rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix S5. Characteristics of the POVCAL sample**

**Table S5.1 List of poverty changes from the World Bank (2013) used in the statistical analysis**

|Country|Observation|Change in<br>$1.25/day<br>poverty|Change in $2/day<br>poverty|Change in real food prices<br>(%)|
|---|---|---|---|---|
|Armenia|1999-2001|1.81|1.91|-3.70%|
|Armenia|2001-2002|-4.87|-4.03|1.12%|
|Armenia|2002-2003|-4.34|-3.4|2.02%|
|Armenia|2003-2004|-3.2|-11.44|3.25%|
|Armenia|2004-2005|-3.45|-8.48|0.19%|
|Bangladesh|1986-1989|11.42|3.42|-1.74%|
|Bangladesh|1989-1992|3.53|2.34|-0.91%|
|Bangladesh|1992-1996|-9.31|-7.59|-0.20%|
|Bangladesh|1996-2000|-2.32|-1.17|2.80%|
|Bangladesh|2000-2005|-8.12|-4.2|0.87%|
|Bangladesh|2005-2010|-7.22|-3.9|6.01%|
|Belize|1994-1996|0.73|3.22|-0.21%|
|Belize|1996-1997|2.79|4.25|0.69%|
|Belize|1997-1998|-1.14|-1.94|-0.09%|
|Belize|1998-1999|0.9|-3.97|-0.49%|
|Bolivia|1993-1999|14.82|12.32|-2.76%|
|Bolivia|1999-2000|3.57|4.26|-2.35%|
|Bolivia|2000-2001|-5.46|-6.26|-0.98%|
|Bolivia|2001-2002|0.57|1.36|-1.76%|
|Bolivia|2002-2006|-5.8|-7.61|3.32%|
|Bolivia|2006-2007|-3.15|-1.01|4.55%|
|Bolivia|2007-2008|2.55|0.61|-5.65%|
|Brazil|1993-1996|-4.65|-7.24|13.74%|
|Brazil|1996-1997|-0.07|-0.05|-5.80%|
|Brazil|1997-1998|-1.29|-1.03|-0.38%|
|Brazil|1998-1999|0.35|0.76|-1.38%|
|Brazil|1999-2001|0.44|0.41|-1.91%|
|Brazil|2001-2002|-1.26|-2.24|1.12%|
|Brazil|2002-2003|0.65|1.07|4.92%|
|Brazil|2003-2004|-1.44|-1.93|-2.43%|
|Brazil|2004-2005|-1.25|-2.16|-3.54%|
|Brazil|2005-2006|-0.88|-2.02|-4.00%|
|Brazil|2006-2007|-0.51|-1.18|3.03%|

38
Hoel, M. O. (1996), Should a Carbon Tax be Differentiated Across Sectors?" _Journal of Public Economics_ , 59, 17-32.

Hood, C. (2010): Reviewing Existing and Proposed Emissions Trading Systems, OECD/IEA, 2010. https://www.iea.org/publications/freepublications/publication/ets_paper2010.pdf Klemick, H. (2012): What is the Optimal Offsets Discount under a Second-Best Can and Trade Policy? National Center for Environmental Economics Working Paper # 12-04. Washington, D. C.: U.S. Environmental Protection Agency.

Lecourt, S. (2012): The EU ETS Phase 3 Preliminary Amounts of Free Allowances. Information and Debate Series no 16, Climate Economics Chair, Paris-Dauphine University, June 2012.

Martin, R., M. M. Muûls, L.B. de Preux and U.J. Wagner (2012): Industry Compensation Under The Risk of Relocation: A Firm-level Analysis Of The EU Emissions Trading Scheme (April 3, 2012). Available at SSRN: http://ssrn.com/abstract=2033683.

Rosendahl, K. E. (2008), Incentives and prices in an emissions trading scheme with updating, _Journal of Environmental Economics and Management,_ 56, 69-82.

Rosendahl, K.E. and Storrøsten, H. (2011): Emissions trading with updated allocation: Effects on entry/exit and distribution, _Environmental and Resource Economics_ 49, 243-261.

Rosendahl, K. E. and Strand, J. (2011), Leakage from the Clean Development Mechanism. _The Energy Journal_ , 32 no 4, 2011, 27-50.

Strand, J. (2012), Strategic Climate Policy with Offsets and Incomplete Abatement: Carbon Taxes Versus Cap-and-Trade. _Journal of Environmental Economics and Management_ , forthcoming.

Strand, J. and Rosendahl, K. E. (2012), Global Emissions Effects of CDM Projects with Relative Baselines. _Resource and Energy Economics_ , 34, 533-548.

27
industries have expanded over time, not all have grown at a common rate. Noticeable, rapid expansion has occurred in a few industries, including transportation, electronics, and chemicals.

Export-oriented industrialization is a well-known policy that helps explain the successful economic growth of the Korean economy. One important but often overlooked element of this success story is the fact that the composition of core-export products has changed over time. For example, textiles, which are used to produce fabric, clothes, apparel, and shoes, were the main export in the 1970s. Over time, the main exports have shifted toward electronics, ships, and cars. Consequently, the value-added shares of traditional light industries, such as textiles, food, and beverages, have declined substantially, matched by an increase in the shares of electronics and transportation.

As an initial step to analyze the plant-level dynamics underling structural change, we document the patterns of entry, exit, job creation and destruction, and the growth of young plants during the industrial shift. Specifically, we focus on the difference between growing industries, which increased their value-added shares (e.g., electronic and electrical equipment and transportation equipment), and declining industries, which decreased their value-added shares over time (e.g., textiles and leather products). In addition to these three industries, we also examine chemical products, which have steadily grown over time and consistently accounted for about 10% of the value-added shares in manufacturing.

The structural transformation in Korean manufacturing involved the active entry, exit, and growth of surviving plants. We quantify the extent to which such plant-level dynamics explain productivity growth. The differences in aggregate TFP are well known to account for most of the income differences across countries. Recent empirical studies spanning a number of countries have 3
Figure B.1: **Density of the Assignment Variable by Survey Status – Sample Frame and Surveyed Households** <!-- Start of picture text --> −.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5<br>Hectares from acre or hectare cutoff<br>Sample frame Surveyed households<br>.2<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> Notes: The figure plots the density of the forcing variable (bank-recorded landholdings) for (a) all households in the sample frame and (b) the sample of surveyed households, in bins of 0.1 hectares within<sup><u>+</u></sup> _−_<sup>.5 hectares around the eligibility cutoff.</sup> Figure B.2: **Density of the Assignment Variable by Bank Type – Commercial Banks and Credit Cooperatives** <!-- Start of picture text --> −.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5<br>Hectares from acre or hectare cutoff<br>Bank accounts Cooperative accounts<br>.2<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> Notes: The figure plots the density of the forcing variable (bank-recorded landholdings) for all surveyed households and separately for (a) accounts at commercial banks (b) accounts at credit cooperatives, in bins of 0.1 hectares and a band of<sup><u>+</u></sup> _−_ .5 hectares around the program eligibility threshold.

51
# **Table 1: Variable Legend and Data Sources**

|<br>**Variable**|<br>**Description / Source**|<br>**Range / direction**|**Country**<br>**Cove**|**Cities**<br>**rage**|
|---|---|---|---|---|
|**City Variables**|||||
|Access to Water|UN Observatory, 1998|%|61|83|
|Access to Sewerage|UN Observatory, 1998|%|57|78|
|Access to Electricity|UN Observatory, 1998|%|61|81|
|Access to Telephone Lines 1|UN Observatory, 1998|%|51|58|
|Access to Telephone Lines 2|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Quality of Infrastructure|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Quality of Electricity|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Access to Cell Phones|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Access to Internet in schools|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Bribery in Utility|EOS, 2003|1 (bad) - 7 (good)|101|271|
|State Capture|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Informal Money Laundering|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Street Crime|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Red Tape Cost of Imports|EOS, 2003|1 (good) - 9 (bad)|101|271|
|Bribery to Affect Laws|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Diversion Public Funds|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Illegal Party Financing|EOS, 2003|1 (bad) - 7 (good)|101|271|
|<br>Bribery in Permits|EOS, 2003|<br>1 (bad) - 7 (good)|101|271|
|Bribery in Tax|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Soundness of Banks|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Trust in Politicians|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Organized Crime|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Quality of Postal System|EOS, 2003|1 (bad) - 7 (good)|101|271|
|Health Access Gap|EOS, 2003|1 (bad) - 7 (good)|101|271|
||Offices of major advertising,||||
|Global City|<br>financial & accounting firms<br>(Taylor 2000)|Hundreds|114|261|
|City Population|Source: www.citypopulation.de,<br>2001|Logs|134|410|
|Website Dummy|City has a website (KLM 2003)|0 - 1 (yes)|133|398|
|Business Dummy|City website has info on how to<br>start a business (KLM 2003)|0 - 1 (yes)|133|398|
|Budget Dummy|City website has info on city<br>budget (KLM 2003)|0 - 1 (yes)|133|398|
|Port Dummy|City has port facilities (KLM<br>2003)|0 - 1 (yes)|134|411|
|Capital Dummy|City is the capital (KLM 2003)|0 - 1 (yes)|134|411|
|**Country Variables**|||||
|Globalization (Kearney)|Journal of Foreign Policy, 2002|0 (close) - 1 (open)|59|254|
|Control of Corruption|Governance Indicator, KK 2002|-2.5 (bad) / 2.5 (good)|134|411|
|Country Population|WDI 2003|Logs|134|411|
|Urbanization|% people not in agriculture (WDI<br>2002)|%|100|366|
|Income per Capita|Heston-Summers and CIA World<br>Factbook, 2001|Logs|134|411|

16
WPS4081

# **Utilities reforms and corruption in developing countries**<sup>**1**</sup>

## **Antonio Estache**

The World Bank and ECARES, Université Libre de Bruxelles

## **Ana Goicoechea**

The World Bank

## **Lourdes Trujillo**

Universidad de Las Palmas de Gran Canaria and Center for Regulation and Competition Policy, City University London

## **Abstract**

This paper shows empirically that “privatization” in the energy, telecommunications, and water sectors, and the introduction of independent regulators in those sectors, have not always had the expected effects on access, affordability, or quality of services. It also shows that corruption leads to adjustments in the quantity, quality, and price of services consistent with the profit-maximizing behavior that one would expect from monopolies in the sector. Finally, our results suggest that privatization and the introduction of independent regulators have, at best, only partial effects on the consequences of corruption for access, affordability, and quality of utilities services.

## World Bank Policy Research Working Paper 4081, December 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_ The authors are grateful for the insights and suggestions of Daniel Benitez, Cecilia Briceno, Claude Crampes, Mathias Dewatripont, Katharina Gassner, Jose-Luis Guasch, Paul Grout, Charles Kenny, Lazlo Lovei, Marco Manacorda, Martin Rodriguez-Pardina, Stephane Saussier, and Stephane Straub, and of participants in seminars at the University of Bristol, the Université Libre de Bruxelles, the Université des Sciences Sociales de Toulouse, the French State Council, and the U.K. Department for International Development.
# **Table 6 Fund characteristics by level of Active Share**

This table reports mean values for the variables by each investor and grouped by four intervals of Active Share. The intervals are defined based on the 25th, 50th, and 75th percentile of Active Share of foreign investors. Number of stocks denotes the different number of stocks held by each investor/fund by month. _Stocks traded_ are the different number of stocks traded by each investor/fund during a month. Monthly net flows are normalized by the value of the investor’s portfolio in the previous month. Percentage of investors in groups is the proportion of each investor type in the interval relative to the total number of investors in each type.

|Active Share Range|Group 1<br>[0 - 52.4]|Group 2<br>(52.4 - 76.6]|Group 3<br>(76.6 - 91.9]|Group 4<br>(91.9 - 100.0]|
|---|---|---|---|---|
|Foreign|||||
|Assets ($ billion cop)|44.8|11.8|7.4|16.0|
|Fund turnover ratio|0.4|0.9|1.3|1.2|
|Number of Stocks|15.0|9.8|6.7|4.2|
|Stocks traded|5.9|3.1|1.9|1.5|
|Net fows|0.3|0.1|0.0|0.0|
|% of investors in the group|25.1|24.9|25.1|24.9|
|% of total number of trades|48.1|18.2|21.2|12.4|
|Domestic Institutions|||||
|Assets ($ billion cop)|97.8|7.6|16.2|12.6|
|Fund turnover ratio|1.1|1.2|1.6|1.4|
|Number of Stocks|20.0|13.3|9.1|6.0|
|Stocks traded|5.6|2.9|2.1|1.7|
|Net fows|0.0|0.0|-0.1|-0.1|
|% of investors in the group|11.9|39.3|29.2|19.6|
|% of total number of trades|37.2|40.7|16.3|5.8|
|Domestic Individuals|||||
|Assets ($ billion cop)|1.8|1.0|1.0|0.3|
|Fund turnover ratio|0.7|1.4|2.6|3.5|
|Number of Stocks|16.1|11.6|9.5|8.3|
|Stocks traded|3.1|2.1|1.9|1.9|
|Net fows|0.0|-0.1|-0.1|-0.1|
|% of investors in the group|3.7|37.8|35.8|22.7|
|% of total number of trades|8.2|41.8|33.3|16.8|

31
economic consequences of the pandemic in terms of employment, unpaid care work, and genderbased violence (Flor et al. 2022).

In this paper, we examine the evidence on the gender-differentiated impacts of weather shocks to understand the potential gendered effect of climate change on a range of outcomes, organized around three areas covered by the World Bank Gender Strategy 2016-23: endowments (health and education), economic opportunities (combining access to jobs and assets as well as a broader view on aspects that affect economic opportunities), and agency (gender-based violence and early marriage). While we included some relevant studies from high-income countries, our focus is mainly on causal economics studies on low- and middle-income countries where the consequences of climate shocks are more severe. We conducted a search for studies on Google Scholar using keywords such as “causal impact of climate change on gender” and “gender impact of climate change” and combined these with other keywords such as “employment”, “education”, and “agency”. We ensured the relevance of the studies by reading them and identifying other relevant studies that are cited in them. Annex Table A.1 lists and summarizes all the papers cited in this literature review. Table 1 summarizes the differentiated impacts of weather shocks on men and women. Due to their past exposure to shocks and data availability, some regions and countries, such as India and Indonesia, have higher representation. Our study adds a gender lens to previous research that examined the impacts of weather events on a range of economic outcomes without a gender focus (Dell, Jones, and Olken 2014), and extends policy work on the topic (Erman et al. 2021) by providing more studies and details.

Girls and women are particularly vulnerable to the social responses triggered by weather shocks, especially in places where they face restrictive gender norms. For example, droughts can result in reduced resources that can lead to sex-selective abortions, malnutrition, and neglect of girls due to son preference. Thus, weather shocks have been associated with girls’ higher mortality (Flatø and Kotsadam 2014; Anttila-Hughes and Hsiang 2013; Haile, Tirivayi, and Nillesen 2019; Chatterjee and Merfeld 2021), poorer health (Hoddinott and Kinsey 2000; Rocha and Soares 2015; Thiede and Gray 2020), and higher school dropouts to care for household chores or early marriage (Björkman-Nyqvist 2013; Bau et al. 2020; Dasgupta and Karandikar 2021; Dessy, Tiberti, and Zoundi 2023). In adulthood, weather shocks can force agricultural households to withdraw women from work, leading to more childbearing and household work as they are less likely to migrate to find employment to overcome losses from lower agricultural yields (Dessy et al. 2020, Afridi et 3
pressures and the immigration of resistant and susceptible moths at certain times of the year." (underlined phrases, ours) Keeping in mind the above observation, we will elaborate on the role of local selection pressures and the role of pest migration as well as the existence of "dilutants" on the level of resistance. We will also discuss the development of resistance to synthetic pyrethroids (SPs) in other regions of India and finally, we will briefly look at the development of resistance to the other commonly used pesticides on cotton.

# Local Selection Pressures

SPs entered the Indian market in the late 705 and assumed importance in the early 80s. The Guntur market has always been a big consumer, accounting for as much as 40% of the total SPs consumed in India. They are cheap (SPs cost only about a third of the price of the traditional pesticides, per application), and broad spectrum and they initially gave unprecedented levels of pest control. This led to two developments. First, farmers began to place excessive reliance on their use. Instead of need based spraying farmers reverted to. time based spraying. This led to an excessive use of these pesticides. Second, side-by-side with the first development, because SPs quickly destroy the most susceptible population, they put a strong selection pressure on the surviving population so that resistant strains can develop rapidly. Thus bioassay evidence (Mehrotra, 1991) indicates that if the survivors of a first pyrethroid spray are inbred in the laboratory the RF can 46
45 correlated. In such case, the overall risk of a portfolio composed of a sufficiently large number of investments is minimized.<sup>6 1</sup> Despite the benefits of diversification, VCFs' pattern of behavior in terms of portfolio choice has shown a trend toward specialization. This is especially true in the U.S., Canada and the U.K.<sup>6</sup> 2 It is apparent that this choice of strategy is based on the belief that the benefits of specialization-based on comparative advantages in expertise--will counteract and probably overcompensate for the increase in portfolio overall risk due to a lower degree of diversification. The reason for this belief may be found in the peculiar traits of this industry, more precisely in the close involvement that venture capital firms want to have with their investees companies.6 This hands-on business style requires a know-how of the industry and/or product involved in the venture business. It would be very difficult, for example, for a venture capital firm to be able to be involved in the management of firms located far from one another, or in firms working in completely different economic sectors.

# 3. Skills

The success of venture capital firms depends largely upon the skills and competence of their managers and their ability to add value to the investees. In fact, it is often remarked that the financial contribution that the venture capitalist makes may be less valuable than the business expertise and the contacts it contributes to new or rapidly expanding businesses (IFC, 1986). The venture capitalist activity demands proficiency in a much wider range of areas than those in sphere of conventional financial institutions.

61 Wagner, W.H., and S.C. Lau (1971) showed, however, that the gains from diversification are exhausted fairly rapidly. Their results indicate that when the number of securities is increased beyond 10, the improvement is minor. In a diversified portfolio, the contribution of one asset to the total risk is given by the correlation of the returns of that asset with the returns of the rest of the assets in the portfolio. This is the systematic risk usually referred to by "beta". The unsystematic risk is eliminated.

62 In 1985 in Canada, about 60 percent of the venture capital funds followed a balanced or diversified focus portfolio strategy, while the remaining 40 percent were allocated according to some type of specialization criterion. In 1987 these percentages reversed. Among the resources that were allocated according the stage of development of the investee firm, 10 percent specialized by geographic region, and 3 percent according to the economic subsector of the investee firm (Venture Capital Journal, April 1988, and Canadian Venture Capital, February 1988).

In the U.K. the funds that are experiencing larger growth are those which concentrate on international investments and buy out financing. During 1987, the first three ECU (European Currency Unit)--denominated funds were launched in the U.K. and raised approximately 11 percent of the total capital raised during the year.

a This issue is discussed in the main text.
interpreted as the return to different characteristics (or more generally the effects on student performance) at different points of the conditional distribution. Following Koenker and Bassett (1978), the estimate of β(τ) is given by: β(τ) is estimated separately for each τ, and the vector of all quantile regression coefficients is: β β τ . Given that this is a model for the conditional quantiles of TS, while the intention is to estimate the unconditional quantiles of TS, one needs to integrate the conditional distribution over the entire range of the distribution of Xs. Melly (2005) shows that one can overcome the potential lack of monotonicity to derive the sample analogue of the θ<sup>th</sup> quantile as: where the θ<sup>th</sup> quantile of the sample is estimated by weighing each observation by ; under standard restrictions, is is a consistent and asymptotically normally distributed estimator. In practice, if the sample is large (as in our case), a smaller number of quantile regressions are estimated. Statistical inference can be conducted using bootstrapping.

Now consider distributions of two samples, 1 and 0, in our case one group in year 2007 and the other in year 2003 (TIMSS 2007 and 2003). The aggregate decomposition is as follows: where, _m,_ and _r_ refer to median coefficients and residuals respectively; , calculated , and , is the quantile estimate of the distribution that would have prevailed if the median return to characteristics had been the same as in sample 1 but residuals had been distributed as in sample 0. Therefore, the first component measures the residual effect, the second the coefficients structure effect and the last the composition effect.

# _2.3   Detailed decomposition: A “hybrid” approach_

In implementing the detailed decomposition at percentiles, in a first step, a reweighting approach is employed to derive estimates of the 2 components of the over-time changes in test score distributions. Using weighting functions, one can transform features of the marginal distribution of test scores into features of the conditional distribution of TS1 given and TS0 given , as well as the features of the counterfactual distribution of TS0 given . In deriving the re-weighting functions, the probability that a person belongs in group 1 conditional on X (“propensity score”) is derived from a logit regression.

In the second step, we use FFL’s unconditional quantile approach to derive the detailed decomposition, by deriving the recentered influence function (RIF) of the dependent variable at quantile τ, where can be estimated by the sample quantile and can be estimated using Kernel density. If the specification of the unconditional quantile regression is linear, i.e., 6