rafmacalaba/datause-encoder-data huggingface dataset:
# **Chapter 3: Exploring Ways to Improve Efficiency and Cost Effectiveness**

34. This chapter will focus on possible measures to address the issues discussed above:

- Concentration

- Subsidy affordability

- Taxation

# **_Addressing Concentration Issues_**

35. In theory, there are three potential approaches to solve the concentration issue. The first is to absorb the power where it is produced. Since local absorption capacity is limited in Northwest and North China<sup>21</sup> (see Figure 1), this option is disregarded. The second option is to deliver the electricity to load centers in Southeast China through high-voltage, direct current (DC) or alternating current (AC) transmission lines. The third option is to build more wind capacity near the load centers in the Southeast region. The two latter issues are discussed further below.

36. To develop wind and deliver power in an effective way, it is necessary to take into account the cost of generation and the cost of transmission. If the transmission line is designed to deliver the full capacity, it could be underutilized because of intermittence issues related to wind generation. For a given capacity of wind, the transmission line needs to be designed to transmit the capacity that minimizes the cost of supply (generation plus delivery). This usually leads to designing the line to transmit only a certain percentage of the installed wind capacity. This percentage is labeled below as the “capacity ratio.” The maximum share of the energy generated and delivered by the line is designated below as the “wind utilization rate.” 37. The China Electric Power Research Institute (CEPRI) carried out a study on the relationship between the wind resource utilization rate and the capacity ratio. The research results show that at the upper end of the wind speed distribution, the utilization rate decreases significantly more slowly than the capacity ratio (see Table 5 and Figure 6).

**Table 5: Relationship between the Capacity Ratio and Generation Losses**

|||||(percent)|||||
|---|---|---|---|---|---|---|---|---|
|Capacity<br>ratio|100|90|80|70|65|60|55|50|
|Resource<br>utilization<br>rate|100|99.86|99.57|86.81|95|82.68|89.80|86.31|

21 There are three routes (South, Middle, and North) of the program Sending Electricity from West to the East. The North route is to send electricity from Gansu and Ningxia, plus North Shanxi and West Inner Mongolia to East China. In 2010, the peak generation capacity in North West Grid was 50600 MW, while the peak load was 44210 MW, the surplus was 6390 MW; Also in 2010, the surplus in North China was 17988 MW; On the other hand it was short of supply in East China, the gap is 24150 MW.

16
poverty rates, population densities to schooling in Vietnam 25 years after the US bombing campaign and find that the local economy had recovered.

Of course, the time dimension involved in these findings is in itself not a reason for general optimism in a post-conflict situation. The evidence on quicker gains is more mixed - mostly because there is no agreed methodology for how to study recoveries. Cerra and Saxena (2008) use a method used to study crisis episodes more generally to study civil wars. In this method the start of an episode is coded as 1 and the impact over time is studied using forwards and lags. They find that, while output falls steeply immediately after conflict (6 percent on average), the economy recovers relatively soon afterwards. Mueller (2012) shows that this, fairly optimistic, view is due to a coding error. Economies contract by more than 18 percent in a civil war and this is not recovered within a decade. However, the problem here is partly one of the correct coding of recoveries which we will turn to next.

# **3.1 Empirical Evidence for Economic Recoveries**

A simple way to check what happens in the recovery period at the macro level is to construct a set of dummies that capture the years after violence ends directly. These can then be used in a regression like the following where _gct_ is the growth rate of country _c_ in year _t_ and _recoveryct_<sup>1is a dummy that takes</sup> the value of 1 in the first year after violence ends. Table 3 shows the result using 5 recovery dummies. The result is a striking bounce of the growth rate by about 2.5 to 3 percentage points right after violence ends but otherwise little evidence for prolonged periods of extraordinary growth. This implies that, in the majority of cases, the output lost is not completely recovered in the recovery period. In addition, the similar results for light per capita growth suggest that the lack of an extraordinary growth period at the country level is not due to measurement issues. Regressions with levels of GDP per capita on the left hand side confirm this lack of a recovery.

This is, however, not a contradiction to the findings of strong recoveries in levels of output within country in the long run. As a check we use the data on night light at the ethnic group level as it offers a unique opportunity to study the dynamics of local economic activity on a yearly basis.

We run the following regression for group _i_ in year _t_ in country _c_ : where _ycit_ is the log of per capita night light, _incidencecit_ is conflict incidence and _recoverycit_ are a set of recovery dummies that capture the period after conflict, all at ethnic group level. The key addition in this regression is a set of of country/year fixed effects _θct_ which capture average economic activity at the country level in every year. In this way we compare how the level of economic activity in group _i_ compares to other groups in the same country _c_ during and after conflict. This mimics the existing 18
suffer to be considered to be deprived. Which criteria should we use for identifying multidimensionally deprived individuals?

> Unlike the usual case of Alkire and Foster (2007),<sup>_c_</sup> _i_<sup>is continuous here due to the</sup> continuous dimensions (MCA indices). Then, the _union_ approach, which defines an individual as deprived when his or her deprivation occurs in at least one dimension, is not the only case where _d_ = 1. In fact, it can also include some cases where _d_ is equal to any minimum deprivation suffered by individuals in the continuous dimensions. On the other side, the _intersection_ approach considers an individual to be deprived when his or her deprivation covers all dimensions. _d_ could take a value _D_ or lower than _D_ again because of the continuous dimensions. The differences between these approaches are not clear-cut, especially as the third approach, that is the _intermediate_ one (Duclos et al., 2006), could be defined over the range 0 and _D_ .

> Let _P_ β ( _x_ ) be the class of multidimensional deprivation indices developed by Alkire and Foster (2007, 2011). We also consider the case of household surveys with sampling

> designs. Let<sup>_s_</sup> _i_<sup>be the sampling weight assigned to individual</sup><sup>_i_and normalized such that</sup> _N_ ∑ _si_ = _N_ . _P_ β ( _x_ ) is given by: _i_ = 1

When β = 0 , we obtain the proportion of poor individuals, which is simply their total number divided by the total population.<sup>_P_</sup> 0 (<sup>_x_</sup> )<sup>, also called the headcount ratio, is a</sup> member of Foster-Greer-Thorbecke (FGT) class of poverty measures. This measure is not sensitive to the number of dimensions in which individuals are deprived and thus violates the principle of _dimensional monotonicity_ . By contrast,<sup>_P_</sup> 1 (<sup>_x_</sup> )<sup>, called the</sup> adjusted headcount ratio, is more satisfactory since it respects such a principle. This is 16
# **Trade Finance in Crisis: Market Adjustment or Market Failure?**

Jean-Pierre Chauffour and Thomas Farole<sup>1</sup> International Trade Department World Bank, Washington, D.C.

> 1 Jean-Pierre Chauffour (jchauffour@worldbank.org) and Thomas Farole (tfarole@worldbank.org) are respectively Lead Economist and Senior Economist in the World Bank International Trade Department. The paper benefited from the work on trade finance in emerging markets that IFC, Ltd. conducted for the World Bank (IFC, 2009). The authors would like to thank Olivier Cattaneo, Mona Haddad, Bernard Hoekman, Richard Newfarmer and other participants in the Joint World Bank-CEPR Conference on the Trade Implications of Policy Responses to the Crisis, Brussels, May 26-27, 2009, for their helpful comments and suggestions. The views expressed in this paper are those of the authors and do not necessarily represent those of the World Bank.
returns to experience is generally not available. Moreover panel data could not solve another fundamental problem. Opening up to trade does not only change commodity prices, it also a¤ects relative factor prices and hence the price of human capital.<sup>2</sup> As a consequence, observing higher returns to experience in more open economies does not allow to conclude that trade increases on-the-job human capital accumulation.

These problems are likely to explain why there exist very few empirical studies about cross-country di¤erences in on-the-job human capital accumulation. The empirical strategy pursued in this paper overcomes these issues as it does not rely on cross-country data. Instead, it uses data on US immigrants from di¤erent source countries to estimate US returns to home country experience. These returns are measured within the same labor market and are therefore not a¤ected by cross-country di¤erences in the price of on-the-job human capital. Furthermore, the US labor market is characterized by a relatively low level of labor market regulation which assures that wages and hence estimated returns to experience are related to productivity.

I provide evidence of a positive and signi...cant e¤ect of home country trade on returns to home country experience of US immigrants, indicating that trade enhances the accumulation of on-the-job human capital. High investment rates per worker, which can be considered a precondition for technology adoption, and strong governments and institutions have a positive and signi...cant e¤ect on returns to home country experience. The positive e¤ect of trade on returns to home country experience persists when restricting the sample to developing countries, providing empirical evidence for the hypothesis that trade increases learning-by-doing through technology transfer even in less developed countries. It is robust to the issues of self-selection, heterogeneity in returns to experience and English speaking origin and is unlikely to be the result of unobserved cultural background.

The remaining part of this paper is organized as follows. The next section provides a short summary of the related literature. Section 3 summarizes the empirical strategy. Section 4 discusses data and provides a preliminary data analysis. The main results are presented and discussed in section 5. Section 6 performs a series of robustness checks. The ...nal section concludes.

> 2 According to the Stolper-Samuelson theorem the relative reward of a factor that is more intensively used in the production of a good increases as the price of the good rises.

2
Policy Research Working Paper 7344

# **Abstract**

The ongoing Ebola outbreak in West Africa has put a huge strain on already weak health systems. Ebola deaths have been disproportionately concentrated among health care workers, exacerbating existing skill shortages in Guinea, Liberia, and Sierra Leone in a way that will negatively affect the health of the populations even after Ebola has been eliminated. This paper combines data on cumulative health care worker deaths from Ebola, the stock of health care workers and mortality rates pre-Ebola, and coefficients that summarize the relationship between health care workers in a given country and rates of maternal, infant, and underfive mortality. The paper estimates how the loss of health care workers to Ebola will likely affect non-Ebola mortality even after the disease is eliminated. It then estimates the size of the resource gap that needs to be filled to avoid these deaths, and to reach the minimum thresholds of health coverage described in the Millennium Development Goals. Maternal mortality could increase by 38 percent in Guinea, 74 percent in Sierra Leone, and 111 percent in Liberia due to the reduction in health personnel caused by the epidemic. This translates to an additional 4,022 women dying per year across the three most affected countries. To avoid these deaths, 240 doctors, nurses, and midwives would need to be immediately hired across the three countries. This is a small fraction of the 43,565 doctors, nurses, and midwives that would need to be hired to achieve the adequate health coverage implied by the Millennium Development Goals. Substantial investment in health systems is urgently required not only to improve future epidemic preparedness, but also to limit the secondary health effects of the current epidemic owing to the depletion of the health workforce.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 devans2@worldbank.org, mgoldstein@worldbank.org, and apopova@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
Policy Research Working Paper 8161

# **Abstract**

A meta-analysis of 126 impact evaluation studies finds that financial education significantly impacts financial behavior and, to an even larger extent, financial literacy. These results also hold for the subsample of randomized experiments (RCTs). However, intervention impacts are highly heterogeneous: financial education is less effective for low-income clients as well as in low- and lower-middle income economies. Specific behaviors, such as the handling of debt, are more difficult to influence and mandatory financial education tentatively appears to be less effective. Thus, intervention success depends crucially on increasing education intensity and offering financial education at a “teachable moment.” This paper is a product of the Strategy and Operations Team, Development Economics Vice Presidency. 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 lmenkhoff@diw.de.

_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
countries, and often lack specific emphasis on generative AI technologies.

Tracking the usage of generative AI tools is a crucial first step in understanding their economic and social implications. Despite progress in connecting more people to the internet, the multi-faceted digital divide is widening both within and between economies, amplifying disparities in productivity and consumer welfare (Sorbe, Gal, Nicoletti, and Timiliotis 2019). Applying innovative data to summarize stylized facts is fundamental. This includes understanding the types of users and economies that are leading in generative AI adoption, those that are lagging, how people are using generative AI tools, and the underlying factors driving these trends. Such insights are essential for policymakers, businesses, and researchers to make informed decisions and foster equitable development in the AI landscape and the broader economy.

However, there is a glaring data gap in monitoring the diffusion and adoption of generative AI, especially in developing countries. Tracking AI adoption is challenging due to several factors. First, conducting comprehensive and representative surveys on AI usage is both time-consuming and expensive, often requiring extensive timelines to gather and analyze data. Many surveys are also one-off and not comparable over time or across countries. For instance, a survey conducted by Humlum and Vestergaard (2024) in Denmark reveals widespread usage, with half of the surveyed workers reporting they use ChatGPT. This adoption has led to significant time savings in daily tasks, particularly for younger and less experienced workers. Despite these insights, similar detailed data from developing countries are lacking, underscoring a notable gap in our global understanding of generative AI’s impact. Additionally, the definition of AI varies widely, encompassing a broad spectrum of technologies from computer vision to robotics and natural language processing, which complicates consistent reporting. Moreover, AI is frequently embedded within products and services, leading to significant under-reporting as respondents may not explicitly recognize or disclose their use of AI. The data gap is even more pronounced in developing countries, which lack the capacity and resources to conduct in-depth surveys. These challenges hinder the collection of accurate, comparable, and up-to-date data on AI adoption, further obscuring the global landscape of AI adoption.

This paper aims to fill this data gap by utilizing novel website traffic data from Semrush. Semrush collects raw data through clickstream analytics, tracking codes, and server log files. It processes approximately 25 billion URLs daily across a global database that contains over 43 trillion 3
important for LAC airports (where they represent almost 60% of total revenues) than for airports in any other region.

**Table 5: Potential explanatory factors of technical inefficiency (2005-2006)**

|**Explanatory factors**|**Latin**<br>**America**|**Asia**|**Europe**|**Canada &**<br>**United States**|
|---|---|---|---|---|
|**Institutional framework**|||||
|Private airport (%)|54.5|25.6|37.9|0|
|Independent Regulatory Agency (%)|31.8|10.3|16.7|0|
|**Socioeconomic environment**|||||
|GDP per capita (U$D)|5,442|17,397|32,598|42,219|
|Tourism expenditures per capita (U$D)|69|532|943|393|
|**Population concentration**|||||
|Population in the area (1,000)|7,719|6,709|3,200|3,984|
|Population > 5,000,000 (%)|45.5|48.7|22.7|34.4|
|**Airport characteristics**|||||
|Hub airport (%)|9.1|17.9|40.9|27.2|
|Passengers connecting (% of passenger)|7.9|9.5|32.8|23.4|
|Aeronautical Revenues (% of total revenue)|56.9|53.8|51.6|49.2|

Source: Institutional and Airport characteristics variables were constructed with information from Air Transport Research Society’s Benchmarking reports. Socioeconomic and population variables were obtained from World Bank’s World Development Indicators database.

Table 6 reports the results – in the form of marginal effects – of estimations for alternative truncated regression models. The first two columns show the estimates of two models with TE-VRS scores as dependent variable, with and without dummies for each world region. The third column presents the estimates of a model with TE-CRS scores as dependent variable, without regional dummies. The Likelihood Ratio Tests (LT) indicate that in all three cases the variables included in the model, taken together, have a statistically significant effect on the dependent variable.

First, it should be noted that there are two variables that appear as the main drivers of technical efficiency in the airport sector. On the one hand hub airports are, on average and depending on the specification of the model, 10% to 15% more efficient than non-hub airports. On the other hand, the size of the population in the area served by the airport also seems to matter: airports located in cities with more than 5 million inhabitants are 17% to 20% more efficient than airports that serve less populated areas.

Second, our results show that the institutional variables (whether the airport is private or public and whether it is regulated by an independent regulatory agency), are associated with positive marginal effects. However, these variables are not statistically significant, with the exception of the dummy for private airports under the VRS assumption. According to these 14
# **Table 4: Descriptive statistics**

|Variable|Definition|Mean St|d. Dev.|
|---|---|---|---|
|_Productivity variables:_||||
|Value added|Log of annual value added per worker (US $)|7.93|1.03|
|Fixed assets|Log end-of-fiscal year book value of fixed assets (in US $)|7.44|1.16|
|Average wage rate|Log of annual wage bill per employee (US $)|6.68|0.73|
|Employment|Log end-of-fiscal year number of employees|3.24|1.29|
|_Business characteristics:_||||
|Business age|Log number of years since the establishment was set up|2.48|0.81|
|Start up size|Number of employee's at the startup of the establishment|2.49|1.09|
|Entrepreneurial roots located|Dummy=1 if the business is located in the birth state of the current owner|0.83|0.38|
|_Business environment variables_||||
|Ouput loss to power outage (%)|Log of city average of estimated % of annual output lost due to power outages|2.06|0.51|
|Constrained by labor regulation (%)|Log of proportion of businesses held back by labor regulation in the city|1.94|1.49|
|_Geographic variables:_||||
|Foreign market access|Log distance of city from the nearest major port|3.17|1.42|
|Domestic market access|Log of ratio of city's domestic market access index to the all India maximum|6.91|0.05|
|Own industry local size|Log of own industry employment in the city|4.44|0.09|
|_Sector:_||||
|Food|Dummy=1 if in the food and bevareges industry|0.11||
|Textiles|Dummy=1 if in textiles|0.13||
|Garments|Dummy=1 if in garmentts|0.14||
|Leather goods|Dummy=1 if in leather goods|0.04||
|Drugs and pharmaceutical|Dummy=1 if in drugs and pharmaceuticals|0.10||
|Chemicals|Dummy=1 if in chemicals|0.11||
|Consumer electronics|Dummy=1 if in consumer electonics|0.08||
|Electrical white goods|Dummy=1 if in electrical white goods|0.07||
|Auto parts|Dummy=1 if in the auto componensts industry|0.13||
|Fabricated metals|Dummy=1 if in fabricated metals|0.05||
|Machine tools|Dummy=1 if in machine toos|0.04||

43
**Figure 4: Participation, demand and rationing by consumption per person, rural India**

# (a) Participation rate

<!-- Start of picture text --> Participation rate in MGNREGS as a function of consumption<br>Rural India<br>0 20 40 60 80 100<br>Percentile ranked by consumption per person<br>(1=poorest; 100=richest)<br>LOWESS<br>PLREG WITH STATE CTRL<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text -->

# (b) Rationing rate

<!-- Start of picture text --> Rationing rate on MGNREGS as a function of consumption<br>Rural India<br>0 20 40 60 80 100<br>Percentile ranked by consumption per person<br>(1=poorest; 100=richest)<br>LOWESS<br>PLREG WITH STATE CTRL<br>.7<br>.6<br>.5<br>.4<br>.3<br><!-- End of picture text --> 22