rafmacalaba/data-use-mentions-extended huggingface dataset:
- Smythe, I. S., & Blumenstock, J. E. (2022). Geographic microtargeting of social assistance with high-resolution poverty maps. _Proceedings of the National Academy of Sciences_ , _119_ (32), e2120025119.

- Stifel, D., & Christiaensen, L. (2007). Tracking poverty over time in the absence of comparable consumption data. _The World Bank Economic Review_ , _21_ (2), 317-341.

- Tanzania’s National Bureau of Statistics. (2011). _Basic Information Document—National Panel Survey 2010-11_ .

- Tarozzi, A. (2007). Calculating comparable statistics from incomparable surveys, with an application to poverty in India. _Journal of business & economic statistics_ , _25_ (3), 314-336.

- United Nations. (2018). Classification of individual consumption according to purpose (COICOP). _Statistical Papers. Department of Economic and Social Affairs. Statistics Division_ , 265.

- UNESCO-UIS/OECD/EUROSTAT (UOE). (2020). Data collection on formal education— Manual on concepts, definitions and classifications. _Montreal/ Paris/ Luxembourg_ .

- United States Census Bureau. (2017). Current Population Survey, Imputation of Unreported Data -

- Items. Accessed on the Internet on May 24, 2021 at <u>https://www.census.gov/programs surveys/cps/technical-documentation/methodology/imputation-of-unreported-data-items.html</u>

World Bank. (2021). _World development report 2021: Data for better lives_ . World Bank.

- Yoshida, N., Takamatsu, S., Yoshimura, K., Aron, D. V., Chen, X., Malgioglio, S., ... & Zhang, K. (2022). _The Concept and Empirical Evidence of SWIFT Methodology_ . World Bank.

30
liquid waste in the rivers, and the recurring problem of rivers and streams overflowing their banks, which has often caused serious tragedies. When the municipality decided to tackle these issues, there was no organized opposition but also no organized support for it doing so. It was unusual for municipalities to take action on such matters, so while San José’s efforts did not violate any formal restrictions on its authority, they did run against stereotypical views of municipal responsibilities.

To pursue its interest in these environmental quality-of-life issues, the municipality of San José initiated a series of activities in 1991 and 1992, of which the following four stand out: In 1991 it proposed a Project for the Recuperation of the Río Torres. This project focused on controlling the disposal of solid and liquid wastes in the Río Torres, a major upper-basin river and one of the most polluted rivers in the Tárcoles basin. The Río Torres project was assisted with funding from France as part of the CIUDAGUA program. Also in 1991, in another unusual action for a municipality, San José issued a municipal policy directive giving industries located in the canton 24 months to begin treating their liquid waste. As a result of that directive, a pilot plan was launched by the Municipality and the Chamber of Industry involving a group of businesses that were major polluters according to data from AyA. When the pilot program ended in 1994, there had been a significant reduction in industrial waste (Rodríguez, 1997).

In 1992, the Municipality of San José adopted an Urban Control Plan to regulate land use and urban growth, which included a series of environmental provisions to protect surface water, aquifers, and recharge areas.

By this time, though, officials and staff of the Municipality had come to recognize that these environmental matters could not be resolved by actions in only one canton. The situation would have to be approached with a broader view, one that included the entire Tárcoles basin. Visits were made to each of the other 35 municipalities in the river basin area to promote
16

# **Comparison of Moscow trends with Yaroslavl**

Of considerable interest for comparison with the privatization situation in Moscow are the findings of a survey conducted in 1993 in Yaroslavl, a historical city and major industrial center with a population of 628,O00.9 The survey revealed much the same trends as in Moscow. I'he rates of housing privatization varied substantially by location from the city center, and a comparison of the age of dwellers living in privatized and in separate, state-owned apartments showed that an overwhelming majority of privatizers are older tenants.

# **1.2.2. The privatization processes in provincial Russian towns**

The findings of sociological surveys conducted in late 1996 in Vladimir, an oblast center, and in Gorodets, a small town in Nizhny Novgorod oblast, permit an analysis of the privatization process in provincial locations that is analagous to the analysis for Moscow described above.

In 1992-1993, Moscow was the national leader in the proportion of privatized/already private housing. By late 1996, the share of privately-owned housing in Vladimir caught up with that in Moscow, and the share in Gorodets exceeded the Moscow rate by 1.5 times, constituting 48% and 66% respectively (Table 1.12). This can be explained, in part, by the presence of private single-family "houses there long before privatization began (in Vladimir, 6% and in Gorodets, fully one-third of the housing stock) l O. At the same time, the privatization rate of the multi-unit housing stock that used to belong to the state and to enterprises also exceeded the rate in Moscow, rising to 47% in Vladimir and to 50% in Gorodets (Table 1.13). The data on the privatization rate of multi-unit housing stock reflects the role of quality and location of housing in the households' readiness to privatize.

**Table 1.12.** Vladimir and Gorodets, housing stock byt.e of ownership,,1996 **(%).
# Table A.10: Husband’s and Wife’s Fertility Preferences

|Full sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|
|**Panel A: Husband’s ideal number of sons**|||
|Fraction of female leaders<br>-0.021|0.026|-0.269|
|(0.156)|(0.180)|(0.203)|
|Observations<br>44,130|31,059|13,071|
|Outcome mean<br>1.209|1.253|1.105|
|**Panel B: Wife’s ideal number of sons**|||
|Fraction of female leaders<br>0.184|0.162|0.158|
|(0.120)|(0.131)|(0.170)|
|Observations<br>61,735|43,676|18,059|
|Outcome mean<br>1.187|1.243|1.053|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are husband’s ideal number of sons in Panel A and wife’s ideal number of sons in Panel B. Column (1) presents estimates for the full sample, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which
al., 2013). This happens because income information is considered “sensitive”, and people are reluctant to report actual earnings, and may choose not to respond to those questions at all (Moore et al., 2000; Hagenaars and Vos, 1988). Field tests conducted in the past have shown that asking follow-up income questions in a series of unfolding brackets achieves superior results in terms of response rates for income amounts, as was the case of the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System Survey (BRFSS), both administered by the Center for Disease Control and Prevention of the United States (Angelov and Ekstrom 2018, Yan et al. 2018).
consumption, a household wealth index was constructed from the data on household assets using the method based on principal components (see, for example, Filmer and Pritchett 2001; Rutstein and Johnson 2004).

The NFHS provides height and weight data for children under age 48 months in 1992/93, under age 36 months in 1998/99, and under age 60 months in 2005/06. The NFHS contains no anthropometric information for deceased children at the time of their death. For comparability between NFHS rounds, the sample was restricted to children under age 36 months. The analysis focuses on the age-adjusted measure of height-for-

> 3 Kashmir, Sikkim, and some remote territories were not covered in NFHS-1. Detailed information on NFHS methodology and sample design is available at www.nfhsindia.org/. The data are available from MEASURE DHS, Macro International Inc. at www.measuredhs.com.

> 4 The number of observations in the 1998/99 round is smaller, as it collected height and weight information only for the last two children under age 3 of ever-married women who were interviewed. In the 1992/93 round, measurements of height were not collected in Andhra Pradesh, Himachal Pradesh, Madhya Pradesh, Tamil Nadu, and West Bengal.

4
The data include balance sheet information for each establishment’s latest fiscal year including sales, the number of full-time employees, total labor compensation (wages), investment, and energy consumption. As such, they allow us to assess the impact of climate change on a variety of firm performance measures. They also provide detailed information on establishments’ characteristics such as their year of establishment (age), the main market of their sales (international, domestic, or local), and the main economic activity (4-digit ISIC codes). Moreover, they include detailed information on the business environment, ranging from access to finance, transport, electricity, trade restrictions, business regulation, and corruption. The data are thus well-suited to test the specific microeconomic mechanisms underlying the impact of climate change on firms and to identify policy constraints that may undermine firms’ ability to adapt. Appendix subsection A.1 provides additional details on the survey design and sampling.<sup>1</sup>

# **4.2 Matched firms and climate data**

The climate data comes from the EU Copernicus satellite and ground-based measurement system, which provides, among other, highly granular geospatial information on hourly temperatures (27 x 27 km at the equator) for the entire world, from 1950–2023. The geocoded weather data include hourly temperatures from which we compute the average annual temperature for the detailed location of each firm.

We match establishments in the WBES data with their relevant weather data based on firms’ geocodes. This is done in three steps. First, we collapse daily temperatures at the yearly frequency by computing within-year measures of weather-related shocks for each firm, exactly matching each firm’s fiscal year by the day. That is, as firm-level balance sheet data refers to the last fiscal year with respect to the date of the interview, we do not aggregate weather data according to calendar years, but consider each firm’s relevant fiscal

> 1The harmonized WBES data are available online at https://www
into the study. We conducted a listing exercise in these 100 villages to classify households as eligible or ineligible according to the VG criteria, and to identify key individuals.

This pipeline of 119 active groups was randomized at the village level in to two tranches. The Early group was to be treated right after baseline (59 groups receive TASAF early treatment), and the Late group to be delayed for a year (60 groups receive TASAF late treatment), with the 12month midline survey conducted immediately before the Late group was treated. This randomization was blocked by district and group type (livestock groups versus non-livestock groups). Importantly for the interpretation of our results, the timing structure of the experiment was common knowledge to the beneficiaries, introducing the possibility that the control group behavior in the midline was altered by the fact that they were about to receive their transfer. Anticipating this issue, we retained the Eligible Non-Beneficiary (ENB) households in the panel data set to have a second (albeit non-experimental) control group that is never treated.<sup>9</sup> The entire study timeline was shifted off for the districts of Moshi and Lushoto, which began first, relative to the remaining districts which trailed by roughly ten months. Because the experiments were blocked by district this does not affect results. Early treatment disbursements took place in December 2008/early 2009 in Moshi and Lushoto, and were made between September and October 2009 in the remaining three districts. Late treatment took place in early 2010 in Moshi and Lushoto and in December 2011/January 2012 elsewhere. Figure 1 shows the timing of the surveys and the interventions.

# **3.2. The Training Experiment**

Human and managerial capital is a critical input to the generation of entrepreneurial returns (Ibarrarán et al. 2019, Bruhn et al. 2010), and in the context of group investments training also has

> 9 These are households in study villages that met the technical eligibility for TASAF in containing an individual defined as ‘vulnerable
F-Test**|||||{.180}|
|**Mean of outcome variable**|||.829|||
|**Adjusted R-squared**|.004|.005|.005|.012|.018|
|**Observations**|5,734|5,734|5,734|5,734|5,734|

**Notes:** *** denotes significance at 1%, ** at 5%, and * at 10%. Data is all from the baseline survey, except PPI scores which were compiled during the village census prior to collection of the baseline survey. All regressions include dummies for each randomization strata (district) and errors are clustered at the unit of randomization (village). Individual controls include: age, enrolment at baseline, employment, PPI score, marital status, household size. Village controls include: village size (nr of dwellings), distance from secondary school, distance from PHU, whether the village is a political stronghold, number of NGOs active, average PPI score, share of Christians, whether the village received food or school relief, distance from Freetown and from Kailahun. Interactions include: enrolment, employment, age and PPI score.
4 percent most educated and wealthy people and the 3.3 percent least educated and wealthy from the treated and control groups’ respective distributions. In Columns 3 and 4, we use the in-migration rate from 1975 to 1980 to trim the 0.4 percent most educated and wealthy people and the 0.6 percent least educated and wealthy from the control group’s respective distributions. In Columns 3 and 4, we use the in-migration rate from 1979 to 1985 to trim the 0.7 percent most educated and wealthy people and the 0.8 percent least educated and wealthy from the control group’s respective distributions. The unit of observation in all columns is the census tract. Information from all columns was obtained from the Population Census of 2007. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

# **V.C External validity**

In Appendix D, we conducted two analyses to rule out that the results are specific to our RD sample. First, we show that at baseline the RD sample is similar in characteristics to the rest of the country (see Table D.11). Second, we show how the main results change once we move outside the two km bandwidth. Figure D.11 shows that results are pretty homogeneous across space.

# **VI EXPLORING POTENTIAL MECHANISMS**

Why would the FMLN’s influence endure so many years after its territorial control ended? As discussed above, one explanation concerns the reshaping of economic structures and local governance that led to the demise of the former commercial agriculture system and disengagement with the state.<sup
lobbyists, often on an issue-by-issue basis, so that informational lobbying by those lobbies is influential on the margin). Hence, informational lobbying of Agriculture committee members is _not_ influential on the margin in the formation of coalitions.

This view of Agriculture committee members possessing a strong continuing link with PACs, whose preferences have been stable over time, is endorsed in Parker and Parker (1998). They factor-analyze data on voting within committees, where leadership is less partisan and more attuned to the issues and lobbying interests surrounding those issues, versus voting on related issues after those issues have left the committee for the floor, where floor leaders are more partisan and seek to establish coalitions based on party and ideology. Parker and Parker’s aim is to differentiate committees in the House in which voting by their members do not change as issues move from committee to the floor from committees in which voting does change. Agriculture and Ways and Means are the two highest ranked in terms of the stability of coalitions as their issues move from committee to the floor.<sup>3</sup> Parker and Parker take this to imply that the influence of special interests on forming preferences of committee members on these committees is strong. In other committees, members often change their votes on the floor, indicating that their preferences while serving on committees were weak to begin with.

In sum, evidence on the relevance and effectiveness of informational lobbying, at least in the context of agricultural policy, is an open issue and deserves further investigation. Hansen finds for informational lobbying, while Parker and Parker do not. Hansen’s is a longterm exercise and identifies threshold points in time during which legislators sought better information because they recognized it to be marginally effective in increasing electoral strength. Shorter run studies do not find this to be the case.

# C. _Quid Pro Quo Lobbying_

In contrast to Hansen (1991), Grossman and Helpman (1994) put forth a theory of how policy responds to lobbying contributions. In their model, money
the probability of surviving in the second round is the probability of winning conditional on making it to the second round, whereas the unconditional probability is defined in terms of a single event, i.e., the probability of winning the second round match. Similarly, hazard models account for the fact that the data contains not only information on

> 14 Such evidence has often been shown using the Crépon et al. (1998) or CDM model, which is the most popular method to estimate the innovation-performance link. The method has been applied to Chile using perception-based innovation measures for a cross-section of plants in the Chilean Innovation Survey by Crespi and Zuniga (2012). This method suffers however from problems of weak identification in the estimation of a causal relationship between innovation and firm performance. For example, as acknowledged by Crespi and Zuniga (2012), in order to be identified, the model needs to assume that certain firm characteristics such as firm size affect only the decision to invest in innovation activities but not the amount to be invested, nor the knowledge generated from such investments. 15 See Kiefer (1988), Klein and Moeschberger (1997), and Hosmer and Lemeshow (2008) on hazard models. The use of hazard models follows the empirical literature on firm survival reviewed by Manjon-Antolin and Arauzo-Carod (2008). In these models, the key concept is the hazard rate which is the probability that a plant will experience an event (exit) at time _t_ , given that the plant is at risk for having an event (the plant survived until _t-1_ ). The hazard rate is the unobserved rate at which events occur.

7
# A profile of border protection in Egypt: An Effective Rate of Protection approach adjusting for energy subsidies

Alberto Valdés and William Foster

|1. Introduction .................................................................................................................. 1|
|---|
|2. Effective Rates of Protection in Egypt: Cost-share Data Sources and Previous Studies<br>.......................................................................................................................................... 3|
|3. A Quick Review of the Algebra of Effective Protection .............................................. 4<br>a. Deriving the basic ERP formula .......
# **Buffer or Bottleneck? Employment Exposure to Generative AI and the Digital Divide in Latin America**<sup>1</sup>

Paweł Gmyrek, Senior Researcher, ILO Hernan Winkler, Senior Economist, World Bank Santiago Garganta, Senior Researcher, CEDLAS-UNLP © 2024 The World Bank and the International Labour Organization 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, or those of the ILO.

1 We would like to thank Richard Samans, Sergei Suarez Dillon Soares, Janine Berg, Leonardo Iacovone, Paulo Bastos, William Maloney, Carlos Rodriguez Castelan, and staff from ILO Research and from the World Bank Poverty team for valuable comments. We also thank ILO STATISTICS team for data contribution, particularly David Bescond, who produced several inputs to our estimates based on ILO micro data repository, as acknowledged in detail in the paper. This article is a product of the staff of the World Bank and the ILO. It has been released both in the World Bank Policy Research Working Paper Series, and in the ILO Working Paper Series (https://doi.org/10.54394/TFZY7681, ISBN: 9789220410035). It also serves as a background paper for the World Bank Regional Study “Digital Equality: A Pathway for Inclusive Growth in LAC”.
Human Opportunity Index (HOI) developed by the World Bank suggests that, although in LAC access to education and basic public services remains far from universal, the region made important progress in providing access to basic education, sanitation, and connectivity during the last 15 years (World Bank, 2016a). Nevertheless, household characteristics (family composition and characteristics of the household head) and income per capita at the household level continue to be strong determinants of access to such opportunities. In addition, heterogeneity across countries continues to be a pressing issue, since countries in Central America show low levels of HOI along with unequal opportunities for children (World Bank, 2016b).

The channels by which economic growth has benefited the poor are not immediately obvious and with this paper we aim to contribute to understanding if and how the expansion in coverage and the subsequent increase in access to opportunities could have affected welfare among Latin Americans.

# **3.2. The evolution of access to services in Latin America**

During the last five decades, access to basic public services and education has been increasing continuously in LAC. With few exceptions—such as Bolivia— countries in the region have seen a massive increase of human opportunities since the 1960s. Even in periods of low economic growth, like the 1980s and 1990s, when average growth rates of GDP per capita were 0.1 percent and 0.7 percent, respectively, access to basic services continued to increase. However, improvement in the region has lagged the achievement of other countries. For example, when we compare educational attainment in LAC against countries from other regions, like Thailand or 11