rafmacalaba/data-use-mentions-extended huggingface dataset:
In this paper, we develop a theoretical model that explains why and how people of different ages perceive the increasing threat of global warming. The model guides our empirical analysis, which relies on data from 38 countries of Europe, Central Asia, the Middle East, and North Africa collected in 2022−23, during the fourth round of the Life in Transition Survey (LITS).

> 2 Factors affecting climate change action include:  political trust and beliefs (Fairbrother et al. 2019); public opposition and political polarization (Lachapelle et al., 2012, <u>Hornsey et al. 2016); gender, race, education, and income</u> distribution (Ballew et al. 2020); and social norms and social capital (Bergquist et al. 2019)

2
Policy Research Working Paper 6362

# **Abstract**

Mass media play a crucial role in information distribution and in the political market and public policy making. Theory predicts that information provided by the mass media reflects the media’s incentives to provide news to different groups in society and affects these groups’ influence in policy making. The paper use data on agricultural policy from 69 countries spanning a wide range of development stages and media markets to test these predictions. The empirical results are consistent with theoretical hypotheses that public support for agriculture is affected by the mass media. In particular, an increase in media (television) diffusion is associated with policies that benefit the majority to a greater extent and is correlated with a reduction in agriculture taxation in poor countries and a reduction in the subsidization of agriculture in rich countries, ceteris paribus. The empirical results are consistent with the hypothesis that increased competition in commercial media reduces transfers to special interest groups and contributes to more efficient public policies.

This paper is a product of the Partnerships, Capacity Building Unit, 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 alessandro.olper@unimi.it and Jo.Swinnen@econ.kuleuven.be.

_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
(percent).<sup>11</sup> After identifying the suitability class of each soil property individually, based on the propertyspecific critical values borrowed from Naidu (2006) and further reviewed and modified with input from the World Agroforestry Centre, we utilize a fuzzy membership method to construct a membership grade for each suitability class, allowing for identification of the suitability class that best approximates the soil sample overall. The fuzzy membership method is commonly employed in land suitability analysis with GIS data (Ahamed et al., 2000; Ceballos-Silva & López-Blanco,

> 11 Multiple variations of the soil suitability framework were created, each containing a different combination of key soil properties. The selected framework that was chosen based on its superior predictive power in bivariate regression on yields measured via crop-cutting.

10
estimate parameters of demand and simulate coverage rate. Unfortunately, this approach will often not work for service such as improved supply of safe, reliable and sufficient water because there are no historical data in the context where the plans are being made. By definition, new government policies and improved products are beyond the range of historical experience. In this setting, the analyst typically has to rely on stated preference (SP) approaches such as contingent valuation surveys (also called WTP surveys) that directly elicit willingness (and ability) to pay statements from respondents (Mitchell and Carson, 1989). In other words, respondents are directly asked about their WTP (Whitehead et al., 2005). The method involves the development of a hypothetical market in the context of in‐person, telephone, mail, or other types of surveys. In the hypothetical market respondents are informed about the current problem and the policy designed to mitigate the problem. The state of the environment before and after the policy is described. Other contextual details about the policy are provided such as the policy implementation rules (e.g., majority rule) and the payment vehicles (e.g., increased taxes or utility bills). Finally, a hypothetical question is presented that confronts respondents with a choice about improved environmental quality and increased costs versus the status quo. Respondents can be presented with multiple scenarios and make multiple choices.

In addition to solving the problem of missing ‘behavioral data’, consider two advantages of conducting WTP surveys in this context. First, seeking households’ opinions and preferences during the planning and design stage is widely viewed as an important ingredient of the economic development process. Paraphrasing Sen (1998), participation is development. Second, the typical WTP survey presents an important form of experimentation which lies somewhere along the spectrum of laboratory experiments and observational studies. Such survey based ‘field experiments’ represent a practical mix of control and realism. Control comes from the design of the survey sample and the structure of
4 middle-income countries (Ahmed, Coulibaly, and Zlate 2017; Presbitero 2017; Reinhart, Reinhart, and Rogoff 2012).

Political instability increases risks of economic crises (Bussière and Mulder 2000). Institutional quality helps avoid macroeconomic imbalances and increase resilience of the economies to shocks. This argument has been robustly supported by the literature. Acemoglu et al. (2003) find that countries that inherited more ‘extractive’ institutions from their colonial past were more likely to experience high volatility and economic crises during the postwar period. Dabla-Norris and Gündüz (2014) find institutional quality to be the strongest predictor of a growth crisis. Collier and Goderis (2012) also show that vulnerability to negative shocks is heightened in resource-rich countries with weak institutions, while Elbadawi and Soto (2016) find the vulnerability of oil-rich countries to be conditional on bad political governance.

Low social indicators and high poverty rates do not help such resilience and may mean that an economic crisis of a smaller magnitude results in a reverse graduation because of its larger adverse social impact. Several studies highlight the implications for economic stability arising from households’ vulnerability to poverty as they are less able to cope with economic fluctuations due to limited access to risk-sharing mechanisms and capital markets (Calvo and Dercon 2013; Dercon et al. 2012). Briguglio (2016) stresses the importance of social indicators in economic vulnerability and economic resilience indexes.

# **III IDA Graduation Model**

In this section, we develop a statistical model of IDA graduation.

# _Sample_

Panel data were used for the sample period 1987–2016 to generate predicted probabilities of graduation status. Countries were included only if they had been IDA eligible for at least one year during the sample period and had a population of over 1.5 million.<sup>4</sup> In addition, country-year observations were included only if the GNI per capita for that year was at least 80 percent of the IDA operational cutoff value. As a consequence, this results in an unbalanced sample as not
jobs different from their prepandemic employment. Over time, this figure rose to more than 20 percent of workers in the second half of 2020, before slowing in 2021, at which point around one in four workers reported having a different job than before the pandemic (Table A6.6). The incidence of job switches is particularly high in LICs, primarily on account of SSA countries, with 40 percent of workers reporting having changed jobs by 2021 since before the pandemic.

We construct transition matrices that trace changes in individuals’ sector of activity or employment type. Because of limitations in the HFPS data, job changes within the same sector or within the same employment type are not captured. For example, the transition matrices would not capture workers who had a job in the services sector in February 2020 and switched jobs to a different services sector in 2021, but they would record a movement from services to agriculture, or from wage employment to self-employment. As such, job change estimates in the transition matrices are lower bound estimates of the total incidence of job 20
estimating the elasticity across suppliers within a product category lies in the fact that most firm-to-firm datasets do not contain product-level (unit) prices from each supplying firm to a buying firm, or lack the required variation in such prices to estimate these elasticities.<sup>10</sup> The lack of firm-level elasticities across suppliers has so far constrained our assessment of the importance of nodal firms, such as the most connected firms, in the propagation of shocks through production networks.

We contribute to the literature in each of these dimensions. First, we measure unit values (prices) and quantities at the transaction (seller-buyer-product-time) level. We derive price changes from supply and transportation disruptions in lockdown-affected districts and estimate the firm-level elasticity of substitution between suppliers within a product category. We then quantify how this elasticity amplifies firm-specific supply shocks through a roundabout production network

> 8A firm’s indirect connections measure not only the number of direct buyers of a supplier but also the buyers’ buyers and their buyers, and so on.

> 9See Bernard and Moxnes (2018) and Carvalho and Tahbaz-Salehi (2019) for a comprehensive literature review. 10Carvalho et al. (2021) observe a binary measure of whether firms were connected via buyer-supplier relationships rather than quantities and unit values associated with such transactions. They use a proportionality assumption, which precludes estimating the elasticity of substitution across suppliers within a product category, as a buyer sourcing from two suppliers in the same industry will source the same amount given the assumption. Although lacking firm-to-firm price data, Dhyne et al. (2022) structurally estimate a similar elasticity in the context of imperfect competition models where they restrict the elasticity to be larger than 1 for mark-ups to be relevant.

5
**Figure 2.   The relationship between village’s wealth and ICDS program participation**

<!-- Start of picture text --> All India All India<br>.9 .9<br>1992<br>.8 .8 1998<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>w ealth percentile (All India) w ealth percentile (by state)<br>Poor Northern states Rich Northern states<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>w ealth percentile w ealth percentile<br>Southern states North Eastern states<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>w ealth percentile w ealth percentile<br>share of villages w ith IC D S<br>share of villages w ith IC D S<br>share of villages w ith IC D S<br><!-- End of picture text --> _Source_ : NFHS I and II.

24
**A.   Data**

Poverty is usually defined as having insufficient resources or income. In its extreme form, it is a lack of basic human needs, such as adequate food, clothing, housing, clean water, or health services. Poverty is also a lack of education or opportunity, and may be associated with insecurity and fears for the future, lack of representation and freedom. Poverty is therefore a complex reality and may have many faces, often changing from place to place and across time.

Consistent with the existing literature, the paper focuses on the economic aspect of poverty, mainly captured through three indicators of poverty: the headcount index, the poverty gap, and the income of the poorest quintile. The econometric analysis uses panel data for 37 SSA countries averaged over five-year periods from 1992 through 2006.<sup>4</sup>

> 4 The choice of period (1992-2006) is dictated by the availability of data on the institutional and poverty variables.

6
views and give a sense of their lives. The testimonials capture the dire situation on the ground and provide a much richer qualitative picture that accompanies and complements the quantitative data. While the data may help the government fine tune its policies, the videos may reach a broader audience and depict the sense of powerlessness, the pain of hunger, the stress of hopelessness and the feelings of disappointment that express people’s experiences. Overall, this helps to create a more rounded perception of the situation on the ground in South Sudan.<sup>7</sup> The levels of deprivation documented by the HFS are staggering. As mentioned above, more than 4 in 5 people across the seven states covered in 2016 were living under the international poverty line of $1.90 USD PPP (83 percent). Such breadth of poverty places South Sudan among some of the poorest countries in the world. The depth of poverty is just as important as its breadth, with the average poor household consuming about one‐half of the international poverty line (a poverty gap index of 47 percent). The incidence of poverty is much more widespread in rural areas compared to urban areas, with the rural poverty headcount reaching up to 86 percent compared to 65 percent in urban areas (p<0.001). The rural poor also to experience a deeper poverty than urban residents, with a poverty gap equal to 50 percent compared to 31 percent in urban areas (p<0.001). Widespread fighting and large‐ scale displacement over several consecutive planting seasons have disrupted many households’ normal agricultural activities, resulting in increasingly large production deficits each year and widespread food insecurity. This has had a devastating effect on livelihoods, given that except for a few oil enclaves the productive structure of South Sudan is one of a rural pastoralist society where more than 4 in 5 people practice subsistence agriculture (World Bank, 2018, 2016).

Despite initial intentions to expand the HFS across the entire country, continued insecurity made
Table 1: Descriptive statistics for districts

||Mean|Standard<br>deviation|
|---|---|---|
|Urban manufacturing share for plants, 2000|0.30|0.23|
|Urban manufacturing share for employment, 2000|0.36|0.24|
|Urban manufacturing share for output, 2000|0.46|0.26|
|Share of population with a graduate education|0.06|0.03|
|Index of infrastructure quality for district (0-4 scale)|2.90|0.72|
|Share of villages in district with paved roads|0.61|0.26|
|Share of villages in district with electric power access|0.82|0.24|
|Share of villages in district with telecommunications access|0.50|0.27|
|Share of villages in district with sanitation and safe water|0.97|0.09|
|Manufacturing wage rate|3,505|2,696|
|Urban-rural wage ratio|1.49|1.73|
|District land area (km-sq)|9,581|8,486|
|Urban build-up share|0.02|0.09|
|Urban population share, 2001|0.23|0.18|
|Urban population share, 1991|0.22|0.17|

Notes: Indian descriptive statistics taken from Annual Survey of Industries, National Sample Statistics, and Population Census. District land area comes from Geographic Information System (GIS) data files of district boundaries in India.
College &Higher|-8.0|-13.0|-4.0|-6.5|

Source: Authors’ own estimate based on NSSO data.

Here we have an intriguing situation, wherein both educational attendance and attainment are associated with declining FLFP. This apparent decline in the FLFP rate, which occurs in the presence of an increase in educational attainment up to a given level, calls for much more detailed analysis. The decline in the FLFP rate for all educational categories and the particularly poor performance of those with secondary (10) and higher secondary (10+2) levels of education need to be explored in detail. In understanding this phenomenon, one potential point of consideration is the link between the marriage market, social status and educational attainment. Another possible explanation could be poor demand side conditions for educated women.

17
Aside from information on antidumping revocations (if any exist), there is nothing systematically missing from the Indian data for all investigations. Nevertheless, for any given investigation there may some missing elements such as dates, decisions or even product codes.

# **3.8 South Korea (KOR)**

In South Korea, the Korean Trade Commission is wholly responsible for anti-dumping investigations. This quasi-judicial agency determines if dumping and/or injury has occurred for each case. The initiation and outcomes of each investigation are published in the Korean Official Gazette (Gwanbo). The Korean Trade Commission also publishes information about ongoing antidumping cases online at its website. The source of the data was hard-copies of antidumping documents in Korean provided by the Korean Trade Commission. Summary information about Korea’s antidumping process is available on the Korean Trade Commission’s website.

In addition to the standard data described in section 2, the Korean file contains additional data described in the table below.

**<u>Table 3.8.1 : Additional Korean Data Included in AD-KOR-v1.0.xls</u>**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-K**|**OR-Master :**||
|**U.**|**P_AD_DUTY**|The maximum (or range) of preliminary anti-dumping duty (duties)<br>imposed|
|**V.**<br>**W.**|**F_AD_DUTY**<br>**REVIEW_DUTY**|The maximum (or range) of final anti-dumping duty (duties) imposed<br>The range and schedule of anti-dumping duties maintained after a sunset<br>review|
|**X.**|**REVIEW_F_DATE**|The date when a sunset review final decision was taken|
|
- Jones, Benjamin F. “The Human Capital Stock: A Generalized Approach.” _American Economic Review_ 104, no.11 (2014): 3752-77.

- Jones, Benjamin F. “The Human Capital Stock: A Generalized Approach: Reply.” _American Economic Review_ 109, no. 3 (2019): 1175-95.

- Klenow, Peter J., and Andres Rodriquez-Clare. _The neoclassical revival in growth economics: has it gone too far?_ ” NBER Macroeconomics Annual, MIT Press, Cambridge, MA (1997): 83–103.

- Kolen, Michael J., and Robert L. Brennan. _Nonequivalent groups: Linear methods. Test equating, scaling, and linking_ . (2014): 103-142.

- Kraay, Aart. “The World Bank Human Capital Index: A Guide.” _The World Bank Research Observer_ 34, no. 1 (2019): 1-33

- Mankiw, N. Gregory, David Romer and David N. Weil. “A Contribution to the Empirics of Economic Growth.” _Quarterly Journal of Economics_ 107, no. 2 (1992): 407-437.

- Mislevy, Robert J., Albert E. Beaton, Bruce Kaplan and Kathleen M. Sheehan. “Estimating Population Characteristics from Sparse Matrix Samples of Item Responses.” _Journal of Educational Measurement_ 29, no.2 (1992): 133-161.

OECD. _PISA 2015 Technical Report._ OECD Publishing, 2015.

- Patrinos, Harry Anthony, and Noam Angrist. _Global Dataset on Education Quality: A Review and Update (2000–2017)_ . The World Bank, 2018.

- Psacharopoulos, George, and Harry Anthony Patrinos. “Returns to investment in education: a further update.” _Education Economics_ 12, no. 2 (2004): 111-134.

- Reardon, Sean F., Demetra Kalogrides, and Andrew D. Ho. 2019. “Validation methods for aggregatelevel test scale linking: A case study mapping school district test score distributions to a common scale.
.5|6.2|11.8|18.7|4.1|7.7|16.3|2.4|
|From China|0.3|1.8|4.9|23.8|36.9|4.1|19.7|16.3|20.5|
|All Sample Countries (12)||||||||||
|From Developing without China|2.7|5.7|6.7|9.8|10.4|1.9|7.9|7.5|2.9|
|From China|0.5|2.3|5.0|17.6|22.5|1.9|15.7|7.5|15.0|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08. Sources: Based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

Table 8 shows the performance of developing countries with and without China for our sample countries. This table revises two important conclusions reached in this paper. First, most of the market share gains attained by the developing countries in the markets of industrial countries are driven by exports from China, which accounts for 72 percent of market share gains during the 2000s. In terms of annual market share gains, all developing countries excluding China increased their market shares in the markets of industrial countries only at 3 percent p.a. The ratio for China for the same period was 14 percent p.a.

Second, China also accounts for the bulk of the increases in South-South trade during the 2000s. China accounts for 82 percent of the market share gains of developing countries in the markets of our seven large developing countries. Without China, market share gains of all developing countries in our sample would be 2.4 percent p.a. versus 20
**Table S15. Prevalence of food insecurity**

||Moderate or<br>Severe Food<br>Insecurity|Severe Food<br>Insecurity|
|---|---|---|
|Ethiopia|-0.207***|-0.103***|
||(0.023)|(0.018)|
|Nigeria|0.081***|0.102***|
||(0.022)|(0.019)|
|Uganda|-0.346***|-0.183***|
||(0.021)|(0.014)|
|Ethiopia-Nigeria|0.000|0.000|
|Ethiopia-Uganda|0.000|0.000|
|Nigeria-Uganda|0.000|0.000|
|Observations|8,713|8,713|
|R-squared<br>_Note_: Each column re|0.147<br>ports results from a si|0.119<br>ngle regression of the|

_Note_ : Each column reports results from a single regression of the prevalence of either moderate or severe food insecurity or just severe food insecurity. Food insecurity is measured using the FIES. The FIES variable is regressed on indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia, round 2 in Malawi, Nigeria, and Uganda. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

42
# **Figure 3: Trends in under-5 and adult mortality**

# **(A) Overall**

<!-- Start of picture text --> 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>Period<br>U5 mortality: All-Non SSA U5 mortality: All-SSA<br>U5 mortality: Overlapping-Non SSA U5 mortality: Overlapping-SSA<br>Adult mortality: Non SSA Adult mortality: SSA<br>(B) Within Sub-Saharan Africa<br>1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>Period<br>U5 mortality: SSA, Lower HIV U5 mortality: SSA, Higher HIV<br>Adult mortality: SSA, Lower HIV Adult mortality: SSA, Higher HIV<br>.06<br>200<br>.05<br>150 .04<br>.03<br>100<br>Adult mortality<br>.02<br>50<br>.01<br>0 0<br>200 .05<br>.04<br>150<br>.03<br>100<br>.02<br>Adult mortality<br>50<br>.01<br>0 0<br><!-- End of picture text --> Note: “All” refers to full sample; “Overlapping” refers to countries/years that are also in the adult mortality database.

35