rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>- 8 -</sup>

Although they have different ownership structures, life insurance companies and pension funds are subject to identical accounting, reporting and other regulatory rules and there is fierce competition between pension and insurance companies. Banking institutions play a small part in the second pillar.

Life insurance and multi-employer pension funds have the lion's share of the market for occupational pensions. Corporate pension funds have a small share of the market, while banks receive about 10 percent of total contributions.

Contributions to occupational pension plans increased steadily over the past ten years or so. Their annual growth rate was remarkably stable, ranging between 10 and 12 percent in nominal terms, while during the same period (1995-2004) inflation averaged 2 percent per year. The strong performance of contributions is reflected in their growing level relative to GDP, which nearly doubled from 2.36 percent in 1995 to 4.31 percent in 2004 (Table 3).

**<u>Table 3  Contributions to Occupational Pension Plans, 1995-2004</u>**

||**1995**|**2000**|**2004**|
|---|---|---|---|
|Contributions (DKKbillion)|23.80|41.52|62.92|
|Contributions/GDP(%)|2.36%|3.21%|4.29%|
|Contributions toAnnuities (% oftotal)|59.8%|57.2%|50.1%|
|Contributions toPhased Withdrawals (%)|9.9%|23.1%|35.1%|
|Contributions toLump Sums (%)|30.3%|19.7%|14.7%|
|Contributions toL&Pcompanies (%)|85.8%|89.6%|89.0%|
|Contributions toBanks (%)|14.2%|10.4%|11.0%|
|Contributions toUnit-Linked||5.2%|8.7%*|

* 2003

Source: Statistics Denmark, Central Customs and Tax Administration and the Danish Insurance Association.

The increase in contribution amounts is partly due to expanding coverage and partly to a gradually rising contribution rate. While contribution rates (like many other features of pension plans) vary considerably among different schemes, the upward trend in contribution rates is illustrated by the following figure, which represents the evolution of the average contribution rate for schemes covered by the labor agreement between the Danish Confederation of Trade Unions (LO) and the Danish Employers’ Confederation (DA). This agreement covers half the labor force. The contribution rate has crept upwards from 1 percent in 1993 to over 10 percent in 2006 (Figure 2).
predominantly used for productivity purposes and tasks related to work and education.

Third, generative AI tools have achieved unprecedented global diffusion both in terms of scale and speed, though low-income countries are falling further behind middle-income countries. ChatGPT is used in 209 of 218 economies worldwide. The United States accounted for over 70% of total traffic for ChatGPT upon its launch, but that share dropped to 25% within one month. Traffic originating from middle-income economies surpassed that of high-income economies within six months, and middle-income economies now contribute more than half of the traffic. In addition to the US, India, Brazil, the Philippines, and Indonesia round out the top five economies in terms of ChatGPT traffic. The over-representation of middle-income economies is remarkable when put into perspective: for example, India only accounts for 14% of US GDP, 35% of US electricity consumption, but over 50% of US ChatGPT traffic. Similar patterns are observed across many other countries, attesting to the widespread interest and uptake of generative AI. Still, low-income economies only represent less than 1% of ChatGPT traffic.

Forth, panel data regressions reveal that countries’ income level, share of youth population, the availability and quality of digital infrastructure, and human capital are strongly correlated with higher uptake of generative AI. Specialization in the digital sector and white-collar occupations, and English fluency predict higher chatbot usage but not other types of generative AI usage. Higher internet speed is more important for image and video generation tools than chatbots. Youth population, better digital infrastructure, higher literacy rate and English fluency also predict more intensive usage of ChatGPT, measured with average duration per session.

Finally, we found indicative evidence that generative AI tools are disrupting the current traffic patterns for certain websites and altering individuals’ online activities. Information websites like Wikipedia, translation and writing tools like Grammarly and Google Translate, IT professionals’ community Stack Overflow have experienced substantial drops in traffic immediately after the launch of GPT-4. Traffic to some of these sites have recovered as they integrated generative AI capabilities, underscoring the dynamic nature of the tech landscape and the importance of adaptability and continuous innovation for sustaining relevance. Cross country panel data regressions also suggest that ChatGPT is changing how people acquire information, learn new skills, and conduct language-processing tasks. Such tasks are increasingly conducted with the help of ChatGPT, while more complex cognitive and analytical tasks seem to have been augmented by ChatGPT.

45
# Table M.1: Data presentation in the literature

|Source*|Data presentation|Crosscountr<br>a<br>g|Data linked to|Ed<br>£<br>idiu<br>t<br>iac|ucation is<br>stment<br>iCovera|sues cove<br> <br>e QuahIE|red:<br>Egtiq|
|---|---|---|---|---|---|---|---|
|Colclough and Lewin|a) Time series data<br>b) Case studies|<br>Yes<br>No|No<br>Yes|X<br>X|X<br>X|(X)<br>X|(X)<br>X|
|Comnia, Jolly, Stewart|Case studies|No|Yes|X|X|X|X|
|Gallagher|Adjustment method|ology Yes|Yes|X|X|(X)||
|Kakwani, Makonnen,<br>van der Gaag|Adjustment method|ology Yes|Yes|X|X|(X)||
|Lockheed, Verspoor, etal.|Time series data|Yes|No|X|X|X|(X)|
|Sahn|Adjustment method|ology Yes|Yes|X|X|(X)||
|World Bank (1988c)|Time series data|Yes|No|X|X|(X)|(X)|

(X) indicates that issues are addressed, but to a lesser degree.

'Note: The list is not exhaustive, and covers only the principal sources cited in the text.
To test the robustness of the standard test based on (7), we allow for systematic covariates of the vignettes under the assumption of internal consistency described in Section 3.1, but now relaxing our assumption of homogeneous shifts in the scales. To do so we employ the method of Compound Hierarchical Ordered Probit, proposed by King et al., (2004), dubbed CHOPIT. In the standard model, one postulates the existence of a series of common cut-off points in the _SW_ space that generate the observed ordinal responses (as in any OP). Instead, CHOPIT postulates that these thresholds are functions of a vector of observed covariates. The extra information on the vignette responses provides the basis for identification, under the internal consistency assumption—specifically that the thresholds for a respondent’s self-assessed welfare are determined identically as for that respondent’s thresholds in the vignette responses. Following King et al. (2004), the thresholds are assumed to be given by:<sup>12</sup> The identifying assumption is that the same parameters, γ _k_ and δ _k_ , and (hence) the thresholds _k_<sup>τ</sup> _i_<sup>determine the ordinal responses on the vignettes. Such response consistency is a natural</sup> assumption to make. Without the vignettes, identification would only be possible under questionable assumptions about the nonlinearity of the functional forms involved. Thus we are able to model determinants of the thresholds separately to those of the latent continuous variable for subjective welfare.<sup>13</sup> We begin by testing for an economic gradient in subjective welfare. Table 5 gives the estimated economic gradient (the regression coefficients on log consumption per capita) for various specifications, comparing OP and CHOPIT, but without any controls in the subjective welfare regression. As can be seen from Table 5, the OP and CHOPIT estimates of the coefficients of the equation for subjective welfare turn out to be quite close. Correcting for scale heterogeneity attenuates the economic gradient (lower β ) for Tajikistan and Guatemala but

> 12 Notice that no error terms appear in the following equations. These are taken to be subsumed in the overall error term<sup>in equation (7).</sup>

> <sup>ε</sup> _i_

> 13 We implemented the CHOPIT analysis using the R statistical analysis program, using the programs 'anchors,' 'rgenoud,' and 'Zelig.'  Further information and documentation on these packages is available at http://sekhon.berkeley.edu/rgenoud, http://wand.stanford.edu/anchors and http://gking.harvard.edu/zelig. The R code is available on request.

15
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE managerial expertise that would allow for the realization of the large potential efficiency gains that have been identified. Promoting competition within the markets, and developing a coherent framework for promoting private participation with standard bidding documents and clearly defined competitive bidding procedures will also be essential to ensure that the benefits of increased private participation will result in more cost-efficient services for a larger share of the population.

**Figure 29. Africa attracts relatively less private sector investment in infrastructure compared with other regions**

<!-- Start of picture text --> a. 2008 $ billion b. Number of projects<br>300 900<br>800<br>250<br>700<br>200 600<br>500<br>150<br>400<br>100 300<br>200<br>50<br>100<br>0 0<br>Energy Telecom Transport Water and Sewerage Energy Telecom Transport Water and Sewerage<br><!-- End of picture text --> _Source:_ World Bank 2010a.

The benefits of further pursuing regional integration are most evident in the power and transport sectors. First, the cost of meeting Senegal’s power needs would be reduced if regional power trading developed further in the WAPP. These savings would reduce the annual infrastructure funding gap by as much as 12 percent a year. Second, continuing the emphasis placed on developing and maintaining regional transport corridors (road and railways) and integrating these corridors with gateways to worldwide markets (ports and airports), a multimodal vision of the transport sector could provide a major boost to Senegal’s international competitiveness.

If Senegal were completely unable to raise additional finance or reduce infrastructure costs, the only way to meet the targets identified here would be to take a longer period of time than the decade contemplated at the outset of this exercise. If the country were to make efficiency gains overnight while holding spending at current levels, it could in fact meet the identified infrastructure targets within a 15year period. Without tackling inefficiencies, those targets could take more than 80 years to achieve. These simulations underscore the importance of making progress on the efficiency agenda, which can put the country more than 60 years ahead in meeting its infrastructure targets.

Within the overall funding envelope, it will be very important to carefully prioritize infrastructure investments. Given the magnitude of the country’s funding gap, it will not be feasible to resolve all pending infrastructure issues at once—thus the need to identify priorities. The foregoing analysis of 44
|implementation|||
|---|---|---|
|s.e.|(0.04)|(0.02)|
|Log investment (USD,PPP)|-0.0221|-0.0236**|
|s.e.|(0.02)|(0.01)|
|Log number citations|0.00882|0.00532|
|s.e.|(0.02)|(0.01)|
|Region Categorical Variable|0.00171|0.00155|
|s.e.|(0.01)|(0.00)|
|Log Rate Quality paper|0.119|0.0786|
|s.e.|(0.17)|(0.09)|
|Dummy peer reviewed pub.|0.0138|-0.105***|
|s.e.|(0.07)|(0.03)|
|Dummy Paper focus|||
|innovation|0.0593|0.0343**|
|s.e.|(0.05)|(0.02)|
|Dummy outcome indicators|||
|incl.|-0.0146|-0.0193|
|s.e.|(0.05)|(0.03)|
|Dummy Robustness Checks|-0.026|0.00312|
|s.e.|(0.05)|(0.03)|
|Constant|-0.0743|-0.215|
||(0.30)|(0.16)|
|Observations|28|28|
|Pseudo R2|0.661|0.656|

(1) Simple truncated model low correlation (2) Simple truncated model with precision weights Robust standard errors in parentheses

- *** p<0.01, ** p<0.05, * p<0.1 Source: Own estimations

Harbord ‘s test (Table 6) confirms the presence of small study effects bias but these are not higher than the actual effects adjusted by reciprocal sample sizes.<sup>17</sup> The Harbord test p-value rejects the null hypothesis, indicating the presence of small study effects and bias. This requires further correction by estimating the WLS with meta-regression adjustments (Table 7). The variable that measures the additionality effects is positive (0.195) and significant at the 99

> 17 Egger et al. (1997) proposed a test for asymmetry of the funnel plot. This is a test for the Y intercept = 0 from a linear regression of normalized effect estimate (where estimate is divided by its standard error) against precision (reciprocal of the standard error of the estimate). Harbord et al. (2006) developed a test that maintains the power of the Egger test whilst reducing the false positive rate, which is a problem with the Egger test when there are large treatment effects, few events per trial, or when all trials are of similar sizes. The original Egger test should be used instead of the Harbord method if there is a large imbalance between the sizes of treatment and control groups. However, the Harbord test has an advantage because it regresses Z/√V against √V, where Z is the efficient score and V is Fisher’s information (the variance of Z under the null hypothesis).

26
WPS3165

# Social Protection in a Crisis: Argentina’s _Plan Jefes y Jefas_

## Emanuela Galasso and Martin Ravallion<sup>1</sup>

_Development Research Group, World Bank, 1818 H Street NW, Washington DC_

**Abstract:** We assess the impact of Argentina’s main social policy response to the severe economic crisis of 2002.  The program aimed to provide direct income support for families with dependents for whom the head had become unemployed due to the crisis. Counterfactual comparisons are based on a matched subset of applicants not yet receiving the program.  Panel data spanning the crisis are also used.  We find that the program reduced aggregate unemployment, though it attracted as many people into the workforce from inactivity as it did people who would have been otherwise unemployed.  While there was substantial leakage to formally ineligible families, and incomplete coverage of those eligible, the program did partially compensate many losers from the crisis and reduced extreme poverty.

**Keywords:** Crisis; safety net; workfare; unemployment; poverty; Argentina

**JEL classifications:** H53, I38; J65

### World Bank Policy Research Working Paper 3165, November 2003

_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 The work reported in this paper is part of the ex-post evaluation of the World Bank’s Social Protection IV Project in Argentina. The authors’ thanks go to staff of the government’s Institute of Statistics and Ministry of Labor, who helped greatly in assembling the data required for this paper, and to the World Bank’s manager for the project, Polly Jones, for her continuing support of the evaluation effort, and many useful discussions. Paula Giovagnoli provided excellent research assistance. Correspondence: <u>egalasso@worldbank.org</u> and mravallion@worldbank.org.

1
# What Explains Boys’ Educational Underachievement in the Kingdom of Saudi Arabia?*

## Mahmoud A. A. Elsayed<sup>a</sup> , Aidan Clerkin<sup>b</sup> , Vasiliki Pitsia<sup>b</sup> , Nayyaf Aljabri<sup>c</sup> , Khaleel AlHarbi<sup>c</sup>

a The World Bank, Washington DC, USA b Educational Research Centre, Dublin, Ireland c Education and Training Evaluation Commission, Riyadh, Saudi Arabia Keywords: Student Achievement, Gender Gap, Boys’ Underperformance JEL classification: I20; I21; I24; I28

- This research has been conducted as part of the Technical Cooperation Program between the World Bank and the Education and Training Evaluation Commission, funded by the Ministry of Finance of the Kingdom of Saudi Arabia under the Reimbursable Advisory Services (RAS) framework.
Intervals between Survey Years and Lining up Years in PovcalNet

|160|155|155|155|155|155|No Data<br>5 Years or More|
|---|---|---|---|---|---|---|
|140|29|29|29|29|29|@ Within 4 Years<br>Within 3 Years<br>® Within 2 Years|
|& 120||Z|13|21|25|Within 1 Year<br>HiSameYear|
|5 100|"||||||
|oO|||||||
|S<br>80|||||||
|oO<br>a<br>E<br>60<br>Zz|||||||
|40|||||||
|50|48|54|52|52|45||
|0|||||||
||2002|2005|2008|2010|2011||

# Intervals between Survey Years and Lining up Years in PovcalNet

<!-- Start of picture text --> 400%()<br>90%<br>23%<br>s 80%<br>s 70%<br>Qa<br>°<br>2 60%<br>s<br>2 50%<br>S<br>ez 40% 76%<br>® 67%<br>£ 30%<br>a 51% 51%<br>20% ae<br>10%<br>0%<br>2002 2005 2008 2010 2011<br><!-- End of picture text --> No Data 5 Years or More Within 3 Years @ Within 1 Year ® Within 2 Years @ Same Year
# Table 1: Sources of Weather Data

|dataset|Length of record|Resolution|Time step|Data|Units|
|---|---|---|---|---|---|
|**Precipitation**<br>||||||
|-Africa Rainfall Climatology version 2 (ARC2)|1983-current|0.1 deg|daily|total precip|mm|
|-Climate Hazards group InfraRed Precipitation with Station data<br>(CHIRPS)|1981-current|0.05 deg|daily|total precip|mm|
|-CPC Global Unifed Gauge-Based Analysis of Daily Precipitation|1979-current|0.5 deg|daily|total precip|mm|
|-European<br>Centre<br>for<br>Medium-Range<br>Weather<br>Forecasts<br>(ECMWF) ERA5|1979-current|0.28 deg|hourly|total precip|m<br>|
|-Modern-Era Retrospective analysis for Research and Applica-<br>tions, version 2 (MERRA-2) Surface Flux Diagnostics|1980-current|0.625x0.5 deg|hourly|rain rate|kg m<sup>2 </sup>s<sup>1</sup>|
|-Tropical Applications of Meteorology using SATellite data and<br>ground-based observations (TAMSAT)|1983-current|0.0375 deg|daily|total precip|mm|
|**Temperature**||||||
|-CPC Global Unifed Gauge-Based Analysis of Daily Temperature|1979-current|0.5 deg|daily|min, max temp|C|
|-European<br>Centre<br>for<br>Medium-Range<br>Weather<br>Forecasts|1979-current|0.28 deg|hourly|mean temp|K|
|(ECMWF) ERA5<br>31||||||
|-Modern-Era Retrospective analysis for Research and Applica-<br>tions, version 2 (MERRA-2) statD|1980-current|0.625x0.5 deg|daily|mean temp|K|

_Note_ : The table summarizes the Earth observation sources and related details for precipitation and temperature data.
# **1 Introduction**

It is well established in the economic literature that infrastructure investment is a major driver of economic development, particularly through its impact on structural transformation (Berg et al., 2017). Infrastructure investments can have sizeable multiplier effects on local economies by boosting productivity through better factor allocation (Vagliasindi and Gorgulu, 2021), facilitating trade through improved access to markets (Jedwab and Storeygard, 2021), or removing barriers to entry that prevent the development of local economic activities (Perez-Sebastian and Steinbuks, 2017). As various types of infrastructure may differently contribute to growth, it is important to disentangle their effects and how these effects may vary when provided in isolation or in combination. In particular, are different infrastructure types complements or substitutes in driving local economic effects? The answer to this question bears much policy relevance as policymakers need to optimize costly infrastructure investments and their distribution over localities. It is also important for policy makers to understand how these effects may be nuanced and vary according to location and sector.

In this paper, we study the joint impact of access to transportation (highways)<sup>1</sup> and electricity infrastructure on local gross value added (GVA) using panel data over four decades and more than 3,000 Brazilian municipalities. The study fills an important knowledge gap by focusing on infrastructure complementarities. It estimates the causal effect of infrastructure complementarities using the difference-in-differences model with potentially heterogeneous treatments (De Chaisemartin and d’Haultfoeuille, 2020), and an identification strategy that relies on the plausible exogeneity of the timing and common trends across municipalities in the absence of treatment. It then establishes a causal mechanism of infrastructure investment impacts, distinguishing between productivity and relative input use channels.

We find that neither the provision of highways nor electricity alone impacts GVA per capita (our main variable of interest) or, separately, GVA or population. At the same time, we find a strong complementarity associated with the joint provision of highways and electricity, which increases GVA per capita at the time the infrastructure investment is made. The effect, however, disappears after one

> 1We focus on highways because they are Brazil’s main transportation mode for both people and freight/cargo. Brazil has the fourth largest highway system in the world. In 2014, road cargo transport accounted for over 60% of Brazil’s transportation matrix (Sandoval, 2014).

2
|Top Manager Experience<br>|0.281***|0.280***|0.216**|0.282***|0.281***|0.217**|
|---|---|---|---|---|---|---|
|(logs)|(0.092)|(0.092)|(0.101)|(0.092)|(0.092)|(0.101)|
|Legal Form of the Firm|Yes|Yes|Yes|Yes|Yes|Yes|
|fixed effects|||||||
|Firm Registered When It<br>Started Operations Y:1 N:0|0.181|0.188|0.317**|0.178|0.184|0.315**|
||(0.140)|(0.139)|(0.142)|(0.140)|(0.139)|(0.143)|
|Firm Uses Technology|-0.278|-0.291|-0.197|-0.278|-0.291|-0.197|
|Licensed from Foreign|||||||
|Company Y:1 N:0|||||||
||(0.191)|(0.190)|(0.203)|(0.190)|(0.190)|(0.203)|
|Firm Competes Against<br>Informal Firms Y:1 N:0|0.036|0.040|0.094|0.038|0.042|0.095|
||(0.106)|(0.106)|(0.111)|(0.106)|(0.106)|(0.111)|
|Firm Has Internationally|-0.268|-0.282|-0.181|-0.266|-0.279|-0.180|
|Recognized Quality<br>|||||||
|Certificate Y:1 N:0|(0.215)|(0.214)|(0.218)|(0.215)|(0.214)|(0.217)|
|Time Tax (average)||-0.024***|-0.024***||-0.024***|-0.024***|
|||(0.008)|(0.009)||(0.008)|(0.008)|
|How Much of an Obstacle?:<br>Tax Rates (average)||0.054|-0.025||0.054|-0.021|
|||(0.126)|(0.128)||(0.126)|(0.128)|
|How Much of an Obstacle?:||-0.162|-0.247||-0.177|-0.255|
|Obtaining Licenses &|||||||
|Permits (average|||||||
|||(0.163)|(0.171)||(0.163)|(0.172)|
|How Much of an Obstacle?:||-0.079|-0.013||-0.069|0.001|
|Labor Laws (average)|||||||
|||(0.149)|(0.156)||(0.149)|(0.155)|
|Labor Productivity (logs)|||0.276***|||0.276***|
||||(0.085)|||(0.085)|
|Sales Growth Rate (annual,|||-0.004|||-0.004|
|%)|||||||
||||(0.004)|||(0.004)|
|Labor Productivity Growth|||0.006|||0.006|
|Rate (annual, %)|||(0.004)|||(0.004)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|17,329|17,329|15,995|17,329|17,329|15,995|

All coefficient values are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country times industry in brackets. *** (1%), ** (5%), * (10%).

40