rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9: Impact of winning the lottery and of migration on household decision-making**

||(1)<br>Controlgrou|(2)<br>p ITT|(3)<br>ITT<br>(Controls)|(4)<br>ITT(IPW)|(5)<br>IV|
|---|---|---|---|---|---|
|Female: all matters|0.597***<br>(0.0105)|0.0464***<br>(0.0147)|0.0354**<br>(0.0153)|0.0356**<br>(0.0151)|0.0610**<br>(0.0256)|
|FDR q-value|||[0.009]|||
|Only female: all matters|0.106***<br>(0.00587)|0.0878***<br>(0.00987)|0.0778***<br>(0.0100)|0.0814***<br>(0.00995)|0.134***<br>(0.0161)|
|FDR q-value|||[0.001]|||
|Female: child matters|0.836***|-0.00206|-0.00705|-0.00639|-0.0121|
||(0.00887)|(0.0125)|(0.0131)|(0.0128)|(0.0223)|
|FDR q-value|||[0.070]|||
|Only female: child matters|0.293***<br>(0.0106)|0.0829***<br>(0.0147)|0.0724***<br>(0.0152)|0.0762***<br>(0.0149)|0.125***<br>(0.0252)|
|FDR q-value|||[0.001]|||
|Female: big decisions|0.513***|0.0530***|0.0420**|0.0418**|0.0724**|
||(0.0119)|(0.0165)|(0.0170)|(0.0167)|(0.0283)|
|FDR q-value|||[0.007]|||
|Only female: big decisions|0.0498***|0.0690***|0.0615***|0.0642***|0.106***|
||(0.00515)|(0.00923)|(0.00910)|(0.00902)|(0.0150)|
|FDR q-value|||[0.001]|||
|Female: HH expense related|0.595***|0.0611***|0.0476***|0.0479***|0.0820***|
||(0.0125)|(0.0172)|(0.0179)|(0.0177)|(0.0299)|
|FDR q-value|||[0.005]|||
|Only female: HH expense related|0.0825***|0.104***|0.0922***|0.0966***|0.159***|
||(0.00688)|(0.0122)|(0.0124)|(0.0124)|(0.0199)|
|FDR q-value|||[0.001]|||
|Applicant: all matters|0.573***<br>(0.0109)|-0.173***<br>(0.0155)|-0.156***<br>(0.0158)|-0.158***<br>(0.0154)|-0.268***<br>(0.0245)|
|FDR q-value|||[0.001]|||
|Index: Female decisionmaking|0|0.334***|0.287***|0.294***|0.495***|
||(0.0290)|(0.0452)|(0.0466)|(0.0458)|(0.0753)|

_Note:_ This table shows the impact of winning the lottery (T1) and of migration on female involvement in household decisions estimated using Equation (2). The first column shows the control group mean. Column (2) shows the ITT estimates with only Upazila indicators, Column (3) adds other controls (applicant height, age, religion, and parental education). Column (4) presents the estimate of the reduced form with inverse-probability-weights (IPW) estimated using the controls. Column (5) presents the 2-SLS estimates of the impact of migration. The rows indicate the outcome variables. _ihs_ refers to the inverse-hyperbolic sine transformation of the variables. The bottom most outcome is a covariance-weighted index of all other outcomes with positive values representing increased income and labor supply. Standard errors, reported in parentheses, are clustered at the union level. Column (3) also presents the (False Discovery Rate) FDR adjusted q-values that adjust for multiple-hypotheses testing. *:p>0.1; **: p>0.05; ***: p>0.01 45
# **5. Conclusion**

This survey has reviewed the existing macroeconomic level literature on the link between infrastructure and development in a critical light. It has shown that this literature suffers from several related problems. First, it often fails to lay down clearly the relevant theoretical questions to be addressed. Second, it also tends to ignore the fact that most relevant answers, from a policy point of view, cannot be meaningfully addressed with the type of data available. Within these limitations, the last section has highlighted some of the potential areas for further work, bridging the gap with related literatures such as the ones on regulation and political economy. It is clear, however, that major advances of the knowledge in this area will require both more theory and better data sets, that go beyond the macroeconomic level, to combine the existing insights with those from sector- and project-level microeconomic studies and allow policy makers to better assess the potential linkages between specific infrastructure investments and growth, and chose the right composition and sequencing of these investments. Assessing the corresponding priorities for research and data development is the object of ongoing follow-up work.

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# **APPENDIX A: DEPRIVATION INDICATORS**

**Table A1: Deprivation Indicators in the Dimensions of Education, Health, Water and Sanitation**

|**Indicator**|**Atkinson & Lugo (2010)**|**OPHI MPI (2013)**|**MDG indicators**<br>**(2008)**|**Ethiopia WMS-HCES 2000,**<br>**2005, 2011**|**2000**<br>|**2005**<br>|**2011**<br>|**Urban/**<br>**Rural**<br>**Indicator**|
|---|---|---|---|---|---|---|---|---|
|_Education of_<br>_School-aged_<br>_Children_|school deprived:<br>household has at least one<br>child 5-16 years old who<br>is not in school|any school-aged child is not<br>attending school in years 1 to 8|net enrollment ratio in<br>primary education;<br>proportion of pupils<br>starting grade 1 who<br>reach last grade of<br>primaryschool|at least one child (age 7-15) in<br>the household is not currently<br>attending school<br>2000, 2005: currently_registered_<br>in school|√|√<br>|√<br>|U, R|
|_Health Facility_<br>_Quality_||||household was dissatisfied with<br>at least one health facility visit, or<br>did not use a health facility due to<br>cost, distance, quality, or other<br>reasons||√<br>|√<br>|U, R|
|_Health Facility_<br>_Access_||||household is located more than 5<br>km away from the nearest health<br>facility (clinic, health station,<br>hospital, health post)<br>2000: health posts did not exist.|√|√|√|R|
|_Drinking Water_|water deprived:<br>household does not have<br>access to piped or other<br>protected source of<br>drinking water|household does not have access<br>to safe drinking water defined<br>as piped water, public tap,<br>borehole or pump, protected<br>well, protected spring or<br>rainwater, and it is within a<br>distance of 30 minutes’ walk<br>roundtrip|proportion of<br>population using an<br>improved drinking<br>water source|household does not use a safe<br>drinking water source defined as<br>piped water, a protected source,<br>or rainwater|√|√|√|R|
|_Sanitation_||household’s sanitation facility<br>is not improved (according to<br>MDG guidelines), or it is<br>improved but shared with<br>other households.|proportion of<br>population using an<br>improved sanitation<br>facility|household does not use an<br>improved toilet facility defined as<br>a private flush toilet or private pit<br>latrine|√|√|√|U, R|

## **Table A2: Deprivation Indicators in Gender, Aspirations and Monetary Dimensions**
While a utility may recover costs through the average tariff level, it is pertinent to examine whether all customer groups are facing cost recovery tariffs. Average revenues from residential customers are above full cost recovery level<sup>17</sup> for MERALCO and just a bit lower for BENECO ( **figure** _20_ ). MERALCO has a greater disparity between residential customers’ share of revenues and consumption than BENECO. MERALCO’s residential customers make up 30 percent of consumption and 37 percent of revenues. BENECO’s residential customers make up 52 percent of consumption and 55 percent of revenues. MERALCO’s industrial consumption (30 percent) outweighs its share of revenues (22 percent), but this relationship may be due to industrial customers imposing lower costs on the system than the average cost of service for all customer classes. Industrial consumption only makes up 0.5 percent of overall consumption for BENECO. **Figure 21** compares the percent of consumption for each customer class to the percent of revenue from that class.

**Figure 19: Average tariff revenue and cost recovery by customer group, 2016**

<!-- Start of picture text --> MERALCO BENECO<br>0.20 0.20<br>Full capital cost<br>0.15 Full capital cost 0.15<br>Limited capital cost<br>Operating cost<br>Limited capital cost<br>Operating cost<br>0.10<br>0.10<br>0.05<br>0.05<br>0.00<br>0.00<br>Source: Rethinking Power Sector Reform Project<br>Average revenues (USD/Kwh)<br>Average revenues (USD/Kwh)<br><!-- End of picture text -->

**Figure 20: Percent of revenue against percentage of consumption by customer group, 2016**

# **MERALCO BENECO**

> 17 A3 is defined as A2 plus financing costs (to the utility) and the associated O&M costs for new capital investments (based on an adequate investment prioritization framework) required to meet future demand.

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-15-

**Table 5a: Comparing Income Distribution in Bulgaria and other Selected Economies**

|Economy|Lowest<br>20%|Second<br>Quintile|Third<br>Quin.|Fourth<br>Quin.|Highest<br>20%|Highest<br>10%|
|---|---|---|---|---|---|---|
|Bulgaria',1992|9.8|13.9|17.6|22.7|36.1|21.8|
|Eastern Europe:|||||||
|Hungary, 1989|10.9|14.8|18.0|22.0|34.4|20.8|
|Poland, 1989|9.2|13.8|17.9|23.0|36.1|21.6|
|Average|8.5|13.1|17.4|22.9|38.2|23.3|
|Lower-middle<br>income economies<br>(LMIEs) Averageb|5.1|9.2|13.8|20.8|51.1|35.3|
|OECD Averagec|6.3|12.2|17.6|24.1|39.7|24.0|
|Memo Items:|||||||
|Bulgaria/E. Europe<br>Average (%)|115|106|101|99|95|94|
|Bulgaria/LMIEs<br>Average (%)|192|151|128|109|71|62|
|Bulgaria/OECD<br>Average(%|156|114|100|94|91|91|

# Source:

**a** World Bank staff estimates based on 1992 Individual Budgets of Households'.

b The average for lower-middle income economies is calculated from ' Table 30 Income Distribution and PPC estimates of GDP", World Development Report, 1993, Oxford University Press, Washington D.C., pp. 296-297. This group includes the following 12 countries: Cote d'lvorie, Philippines, Morocco, Peru, Colombia, Jamaica, Tunisia, Thailand, Costa Rica, Panama, Chile, and Malaysia.

' Similarly, the average for OECD economies is calculated from ' Table 30 Income Distribution and PPC estimates of GDP', World Development Report, 1993, Oxford University Press, Washington D.C., p. 297. This group includes the following 17 countries and the year for the income distribution estimate: Australia, 1985; Belgium, 1979; Canada, 1985; denmark, 1981; Finland, 1981; France, 1979; Germany, 1984; Italy, 1986; Japan, 1979; Netherlands, 1983; New Zealand, 1982; Norway, 1979; Spain, 1981; Sweden, 1981; Switzerland, 1982; United Kingdom, 1979; and United States, 1985.
A simple example of a poverty measure using attainment-aggregation is the usual headcount index of poverty:<sup>10</sup> where _Fy_ is the distribution function for aggregate consumption _y_ and _z_ is the poverty line in that space. To keep things simple for expository purposes (including graphing), suppose that there are two attainments in amounts _x_ 1 and _x_ 2, with prices _p_ 1 and _p_ 2, so _y=p_ 1 _x_ 1+ _p_ 2 _x_ 2.

The second approach measures poverty in each of the dimensions separately and then aggregates the dimension-specific “deprivations” into a composite index. Formal treatments of this approach can be found in Tsui (2002), Bourguignon and Chakravarty (2003), Duclos et al. (2006) and Alkire and Foster (2007). The Alkire-Santos MIP is an example. I shall call this “deprivation aggregation.” To see more clearly how this second approach works, consider again the two continuous attainments, _x_ 1 and _x_ 2, with distribution functions _F_ 1 and _F_ 2 respectively. Poverty lines, denoted _z_ 1 and _z_ 2, are defined in each space and the weights on deprivations are _w_ 1 and _w_ 2 ( _w_ 1+ _w_ 2=1). Then a simple example of a poverty measure using deprivation-aggregation is the weighted incidence of poverty across the two dimensions:<sup>11</sup> This is only one possible way of aggregating deprivations. Alternatively one can focus on the joint distribution, and ask what proportion of the population is poor in at least one of the two dimensions (Bourguignon and Chakravarty, 2003). Letting _F_ 12 denote the joint distribution function, the poverty measure is then . Alternatively, one might ask what proportion is poor in both dimensions, i.e., . The measure proposed by Alkire and Foster (2007) goes further in introducing an extra parameter, such that a household is

> 10 The headcount index has a number of well-known limitations, and there is a large literature on alternative measures; Atkinson (1987) lists a number of these. But the headcount index is all we need for the present expository purpose.

> 11 Higher-order measures can also be defined, such as based on the Watts (1968) index (see Chakravarty et al., 2008) and the Foster, Greer and Thorbecke (1984) measure (Alkire and Foster, 2008). Tsui (2002) provides a general formulation of subgroup-consistent poverty measures based on deprivation aggregation. However, I will confine attention to the simple form in (2).

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<!-- Start of picture text --> Pre-harvest assessment triggered<br>Pre-harvest assessment triggered by Disaster Risk Management Technical Working Group (DRM TWG) and<br>conducted by woreda-level staff<br>Pre-harvest assessment<br>Initial assessment of number of people in need of food assistance based on a range of information including crop<br>and rainfall data<br>Main seasonal needs assessment<br>Multi-agency teams dispatched by the federal government to the regions conduct the main seasonal assessment<br>Drafting ofHRD document<br>The federal-level HRD Editorial Committee drafts the HRD document based on beneficiary numbers<br>Review of draft HRD document by DRMFSS<br>DRMFSS confirms the summary provided by the HRD Editorial Committee with regional governments<br>Submission of beneficiary numbers to State Minister<br>DRMFSS submits beneficiary numbers to the State Minister for review<br>Final review by national DRM Council<br>Beneficiary numbers are reviewed by the National DRM Council<br>Finalization ofHRD<br>Beneficiary numbers are submitted to the HRD Editorial Committee and published in the Humanitarian<br>Requirements Document<br><!-- End of picture text -->
# **Introducing a Performance-Based School Grant in Jakarta: What Do We Know about Its Impact after Two Years?**

Samer Al-Samarrai<sup>*</sup> , Unika Shrestha<sup>*</sup> , Amer Hasan<sup>*</sup> , Nozomi Nakajima<sup></sup> , Santoso Santoso<sup>*</sup> and Wisnu Harto Adi Wijoyo<sup>*</sup>

**JEL classification codes:** C21 **,** D73, I22, I28

**Keywords:** Education, quality of education, results-based management in the public sector, education funding formulae

* Education Global Practice, World Bank Group, 1818 H Street, NW, Washington, DC 20433

>  Harvard Graduate School of Education, Harvard University, <mark>13 Appian Way, Cambridge, MA 02138 Acknowledgements: The data on the UN examination scores was provided by PUSPENDIK (Pusat Penilaian Pendidikan) of the Ministry of Education and Culture (MoEC). The authors are grateful to Professor Ir Nizam and Dr A. Suprananto of PUSPENDI for providing and aiding the team’s understanding of this data. The data on education indicators for metropolitan areas was provided by the PDSPK (Pusat Data Statistik Pendidikan dan Kebudayaan) team in MoEC. The authors acknowledge the enormous amount of help provided by Dr. Ir. Bastari, Ir. Siti Sofiah, and Watik Sudarwati. Information on school grants and additional information on Jakarta schools was provided by UPT PDSIP Disdik of the Jakarta government. The authors are grateful to Kadarwati Mardiutama in facilitating this process and to Harry Patrinos and Shwetlena Sabarwal for providing comments on an earlier draft of the paper. The authors gratefully acknowledge financial support from the REACH Trust Fund at the World Bank. Corresponding author - Samer Al-Samarrai (salsamarrai@worldbank.org). 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.</mark>
# **7 Early Impacts of Generative AI on Website Traffic**

In this section, we explore the early impacts of generative AI on individuals’ online activities and the implications on the digital ecosystem. Generative AI tools can disrupt the current traffic patterns and advertising-based business models through multiple channels: First, chatbots can provide direct answers, potentially reducing the need for users to click through to multiple websites and aggregate information by themselves. This may divert traffic from traditional search engines and information websites. Second, AI-generated content may compete with human content creators, reducing traffic to blogs, news sites, and other content platforms. However, high-quality, curated content will become more valuable, benefiting some content creators that offer authoritative, novel, and unique content. Third, AI assistants could act as intermediaries, potentially reducing direct traffic to e-commerce websites. If traffic is diverted, it could significantly affect the current attention-advertising based business model. Fewer page views and clicks could shrink ad revenue for many websites. However, traditional websites and platforms will integrate generative AI features to remain competitive. New monetization models will also emerge to leverage AI capabilities. Eventually, a new balance will be struck between AI-mediated and direct web interactions.

Analysis of monthly traffic patterns across various websites reveals distinct impacts potentially correlated with the emergence and updates of ChatGPT. Figure17 compares the normalized traffic trends of three popular information-acquiring websites: Google, Wikipedia, and Quora. Despite ongoing speculation about whether Bing or ChatGPT could supplant Google as the go-to portal for internet users seeking information, we observe no clear trend in Google’s traffic before and after the launch of GPT 4. As of March 2024, Google’s traffic remains 80 times higher than ChatGPT’s (Figure 3). This substantial gap suggests that the fluctuations in Google’s traffic are more likely attributable to seasonality rather than ChatGPT’s influence. Similarly, Quora’s traffic exhibits high volatility without clear correlations to ChatGPT’s trajectory. Wikipedia’s traffic, however, was quite stable before the launch of GPT 4, but displays a downward trend since the launch of GPT 4. This shift in Wikipedia’s traffic suggests a potential migration of users towards ChatGPT for information inquiries and learning purposes.

These observations indicate that while generative AI tools like ChatGPT are making inroads in the information-seeking landscape, their impact on established platforms varies. Google’s resilience 38
analyzing the proportion of firms/households that demand credit but are unable to obtain it.<sup>**8**</sup> We are able to identify firms/households that are constrained in the access to loan markets by looking at agents who have either applied for a loan and were denied or who needed the loan but have not applied (potential demand). Using the answers from various questions, we classify all surveyed respondents into 4 main _participation categories_ guided by the decision tree showed in Figure 1. The categories are as follows: (1) not demanded, (2) demand but did not apply for a loan, (3) applied for a loan but was rejected, and (4) received a loan.

The _first category_ requires identifying respondents that do not demand loans (not demanded). Obviously, those agents who applied for loans in the past three years have a demand for loans. Nonetheless, some agents with a demand for loans may be reluctant to apply because of their perceptions of a complicated loan process with associated high transactions costs, lengthy procedures, or lack of knowledge about how to apply. These agents may find that the cost of applying exceeds the expected benefits of additional funds. Others are discouraged from applying because of a perception that their loan applications would have a high likelihood of rejection due to a lack of adequate collateral or co-signer, or pure discrimination. Another possibility is that agents are discouraged from applying because the loan products offered are ill-suited for their operations or interest rates and fees are too high.

**Figure 1: Participation in loan markets**

<!-- Start of picture text --> Full amount?<br>Demand? Apply? Receive?<br>Yes Yes Yes<br>No Yes<br>No No<br>No<br>1. Not demanded 4a. Received<br>(unconstrained) (unconstrained)<br>3. Rejected<br>(constrained)<br>2. Demand exists<br>but did not apply<br>(constrained) 4b. Received<br>(partially<br>constrained)<br><!-- End of picture text --> To separate those individual entrepreneurs or enterprises that did not apply for loans into the categories of “demand” and “no demand,” we use the self-reported reasons as why they did not apply for a loan. Specifically, we use the following responses as indicators of “no demand”: (a) prefers to use his own resources; (b) has access to other sources of financing (implying no demand for this source) and (c) has

> 8 When using indirect indicators to assess the incidence of credit constraints, the richness of the survey data determines how to identify credit-constrained agents. For example, Paulson and Townsend (2001) used as an indirect indicator of credit constraint— those households that could increase the size of their business in a profitable way. Guiso (1998) defines credit constrained as those agents that wish a larger amount of credit at the current interest rate or had their loan application rejected. Levenson and Willard (2000) define as credit constrained those that did not obtain all financing needed.

7
# How Fit are Feed-in Policies? Evidence from the

# European Wind Market

Fan Zhang<sup>_∗_</sup>

**Key Words** : Feed-in Tariffs, Wind Energy, Europe

**JEL Classification** : L52, L98, Q28, Q48

**Sector Board** : Energy and Mining

> _∗_ World Bank Energy Department of Europe and Central Asia Region. Email: fzhang1@worldbank.org. I thank Uwe Deichmann, Carolyn Fischer, Joshua Linn, Govinda Timilsina and participants at the World Bank SDN BBL seminar for helpful comments and suggestions. I thank Louis Preonas and Xu Chen for excellent research assistance. Financial support from the World Bank Research Support Budget is gratefully acknowledged. All errors remain those of the author.

1
- ICT infrastructure and policies and systems that support efficient and deep production networks and supply chains;

- Interconnected, environment-friendly transport networks with road, rail, sea, and air links that promote trade and investment within the region and with global markets, and widen access to markets and public services;

- Green energy projects that allow countries to benefit from natural endowments, providing efficient and secure supplies of electricity, coal, gas, oil, and alternative energies;

**Figure 2:** _Internet and Broadband Services_

<!-- Start of picture text --> 70 Use of internet per 100<br>60<br>50 Broadband users per 100<br>40<br>30<br>20<br>10<br>0<br>Source: World Bank, WDI<br>EU12 Region EAP12 region Azerbaijan Russian Federation Kazakhstan Belarus Moldova Georgia Ukraine Uzbekistan Kyrgyz Republic Armenia Tajikistan Turkmenistan<br><!-- End of picture text -->

- Sustainable management of water resources that is aligned with urban growth, change in land use, and agricultural and industrial use.

Some Eurasian countries have been taking off in terms of mobile phone and access to internet services (Figure 2). Mobile phones are very common, but are only used for their voice feature, and are under-utilized for data potential. In the same vein, access to internet services has grown exponentially over the last five years, but its use has been well below its potential. Comparing globally, we find that Eurasia lies ahead of lower middle-income countries, but behind the EU12 and EAP12, with businesses having both a low use of internet and a low capacity to innovate, research and pioneer new products and processes (Figure 3). Part of the problem lies in the limited connectivity of those countries to broadband services. Kazakhstan has about 7 broadband users per 100 people, compared to 30 in the United States. This lag in high-speed connectivity seriously hampers firms’ ability to use the internet effectively. This observation is consistent with firms increasingly reporting telecoms has a problem in 2009 relative to 2005, despite significant advances in the physical availability of new technologies in the region (EBRD, 2012).

9