rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2.3 Data Set Construction**

Our data on country accession to the Montreal Protocol and its amendments, including dates of accession, are taken from the United Nations Environment Programme (UNEP) website.<sup>7</sup> In addition, data on ODS consumption are taken from the United Nations Environment Programme (UNEP) database.<sup>8</sup> The data are self-reported at the country-year level in ozone-depleting tonnes. We target seven of the ten substances regulated by the Montreal Protocol reported by UNEP: CFCs, halons, other CFCs, CTC, TCA, HCFCs, and MB. We omit HBFC and BCM as data are only available for a handful of countries. Note that these data reflect net consumption levels—the data record negative values if net destruction of ODS exceed consumption levels.

For the last group of substances regulated by the Montreal Protocol, HFCs we use data on emissions from the Emissions Database for Global Atmospheric Research (EDGAR) (Crippa _et al._ , 2021).<sup>9</sup> Emissions are given at the yearly level in kilotons from 1990-2018. For our study, we focus on five HFCs that have been common substitutes for CFCs and HCFCs – 23, 32, 125, 134a, and 152. We convert emission kilotons to CO2 equivalent tons and aggregate the five HFCs together.

Since ODS consumption is bound by the phaseout schedules set by the Montreal Protocol, a country’s ODS consumption from UNEP needs to be normalized by the country’s baseline in order to determine the degree to which the country is in or out of compliance, as well as how trade agreements impact compliance. The Montreal Protocol does not specify yearly targets, only dates for key reduction targets. For example, for Article 5 countries, 2007 CFC consumption can be no more than 15% of base level, and 2010 consumption can be no more than 0% of base level. We construct yearly consumption targets for each country
from high-income countries. This price is fixed at unity in the base year and throughout the projection period to 2015.

The version of the LINKAGE model used for this study is based on the GTAP database and is solved with 27 regions and 25 sectors. There is a heavy emphasis on agriculture and food, comprising 13 of the 25 sectors, and a focus on the largest commodity exporters and importers.

# **Effects of current protection policies**

The LINKAGE model provides a baseline projection of the world economy first to 2005 and then to 2015 assuming no other policy changes. Deviations from that baseline in 2015, due to total liberalization from 2005, are then examined. The first step requires a pre-simulation to bring the world as depicted in the GTAP dataset in 2001 up to the start of 2005. In terms of policy shocks, we include only key multilateral commitments in that pre-simulation, namely the final stages of Uruguay Round implementation including the phase-out of the Multifibre Arrangement (MFA), the accession of China and Taiwan to the WTO, and the eastern enlargement of the European Union from 15 to 25 members. The impacts of those three reforms are non-trivial: had they not been implemented, the dynamic gains in 2015 from freeing global merchandise trade would have been an extra $64 billion per year. Nearly half of that difference is due to the removal of MFA quotas and hence should be considered part of the Uruguay Round’s legacy. The effect of those reforms on tariffs can be seen by comparing the estimates at the start of 2005 in Table 2 with those for 2001 in Table 1.

The next step is to measure the prospective effects of removing all agricultural subsidies plus those tariffs summarized in Table 2 over the 2005-2010 period. This could be done for each 9
2,500 case records from DHS survey clusters for the southwest coastal region in 2018 and 2022. Our analysis asks whether three standard measures of child health – mortality, diarrhea incidence and stunting – are worse for children born during the annual peak salinity months. We find substantially higher mortality and stunting rates for children born during those months, while the results for diarrhea are mixed.

In summary, our research suggests two basic conclusions about salinity responses in southwest coastal Bangladesh. First, the results show that salinity change in riverine Bangladesh has very large consequences for fish protein availability, and the available evidence suggests that this remains a significant child health problem despite the continuing fall in the national poverty rate. The second conclusion relates to methodology. Traditionally, projections of salinity impacts on the size and spatial distribution of fish stocks have relied on technically determined salinity tolerance parameters. However, the results of our econometric analysis suggest that these parameters have limited utility for projecting the impact of salinity changes on actual fish catches. Future projection exercises might well benefit from using more empirical evidence on the timing and magnitude of fish catches as salinity changes.

With the near certainty that sea-level rise will persist beyond 2100, even with immediate stabilization of greenhouse gas emissions, coastal regions like Bangladesh are at the forefront of climate change impacts. Our research highlights how coastal communities adapt to increasing river water salinization by modifying their behaviors. These adaptive strategies provide valuable insights into the potential responses of millions of households worldwide who will confront similar challenges by the end of the century.

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Poverty Alleviation and Interhousehold Transfers: Evidence from BRAC’s Graduation Program in Bangladesh<sup>*</sup> SELIM GULESCI

# **1 Introduction**

In rural parts of developing countries, individuals are faced with substantial risks and often rely on transfers from their social networks for consumption-smoothing (Rosenzweig (1988), Udry (1994)); yet there is limited evidence on how large-scale affect interhousehold transfers. This evidence antipoverty programs paper provides on the causal effect of a large-scale poverty alleviation program on informal transfers within rural Bangladeshi communities. Implemented by the NGO BRAC, “Targeting the Ultra Poor” program (henceforth “the program”) targets the poorest households in rural communities in Bangladesh and provides them with a transfer of productive assets, training and other support services. Bandiera et al. (2017) show that the program leads to long-run improvements in the socio-economic conditions of beneficiary households. Similar programs targeting ultra-poor households have been evaluated in other contexts and, by and large, have found positive effects on socio-economic conditions of the targeted households (Banerjee et al. (2015, 2016, 2018), Blattman et al. (2016), Collins and Ligon (2017)); but to date, we have limited evidence on these proeffects on informal transfers. In this I the randomized roll-out grams’ paper, exploit of the program and panel data covering over 21,000 households over seven years to study the program’s effects on interhousehold transfers.

> *Selim Gulesci is Associate Professor at Trinity College Dublin, his email address is: gulescis@tcd.ie. The author thanks the editor (Eric Edmonds) and three anonymous referees for comments that improved the paper; Oriana Bandiera, Tim Besley, Robin Burgess, Maitreesh Ghatak, Imran Matin, Imran Rasul and Munshi Sulaiman for their support and advice throughout the project; Yann Bramoullé, Greg Fischer, Eliana La Ferrara, Rohini Pande, Jean-Phillipe Platteau for helpful discussions; seminar participants at Aix-Marseille, Bocconi, EBRD, Edinburgh, IFS
This study makes several distinct contributions to the extant literature, specifically it: (i) explores how well water infrastructure can cope with extreme weather variability across a wide range of countries, most of which are in the developing world – where water infrastructure is likely to be less robust, (ii) establishes the relationship between water infrastructure quality and manufacturing firm performance using nationally representative firm-level data for 103 economies, (iii) quantifies the magnitude of this effect, and (iv) presents results that are differentiated by income level, according to the World Bank income classification.<sup>2</sup> The rest of the paper is structured as follows. Section 2 presents the data, section 3 outlines the empirical specifications used to examine, first, the impact of extreme weather events on water supply, and, second, to analyze the effect of supply shortages on firm performance, while section 4 presents the results of these analyses. Section 5 presents several robustness checks; finally, section 6 concludes.

# 2. Data

The firm-level data source is the World Bank Enterprise Surveys. For the analysis, over 16,000 manufacturing firms covering a cross-section of 103 countries between 2009 and 2015 are used.<sup>3</sup> The Enterprise Surveys (ES) provide a suitable data source for our analysis for several reasons. First, the surveys are nationally representative, stratified by location (within country), firm size, and sector. Second, the data are comparable across economies; the Enterprise Surveys (ES) use a standard survey instrument to collect data employing the same methodology across all countries; they target business owners and top managers of firms as respondents. Third, the surveys have high coverage in terms of countries and topics including access to finance, corruption, infrastructure, crime, competition, labor, business environment obstacles, and performance. The comparability of data across countries as well as the high coverage is a rarity given that many survey data sets face issues of low country coverage,
cannot attribute school spending to specific sources of funding (discretionary vs. rule-based). However, the total shares spent on those items most suitable for substitution (books, chalks, and stationary) add up to 57% and 47% respectively for schools without and with discretionary funding, suggesting that in both cases, substantial and similar spending occurs on items that could be substituted by households.

This also helps rule out explanations based on diminishing returns to the items procured or the durable nature of school materials. In both countries, the majority of the grant is spent on material that is used up during the school year (stationery, notebooks, practice books, etc). In the AP experiment, it is possible that some of the classroom materials purchased may be durable, 24
speed with which a 9 A total of eight different regressions are estimated in this paper, half of which are based on the original IEF index while the other half use the modified IEF index discussed in Box 3. A thorough discussion of the estimation procedure is presented in Appendix 2 of this paper.

10 We begun with a total sample of 100 countries. However, as can be observed in Appendix 3, this number got reduced to slightly over 80 since some data was not available for our original sample, particularly that on the level of educational attainment. Note that CEE countries have been excluded from the econometric estimation. Also, we modify in all regressions the data from Johnson and Sheehy by inverting the index ordering so that higher numbers --the range is from I to 5-- indicate less government intervention or a higher degree of economic freedom. The goal of this modification is to make this index consistent with equation [4].

11 Note that the sum of a and a in Table 3 leads to a share of capital, in its two types, equal to approximately 60 percent of total output. This is consistent with the finding that high shares of capital are required to explain slower speeds of convergence in neoclassical growth models. In this respect, the usual shares of capital of close to 30-40
- _3.4. The Size of the Welfare State, the Demand for Social Insurance, and Parents’ Incentive to Instill Work Norms in their Children_

A longstanding puzzle is the considerable difference between Western Europe and the US in the size of the welfare state. Several reasons have been put forward as an explanation for this difference; among them are differences in political institutions and in ethnic heterogeneity (Alesina and Glaeser 2004) and self-fulfilling beliefs and multiple equilibria (Piketty 1995, Bénabou and Tirole 2005). However, it may also be the case that the willingness to migrate to the US has historically been associated with different ideologies and these ideologies may have affected the population‘s preferences. In order words, the US population may have a different demand for social insurance and this may be another reason for the differences in the welfare state. A recent paper (Naef _et al_ . 2011) provides evidence for the hypothesis that the US population is less risk averse than the German population. Whether this preference difference holds more generally between the US and Europe is, however, not known.

Eugster et al. (2011), in this symposium, study the impact of differences across language groups in the demand for social insurance across the cultural boundary defined by German and ―Latin‖ ( _i.e_ . French, Italian, and Romansh) language groups in Switzerland. The authors use a regression discontinuity design to identify cultural differences in the demand for social insurance. The Swiss case is well-suited for this research design because on both sides of the language border exactly the same legal system prevails. This means that the supply of social insurance ( _e.g._ unemployment insurance, the retirement system, maternity leave, etc.) is identical. In addition, the wealth distribution, the probability of becoming unemployed, and other risks are similar on both sides of the border. Eugster _et al._ use data from referenda over the period 1980-2009 to document a persistent difference in the demand for social insurance – the
policies as most appropriate for different developing country contexts. This requires more impact evaluation studies of innovation policies targeted at green technologies.

The next section explores recent data on existing patterns of green innovation. The third section then presents an overview of green innovation policies, discussing the policy rationale and the range of available instruments. A final section provides summary recommendations.

# **2 GREEN INNOVATIONS – GROWING FROM A SMALL BASE**

Innovation in the context of development should be defined broadly as the commercialization of new ways to solve problems through improvements in technology, with a wide interpretation of technology as encompassing product, process, organizational, and marketing improvements. Besides frontier (new-tothe-world) innovations, this definition includes catch-up innovations, namely the diffusion (both across and within countries) and the adaptation to local context of existing green products, processes, organizational and marketing technologies.<sup>2</sup> Green technologies comprise a vast range of fundamentally different technologies that support wealth creation and achieve more resource-efficient, clean and resilient growth:

- Regarding pollution reduction and greater resource efficiency, technologies include improved recycling and energy efficiency in buildings (thermal insulation and new materials, heating, energyefficient lighting), production processes (new uses of waste and other by-products from firms into production inputs at the same or other firms), agriculture (from GM crops to mechanical irrigation and farming techniques), transport infrastructure, and urban design (including land use).

- Regarding climate change mitigation, technologies include cleaner energy supply (wind, solar, geothermal, marine energy, biomass, hydropower, waste-to-energy, and hydrogen fuels), end-use (electric and hybrid vehicles, climate-friendly cement), and carbon capture and storage (CCS).

- Regarding adaptation, they include more climate-resistant products and processes appropriate for changing environments (such as higher yield seeds for more arid and saline soils together with drought-resistant cultivation practices) and tools to understand and insure against climate risks with improved early-warning system processes (sea-walls, drainage capacity, reductions in environmental burden
module.<sup>10</sup> In particular, we focus on women and youths’ reporting of employment and work compared to men and adults, respectively, within the context of a household survey. We conducted this experiment in four stages described below.

> 9 To our knowledge, only one study shows how response fatigue can have differential effects on the reporting of productive activities by gender and age (Ambler et al., 2021). The authors, however, do not study the impacts of any specific survey method on the reporting of labor outcomes.

> 10 The standard labor module follows the guidelines of the 19th International Conference of Labor Statisticians (ICLS) and collects respondent labor data for the previous 7 days.

6
in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years), where the weights used are the<br>proportion of firms covered in the calendar<br>year.|
||Source: Doing Business, World Bank.|
|Cheating on Taxes is Justified (dummy)|<br>In the World Value Surveys, respondents were<br>asked the following question: “Do you think<br>cheating on taxes is justified?” Answers were<br>recorded on 1 (Never justified) to 10 (Always<br>justified) scale with higher values implying a<br>higher justification for cheating on taxes. We<br>first compute country-level average of the<br>responses for each year for which data are|

42
with the survey evidence presented above (i.e. that insufficient funds are the most common explanation cited by households for non-purchase of insurance).

# 8. Does Insurance Provision Affect Behavior?

Despite tremendous increases in global agricultural productivity brought about by the Green Revolution, traditional farming practices still predominate in many parts of India and in other developing countries. This holds despite high expected rates of return from switching to more productive technologies such as higher-yielding seeds and fertilizer (see Duflo, Kremer, and Robinson [2008] and Suri [2011] for evidence from sub-Saharan Africa).

Credit constraints and limited access to information are often proposed as explanations for low investment and technology adoption in the developing world (Feder, Just, and Zilberman 1985). An additional explanation may be that low agricultural investment is a constrainedoptimal response to the riskiness of these investments. Although key farm inputs increase average agricultural profitability, there is significant variation in their return on investment. For example, the application of fertilizer in semiarid areas in India relies on sufficient rainfall for it to be effective. Thus, the return on fertilizer investments is very dependent on weather conditions, which are beyond the household’s control. Consequently, risk-averse households may be unwilling to bear consumption fluctuations associated with these investments and may decide not to adopt them or instead to shift toward lower-risk, lower-return alternatives.

Dercon and Christiaensen (2007) provide evidence of this hypothesis using panel data on rural households from Ethiopia. Fertilizer purchases are lower among poorer households due to both liquidity constraints and their inability to cope ex post facto with adverse shocks. Thus, lack of insurance leads to underinvestment in fertilizer (see also Lamb [2003] and Horowitz and Lichtenberg [1993]).
version of specification (1) where we replace the district fixed effects with the following district-level controls from the 2011 Census of India: population density, share of population that lives in urban areas, share of population that belongs to a Scheduled caste, share of population that is female, share of population that lives in slums, and total district population. We also control for the all-cause mortality rate in 2019 at the district level from SHRUG and fixed effects for zones (i.e., groups of districts) within Haryana. Robust standard errors clustered by ITI district are in brackets. Variable definitions are in the online Appendix.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

Additionally, Table 6 shows that our results are robust to conducting inference using wild-clustered bootstrapped standard errors.

# **5 Conclusion**

Young job-seekers often have incorrect beliefs about the labor market and unrealistic wage aspirations that can prevent them from making optimal labor market decisions. Our study suggests that exposure to the pandemic lowered the wage aspirations of affected young women, especially for rural women, and made them more realistic without affecting their high willingness to work. However, this “correction” may also have a negative consequence for women’s agency related to work decisions—this is because the decrease in their willingness to migrate is likely to decrease their expected income (and hence their agency) given that migration to urban areas is an important pathway to higher incomes for many rural households. Indeed, we find some suggestive evidence that COVID-19 reduced young women’s ability to make independent decisions about whether they want to work and what type of work they want to do after graduation, especially in rural areas. Future research should evaluate whether the negative effect of COVID-19 on women’
_Construction of the Outcome Variables._ I use two different measures of household welfare: consumption expenditure per capita and wealth. Consumption expenditure has three components: food, education, and other non-food. The ESS has information on each household’s consumption expenditure on 25 commonly consumed food items in Ethiopia,<sup>13</sup> for a one-week recall period, which is annualized by multiplying the aggregate value by 52.<sup>14</sup> It further has annual household education expenditure, which is household expenditure on school fees, uniforms, books, stationary, and so on, and the value of scholarships and assistance received by household members from the government or other organizations. The ESS further has household expenditure on 11 basic household goods (batteries, charcoal, matches, and so forth) for a one-month period and 12 other goods (furniture, clothing, kitchen equipment, and so forth) for a 12-month period. Expenditure on basic goods is annualized and together with expenditure on other goods it yields annual household other non-food consumption expenditure. These components are converted into per capita terms and added together to construct annual household consumption expenditure per capita.<sup>15</sup> Region-specific spatial and countrywide temporal price indices from the CSA and World Development Indicators, respectively, are applied to convert all the values into 2016 prices.

The ESS has rich information on each household’s housing conditions, ownership of a wide array of assets, and access to other indicators of wealth. I use this to construct round-specific composite wealth score for each household using principal component analysis, following <mark>Rutstein (2015).</mark><sup>16</sup> In the interest of retaining spatial and temporal comparability of the composite wealth score, I constructed it at once using the entire household-round observations. In particular, in constructing the wealth score, I include whether a household lives in its own house, and a household’s number of members per number of sleeping