 1 that both export birth and death rates are high across the sample. On average, about 15 percent of all export relationships prevailing in a given year disappear before the next year begins. Developed country exporters typically experience death rates of less than 10%, whereas developing country exporters face much higher fatality rates with countries such as Mozambique and Zimbabwe experiencing average death rates of 50% or more. The information in Table 2 shows considerable variation in the frequency of the death rate of inflows across importers. Hence, it is important to explain considerable heterogeneity across both exporters and importers in the survival rates of trade flows.

To gain a better understanding of the pattern of duration dependence present in the data, we proceed to investigate non-parametric estimates of the survivor function. Letting T denote the time to a failure event for a particular trade flow, we define the survivor function S (t) as the cumulative probability of surviving up to some point in time t. Our data on the duration of export flows are expressed in annual observations. This means that we only know if an export flow is recorded during the year. For flows that cease in a particular year, all we know is their duration expressed in annual increments. For those flows that survive, the available information is that their duration exceeded the lower limit of the last observed duration. In effect we are working with grouped survival time data, and so we use the life table method to estimate the survival function nonparametrically. This method uses the same idea as the Kaplan-Meier product-limit estimator for continuous time data but adjusts the estimate of the survivor function for the possibility that export flows may have ended at any point during the time interval. This boils down to using an average estimate centered on the midpoint of the interval.

Defining _d j_ as the number of failures observed in interval<sup>_i_,</sup> _j N j_ as the number of spells at risk of failure at start of the interval and<sup>_n_</sup> _j_<sup>as the adjusted number of spells at risk of</sup> 6
# **1.** **<mark>Introduction</mark>**

<mark>The internet, mobile phones, and related technologies that facilitate the collection, storage, analysis and sharing of data and information are changing many aspects of life among a large and growing share of the world’s population. These digital technologies have been spreading rapidly. Even among the poorest 20 percent in developing countries, 70 percent have access to mobile phones (World Bank 2016). This is more than have access to improved sanitation or electricity in their homes. More than 40 percent of the global population has internet access and there are major initiatives underway to connect those still unconnected—the great majority of them in rural areas in developing countries.</mark> <mark>Digital tools generate immediate private benefits as people can more easily communicate with friends and family and access new forms of leisure and new sources of information. These are highly valued benefits that explain the fast adoption of mobile phones even among those with few means. A larger question is whether new technologies also significantly promote economic development. There have been many claims of immediate and large effects, not least from politicians and technology boosters in industry. Solid empirical evidence based on careful identification of impacts is much harder to come by.</mark> <mark>There are indeed a number of compelling examples across sectors of the economy where technology has improved economic prospects, helped support livelihoods and facilitated service delivery. In agriculture, Jensen’s seminal study of Kerala fishermen provided a clean identification of significant impacts of mobile phones on earnings, price volatility and reducing waste (Jensen 2007). Aker’s work on small-scale African farmers showed significant time and cost savings in using information and communication technology (ICT) for extension services (Aker 2011). At the other end of the productivity spectrum, modern large-scale agriculture is becoming unthinkable without such precision agriculture tools as GPS, satellite and drone monitoring, and increasingly detailed and instantly available weather and climate information (Oliver, Robertson, and Wong 2010).</mark> <mark>At the same time, despite many individual success stories in many sectors of the economy, there has been surprisingly limited evidence of aggregate improvements in important development outcomes. Globally, productivity growth has slowed, inequality is a rising concern not just in</mark> 2
- 10 -

Treasury bill rate, disposable income, the federal deficit and the GNP deflator for nonfarm sales. Just (1989a) simulates with USAGMKTS the effects of three kinds of agricultural policy change: 1) changes of plus and minus ten percent In price supports for feed grains, 2) changes of plus and minus ten percent in both price supports aid target prices for feed grains and 3) a reduction of ten percent In the acreage Jiverslon requirement for feed grain program participants. These experkients show that these agricultural policy Instruments have substantial effects on U.S. agricultural prices. Plausible U.S. policy adjustments such as the abovementloned changes can cau3e world market prices to change by 10 to 15 percent depending on the current state of the U.S. agricultural economy.

VI Experknents to Esthkate the Sensitivity of Prices, Production and Traded Quantities In Mexican Agricultural Markets to U.S. Agricuttural Pollcy For analytical clarity, It Is useful to study Mexican agricultural markets using two polar modes of operation: 1) markets wlth decoupled linkages to world prices and quantity adjustment by the government, i.e., the Mexican policy choice of recent decades, and 2) markets open to trade at world prices. As one would expect, simulation of the historical policy choice, decoupled markets, shows no direct response whatever on Mexican agricultural markets In response to the U.S. agricultural policy changes described in sectlon V. Of course, to the extent that U.S. pollcy changes affect world prices, there will be an Indirect effech. on the budget of the Mexican government due to the effect of changed prices on the foreign exchange value of the trading needed by the government to achieve supply adjustments on domestic markets Including any adjustments of Mexican
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<!-- Start of picture text --> In-sample (1995-2005) Predictive Performance<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Q1 Q2 Q3 Q4<br>Project Life, % by:<br>Disbursements Time<br>Prediction Model<br>Comparison of the in-sample predictive performance during different stages (quarters) of<br>Out-of-Sample (>2005) Predictive Performance<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Q1 Q2 Q3 Q4<br>Project Life, % by:<br>Disbursements Time<br>Prediction Model<br>% of unsatisfactory correctly predicted by the ISR-DO rating<br>% of unsatisfactory correctly predicted by the ISR-DO rating<br><!-- End of picture text -->

**Figure 2a:** Comparison of the in-sample predictive performance during different stages (quarters) of project life.

**Figure 2b:** Comparison of the out-of-sample predictive performance during different stages (quarters) of project life.

11
17 89 dummy variable are always Tiegative, except for the urban population. But the coefficients for 1980-84 are mixed: they are positive for children of women with no education, rural women with no education, and urban women with no education. The coefficients for the pre-adjustment period are all negative (except for a large positive coefficient for children of women with secondary or higher education, a value probably reflecting small numbers of sample cases). The coefficients for the post-adjustment period are positive for children of urban women, women with primary education, and urban women without education. Though none of the coefficients is significant, the relative change model is also consistent with a positive effect of adjustment on child mortality for the urban poor, but a negative effect for both rural mothers and rural poor mothers.

The analysis reported in table 2 leaves much to be desired. The coefficients shown are generally not significant, the amount of variance accounted for is very small in the relative change model, and there are some aberrant values, probably as the result of small numbers. Perhaps most important, an aggregate analysis of this sort can only suggest causal links because of the vast number of factors not controlled for. As a partial answer to this last concern, we now turn to an individual-level analysis with a much more clearly defined economic model underlying it; the model will be illustrated using data from the Cote d'Ivoire Living Standards Measurement Survey.

# Conceptual Framework; Child Health Investment in Sub-Saharan Africa

For analytical purposes, the notion of child health is limited to a child's capacity to combat disease. Most indicators of child health used to measure
# XIV INTRODUCTION

Figure 1 Annual Increments to Female Life Expectancy (in years), by Initial Level, Compared with U.N. and Previous World Bank Pattems <!-- Start of picture text --> o0.9<br>0.7-<br>0.6-<br>0.6 - ~~CURRENT<br>0.5 U<br>0.4<br>0.3-<br>0.2-<br>0.1<br>35 40 45 S0 55 60 65 70 75 80 85<br>INITIAL LIFE EXPECTANCY<br><!-- End of picture text --> Figure 2 Annual Decrements to Infant Mortality Rate (per thousand), by Initial Rate, Compared with the U.N. and Previous World Bank Pattems <!-- Start of picture text -->
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**Table C8: Number of prefectural level regions by macro-region (** **_REGION_ ) and share of national urban employment (**  **)**

||**Share of national urban**<br>**employment (****)**|**0.000757**|**0.001537**|**0.002533**|**0.005257**|
|---|---|---|---|---|---|
|**_REGION_**|**_Macro-region_**|||||
|**METRO**|Metropolitan areas|0|0|0|3|
|**NE**|North East|4|14|11|7|
|**CO**|Coastal|4|9|22|40|
|**CE**|Central|8|20|26|16|
|**NW**|North West|32|9|7|2|
|**SW**|South West|34|31|17|15|

The other significant interaction – the _REGION_ * _S_<sup>_R_</sup> interaction for the impact of the NEN on real rural wages – is evident in Table C9. This again indicates that the differentiated effect according to macro-region itself varies according to how urban or rural a prefectural level region's economy is. It is evident that, for regions in the lowest category of _S_<sup>_R_</sup> (i.e. the least rural, most urbanised regions), the impact on real rural wages is relatively low and even negative for the three metropolitan areas. Furthermore, with the exception of the 11 least rural regions in Central, this appears to be the case regardless of macro-region. This indicates that the impact of the NEN on real rural wages does not receive a stimulus from the presence of a diverse urban economy. The direct intra-regional linkage across rural and urban sectors appears to be currently quite underdeveloped, although there is a suggestion of spatial spillover between regions. The generally faster overall economic NEN induced growth in Eastern regions is evident from the comparatively high growth across more rural Coastal regions, especially the eight most rural prefectural level regions in Coastal.

**Table C9: Impact on real rural wages by macro-region (** **_REGION_ ) and rural share (** **_S_**<sup>**_R_**</sup> **)**

||**Rural sector share of**<br>**total employment (****_S_**<sup>**_R_**</sup>**)**|**0.472**|**0.676**|**0.786**|**0.858**|
|---|---|---|---|---|---|
|**_REGION_**|**_Macro-region_**|||||
|**METRO **|Metropolitan areas|-2.647|*|*|*|
|**NE**|North East|0.558|2.340|3.800|*|
|**CO**|Coastal|0.518|4.909|4.768|6.746|
|**CE**|Central|2.858|3.783|4.383|5.861|
|**NW**|North West|1.274|1.919|2.532|2.899|
|**SW**|South West|0.796|2.228|3.871|3.618|

**Table C10: Number of regions by macro-region (** **_REGION_ ) and rural share (** **_S_**<sup>**_R_**</sup> **)**

||**Rural sector share of**<br>**total employment (****_S_**<sup>**_R_**</sup>**)**|**0.472**|**0.676**|**0.786**|**0.858**|
|---|---|---|---|---|---|
|**_REGION _**|Macro-region|||||
|**METRO **|Metropolitan areas|3|0|0|0|
|**NE**|North East|20|13|3|0|
|**CO**|Coastal|19|28|20|8|
|**CE**|Central|11|15|24|20|
|**NW**|North West|17|8|14|11|
|**SW**|South West|12|19|22|44|

47
- Moya, A., and M. Carter. 2019. “Violence and the Formation of Hopelessness and Pessimistic Prospects of Upward Mobility in Colombia.” _World Development_ 113: 100–115.

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32
# **5.2   Attrition Analysis**

An important concern for any impact evaluation is the fact that participants sometimes drop out of the study before its completion. Our baseline and endline survey records show an overall attrition rate of 20 percent after three years. This rate is in line with the attrition rates found in other similar randomized studies of this type.<sup>40</sup> In addition to the fact that sample size is reduced, when attrition is systematically related to the outcomes under study or to the program itself, it can bias the estimates of the causal effects of the interventions studied. We followed the standard procedures in the impact evaluation literature to address this potential problem.

First, we explored whether the attrition rates differed between the treatment and comparison groups for each of the interventions studied. As Appendix 2 shows, the proportion of households that stayed in each group ranges from 0.72 to 0.84, and we cannot reject the null hypothesis of equal level of attrition in the control and treatment groups at conventional significance levels in any of the samples studied.

The fact that there was no differential attrition in the treatment and comparison groups is consistent with the hypothesis that attrition is ignorable, and it suggests that the estimate of the treatment effects will not be biased unless different types of households dropped out of the sample in the treatment and the comparison groups.<sup>41</sup> For example, if households with healthier children were more likely to drop out when they were assigned to the treatment group, this could bias the estimator of the program effect on health outcomes downward. Fortunately, this does not seem to be the case in our study. Baseline balance was achieved for the entire initial sample in most of the variables (see Appendix1), and in Appendix 2 we can see that baseline balance is also high for a set of variables for the non-attriters’ sample. This implies that those remaining in the sample and the attriters were balanced in terms of observable characteristics.

> 40 For example, Banerjee et al. (2007) find attrition rates of 17 and 18 percent, respectively, in the comparison and treatment groups in Vadodara (India) in the first year when they evaluated _Balsakhi_ education program in India.

> 41 Angrist 1996

20
engineering, increases in the presence of foreign graduate students have a positive and significant impact on future U.S. patent applications and grants awarded to both firms and universities. This finding extends to the relative presence of skilled immigrants in the labor force, with an increase in the skilled immigrant share significantly raising later patent awards in both types of institutions. Put simply, we find that both enrollment of foreign graduate students and immigration of skilled workers have a strong and positive impact on the development of ideas in the United States.

The paper proceeds as follows. In the next section we review literature that motivates this study. In Section Three we develop a simple model and set out the econometric specification. In Section Four we provide results and discuss their economic and policy significance. In Section Five we conclude.

# **2. Background and Literature Review**

The question of whether skilled and other forms of immigration bring net benefits is much discussed in media and policy circles in rich countries.<sup>6</sup> It is even the subject of negative, and rather polemical, pieces by well known scholars (Borjas, 2002, 2004; Huntington, 2004). It is evident that a major component of such gains must be the contribution of skilled immigrants and students to an economy’s capacity to innovate and raise productivity. As noted earlier, the question has not been the subject of systematic empirical analysis. However, there are related strands of literature that help motivate our analysis.

> 5 For comparison with other countries see the results of the Trends in International Mathematics and Science Study (TIMMS) at http://timss.bc.edu/timss2003.html.

> 6 See, for example, “The Politics of Immigration: Business v. Bush,” _The Economist_ , October 18-24, 2003, pp. 2930, and “German Immigration: Brains Not Welcome Here,” _The Economist_ , May 1-7, 2004, p. 50.

3
1 WPS3941

# **Water Markets, Demand and Cost Recovery for Piped Water Supply Services: Evidence from Southwest Sri Lanka**<sup>**1**</sup>

## **Céline Nauges and Caroline van den Berg**<sup>**2**</sup>

_In many countries water supply is a service that is seriously underpriced, especially for residential consumers. This has led to a call for setting cost recovery policies to ensure that the tariffs charged for water supply cover the full cost of providing the service. Yet, the question arises how consumers will react to such price increases. We illustrate the impact of price increases on consumption of piped water through a study of the demand for water of piped and non-piped households using cross-sectional data from 1,800 households in Southwest Sri Lanka. The (marginal) price elasticity is estimated at -0.74 for households exclusively relying on piped water, and at –0.69 for households using piped water but supplementing their supply with other water sources, with no significant differences between income groups. Those households that depend on non-piped water sources have a time cost elasticity (as a proxy for price elasticity) of only -0.06. We discuss the implications of these results in terms of pricing policy._

## World Bank Policy Research Working Paper 3941, June 2006

_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 Financial support from the Bank-Netherlands Water Partnership for Water Supply and Sanitation (BNWPWSS), a facility that enhances World Bank operations to increase delivery of water supply and sanitation services to the poor, is gratefully acknowledged.

> 2 Céline Nauges is a Senior Research Fellow at the French National Institute for Research in Agriculture at the University of Toulouse.  Caroline van den Berg is a Senior Economist at the Energy and Water Department of the World Bank. The opinions reflected in this paper are the opinions of the authors and not opinions of the World Bank, the French National Institute for Research in Agriculture, or the BNWP-WSS.