rafmacalaba/datause-encoder-data huggingface dataset:
# **2.  Incentives for and Constraints on Defensive Lending by IDA**

To see why IDA _might_ face incentives to engage in defensive lending, some further institutional details are required. IDA is set up as a rotating fund, in which new loans are financed by a combination of repayments of old loans (referred to as "reflows") and donor contributions. Donor contributions primarily take the form of regular triennial "replenishments" of IDA resources. The amounts of these contributions are negotiated and agreed upon by the donors following a review of the performance of past IDA lending. Because IDA loans are highly concessional with very long maturities (standard IDA terms involve a grace period of 10 years followed by a 30 year repayment period), donor contributions have accounted for the bulk of IDA's financing since its creation in 1960. For example, between 2000 and 2006, donor contributions to IDA totalled $36.5 billion, while repayments of IDA loans totalled just $8.1 billion.<sup>5</sup> The administration and design of IDA lending is entrusted to the staff and management of the World Bank, and this work is financed by service charges that are proportional to the value of IDA credits extended. Between 2001 and 2006, these service charges averaged $795 million and were roughly equal to the annual administrative expenses of IDA, which averaged $778 million over the same period.<sup>6</sup> These service charges account for a very significant share of the operating budget of the combined operations of IDA and the non-concessional arm of the World Bank, the International Bank for Reconstruction and Development (IBRD). In particular, on average over the same period, service charges on IDA lending accounted for 46 percent of the combined administrative expenses of IDA and IBRD. Thus, the World Bank staff who administer IDA resources have strong incentives to ensure the continuation of regular donor contributions to IDA, as well as regular reflows which can be re-lent.

The institutional dependence of the World Bank on IDA lending _potentially_ creates incentives for defensive lending. First, the willingness of donors to provide fresh resources during each IDA replenishment is likely to depend on their perception that

> 5 IDA Annual Report, Special Purpose Financial Statements, Statement of Cash Flows (various issues).  Donor contributions refer to direct contributions, as well as indirect contributions through the HIPC Trust Fund and from IBRD.

> 6 IDA Annual Report, Special Purpose Financial Statements, Income Statement (various issues). Service charges refers to both service and commitment charges.

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We find no evidence of an “age gap” in perceptions of climate change. Older respondents seem to be as (or even more) concerned about climate change as younger respondents. However, we do find that older individuals are more likely to object to tax increases to finance climate adaptation and mitigation policies, which is likely to reflect the general decline in willingness to pay taxes with age rather than a specific lack of willingness to finance climate change policies.<sup>3</sup> Past exposure to natural disasters, a person’s physical and psychological health, the degree of risk aversion, sources of information, trust in institutions, and whether a respondent was planning to move away also affect perceptions and willingness to pay for climate change, but none of these confounding factors reverse the climate change − age gradient estimated by the most parsimonious models. The positive age gradient is also robust to a range of specifications, definitions of the dependent variable, and corrections of potential omitted variable biases.

This paper contributes to the literature on the determinants of individual perceptions and engagements in climate change actions in several ways. First, it develops a theoretical model that shows that the relationship between age and concerns about climate change and the willingness to pay for climate change actions is ambiguous and depends on various factors. Second, the paper distinguishes between climate-related beliefs and actions toward climate change mitigation, an important distinction, as one can believe that climate change is real and manmade but still not be willing to bear an additional tax burden to mitigate its impacts. Third, the LiTS data allow us to account for the wide range of determinants of climate beliefs that earlier studies have identified as varying across different age groups, including ideology, attitudes toward risk and fairness, trust in government, primary sources of information, and health conditions which can exacerbate climate morbidity.

The paper is organized as follows. Section 2 reviews relevant literature on generational differences in beliefs about climate change. Section 3 discusses the study’s theoretical framework and empirical methodology. Section 4 describes the data. Section 5 presents the main results of the empirical analysis. Section 6 shows the result of robustness tests. Section 7 summarizes the paper’s main findings.

> 3 In April 2024, the European Court of Human Rights issued a ruling in favor of a group of 2,400 Swiss women over age 64 who argued that because older women are more likely to die in heatwaves, Switzerland must take greater action to prevent the planet heating beyond the Paris agreement target of 1.5°C and demand compensation for the lost consumption from the government, rather than paying taxes to finance climate change mitigation policies (Daly 2024).

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general equilibrium model with a well-defined global database to map the interactions of diseases with multiple channels of impact, covering not only the deterioration of human health and mortality, but also the effects on labor productivity, health care costs, productivity in the livestock sector, and global food trade, which is restricted by fear. We simulate the economy-wide repercussions within economies as well as their cost and spread over time and across countries through trade and resource flows. We also trace the global effects on poverty, especially in developing countries, by employing a microsimulation framework that draws on household survey data with global coverage.

Relative to a world without AMR, the losses during the period 2015-2050 may add up to 85 trillion dollars in GDP and 23 trillion dollars in global trade (in present value). By 2050, the cost in global GDP could range from 1% (low case) to 3.8% (high case). Because of AMR, the target of eliminating extreme poverty would be harder to reach: there could be an additional 24.1 million extremely poor people by 2030 in the high-AMR scenario, of whom 18.7 would be in lowincome countries. In general, developing countries will be hurt the most, particularly those with the lowest incomes.

Our analysis shows that the GDP loss due to AMR is most severe for low-income countries. In the high-case scenario, the real GDP of the low-income region in 2050 could fall 5.6% below the baseline; similarly, 4.4% for the two middle-income regions and 3.1% for the high-income region. For the world as a whole, the corresponding decline in GDP is 3.8% or 6.1 trillion dollars.

With regard to trade, AMR will also lead to a severe decline for low-income countries. Compared to a world without AMR, for the high-case scenario, the projected declines in real exports by 2050 equal 5.2% for low-income countries, about 4.2% for middle-income countries, and 3.1% for high-income countries. The decline in global trade amounts to 3.7% or 1.7 trillion dollars.

Likewise, health expenditures will rise most dramatically in developing countries. For the high case, by 2050, the extra health spending induced by AMR over the baseline will come to about 25.3% for low-income countries, 16.0% for the lower middle-income countries, 14.7% for the upper middle-income countries, and 6.2% for the high-income countries. Although the extra spending measured in dollars is higher the higher the income of the region, the added expenditures would likely be much more regressive and impoverishing in lower income settings where the additional expenditures constitute a larger chunk of the household budget and likely out-of-pocket because of the lack of universal health coverage/financial protection.

For the livestock sectors, trade reductions from a “fear factor” will further reduce production, also here most strongly for low-income countries. For our high case, the decline in livestock production by 2050 is 11.1% for low-income countries; 7% - 9% for middle-income countries, and about 6% for high-income countries.

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pattern holds for aid in all sectors (health, transport, etc.). If differences are detected across sectors, this might give some hints as to the underlying mechanisms. Irrespective of the sector considered, coefficients on the mission dummies are always positive, statistically significant at conventional levels, and large relative to the means of the dependent variables (see estimates presented in the appendix); the magnitudes are highly comparable across sectors.

A further test checks if the use of binary treatment and outcome variables is important for the results. When using the number of missions and the log number of missions plus 1 as treatment variables, results are very similar to the baseline. Another investigation is of whether the relationship between aid and missions also holds at the intensive margin; this is done by replacing the dependent variable with the number of World Bank aid projects. Estimates from OLS, Poisson, and negative binomial regressions confirm the baseline results. Finally, the correlation between aid and missions also survives the inclusion of subnational fixed effects at the ADM1 (states, governorates) or ADM2 (districts, municipalities, communes) level.

Taking stock, this section has documented a robust and sizable spatial relationship between World Bank aid and the historical presence of mission stations. Although it is hard to claim causality with observational data, the estimated relationship survives a vast array of robustness tests, including the most recent procedure suggested in the econometric literature for assessing the bias from unobservable factors (Oster, 2019).

# **3 Implications for Aid Effectiveness**

Having established that mission areas attract a disproportionate share of World Bank aid, it is natural to ask whether this has implications for aid effectiveness. Answering this question is interesting both from a policy perspective and in terms of gaining a better understanding of the mechanism at play. For example, it is possible that missionary interventions paved the ground for aid interventions later on, by providing suitable conditions for effective project implementation. These conditions might include cooperative behavior, trust in foreigners, and specific skills (such as language), all factors that previous research has found to be positively affected by missionary activity. The goal of this section is to test whether aid projects implemented parisdeclarationandaccraagendaforaction.htm.

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Appendix Table 11 Probability of Firms Using Credit Program: Heckman Correction

|||Heckman C<br>1st Stage|orrection<br>2nd Stage|
|---|---|---|---|
||(1)|(2)|(3)|
||Approvals|Applications|Approvals|
|_Panel A: Analysis using Predicted Default Risk_||||
|Risk|-0.264|0.547|-0.123|
||(0.022)|(0.035)|(0.066)|
|Increase in Sales Dummy|0.014<br>|0.187<br>|0.072<br>|
||(0.006)|(0.008)|(0.027)|
|Decrease in Sales Dummy|0.014<br>|0.190<br>|0.073<br>|
||(0.006)|(0.007)|(0.027)|
|Use Employment Program|-0.009|0.117|0.021|
||(0.004)|(0.005)|(0.014)|
|Inverse Mills Ratio|||0.186|
||||(0.083)|
|Dependent Variable Mean|0.913|0.656|0.913|
|Dependent Variable Std. Dev.|0.281|0.475|0.281|
|Number of Firms<br>|36,609|62,859|36,593|
|R<sup>2</sup>|0.030|0.063|0.030|
|Industry FE|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|
|_Predicted Default Probability_||||
|Firms with Previous Loans|0.090|0.084|0.090|
|_Panel B: Analysis Using Spread_||||
|Spread _Ex Ante_|-0.002|0.002|-0.002|
||(0.000)|(0.001)|(0.000)|
|Increase in Sales Dummy|0.018|0.176|0.051|
||(0.008)|(0.009)|(0.033)|
|Decrease in Sales Dummy|0.014|0.183|0.049|
||(0.008)|(0.008)|(0.033)|
|Use Employment Program|-0.009<br>(0.004)|0.103|0.006|
|Inverse Mills Ratio|||0.108<br>(0.101)|
|Dependent Variable Mean|0.910|0.729|0.911|
|Dependent Variable Std. Dev.|0.286|0.445|0.285|
|Number of Firms|24,514|38,250|24,444|
|R<sup>2</sup>|0.032|0.068|0.032|
|Industry FE|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|
|_Predicted Default Probability_||||
|Firms with Previous Loans|0.107|0.107|0.107|

This table reports probit estimations of the probability of a firm with previous loan using a government program on a set of firm-level characteristics, and implementing a two-step Heckman selection correction in the approval estimation. Panel A defines risk using the predicted default probability from the default probability model reported in Table 1, Column 8; Panel B defines risk using the spread between the interest rate of the loans a firm received and the risk-free rate. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. In Column 1, the dependent variable is equal to one if the firm’s loan application is approved and zero otherwise, in Column 2, the dependent variable is equal to one if the firm applies to the program and zero otherwise in Column 3 the dependent variable is equal to one if the firm’s loan application is approved and zero otherwise. Column 2 is the first-stage, and Column 3 is the second-stage of two-step Heckman selection correction. Increase (decrease) in sales dummy is equal to one if sales growth is greater (lower) than or equal to 2% (-2%), and zero if sales growth is between -2% and 2%. Use employment program is a dummy variable equal to one for program participation and zero otherwise. The table uses the firms with previous loans of the credit program eligible firms sample. Some observations are dropped when estimating the model with fixed effects. Standard errors, shown in parentheses, are clustered at the industry and municipality level.

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A brief word on the meaning of specific language categories. National language refers to an indigenous language spoken by over fifty percent of the population that is also an official language. Local language refers to a non-official local language spoken by a minority of a country's population. Official language refers to a metropolitan or world language that is an official language but one which is not indigenous in origin. Fmreiga language refers to a language that is neither official nor indigenous.

We analyse the subject matter of national curricula according to four basic classifications: (1) By world region. We use the World Bank classification dividing less-developed countries into four geographic areas: Latin American and the Caribbean (LAC); Asia; Sub-Saharan Africa (SSA); and Southern Europe, the Middle East and North Africa, (EMENA).

(2) By per capita income level. Estimates of per capita GNP in 1984 (World Bank, 1986) are used to divide countries into six categories. The first four categories refer to developing countries; the last two categories to developed countries: Low-income (per capita GNP below $400); Lower middle-income (per capita GNP $401-$1700); Upper middle-income (per capita GNP $1701 - $7300); High income oil exporters (per capita GNP above $7300); Market Economy; and Non-Market Economy.

(3) By gross primary enrollment rate. UNESCO (1987) figures on gross primary enrollment ratios (GPER) in 1984 are used to divide less-developed countries into those with a GPER of more or less than 80 percent.

(4) By predicted GNP growth rate and gross primary enrollment rate. This is a two dimensional typology based on economic growth potential (positive or negative) and gross primary enrollment level (high or low).
and non-exporters, and in terms of innovation inputs and outputs, outward orientation, and the (joint) decision to export and innovate. In particular, we examine if the exporter premiums are different between these sectors, using the same methodology as in Bernard and Jensen (1999). Analyzing the exporter premium provides a simple way of actually comparing groups of firms within a sector, as well as of comparing sectors.

# **(a) Data description**

We analyze the fifth round of the Technological Innovation Survey carried out in Chile in 2007.<sup>13</sup> This survey covers the period 2005-2006.<sup>14</sup> The survey analyzes the manufacturing sector, service sector and primary sector, classified according to ISIC rev 3, at two digits of aggregation. We focus on the first two in this paper. This survey provides data on 2933 plants (51% from manufacturing) for these two sectors. Weights are available for each plant so for most of our analysis we will use weighted descriptive statistics and regressions, given that we want to conduct a sector-wide comparative analysis.<sup>15</sup> The survey provides information on objective measures of innovation, such as expenditure in non R&D innovation, R&D expenses, training, salaries of personnel involved in innovation activities, and percentage of sales from innovative products. It also provides subjective measures, such as if the firm has introduced new products, processes or services in the market, the degree of innovativeness of the new product or service, the existence and extent of collaboration and sources of information for innovation, etc. Additionally, it provides information on the firm’s export status.

Since we want to compare exporters and non-exporters in both sectors, to narrow our analysis to services sectors where analyzing export behaviors is meaningful, we divide the services sector into ‘tradable’ and ‘non tradable’ services. The tradable services sector includes categories I (transport, storage and communications), J (financial intermediation), K(72) and K(73) (business services). To define 12
# 3. Vulnerability to HIV/AIDS

Migrants are more vulnerable than non‐migrants to HIV infection, partly because they are more likely to engage in multiple and concurrent partnerships (MCPs). Qualitative work suggests that female migrants are seen as particularly vulnerable: “Yes I do believe that [female migrants] are at risk of HIV. This is because sometimes these boys cheat us and take out condoms whilst we are not aware during sexual intercourse and that is how HIV is transmitted”; “I believe this to be true and that women living and working away from their family are in danger because sometimes women get into romantic relationships just because we cannot afford the things we desire or need”; “Women [are more at risk] because more often, men trick them into getting in bed with them and they are mostly abused sexually.” _Figure 4: Age Profile of HIV Prevalence Across Time_ <!-- Start of picture text --> Men<br>50<br>40<br>30<br>20<br>10<br>0<br>15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49<br>2014 2009 2004<br>Women<br>50<br>40<br>30<br>20<br>10<br>0<br>15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49<br>2014 2009 2004<br>HIV Prevalence (%)<br>HIV Prevalence (%)<br><!-- End of picture text --> 41
# **5.2 Performance Premium of Global Retail Chains**

where _yit_ is the outcome variable for retailer _i_ operating at time _t_ capturing the retailer’s performance. The performance indicators include employment, capital stock, capital-labor ratio, total sales, market share, sales per worker, real wage per worker, value added per worker (value added is defined as the difference between sales and material costs), return to assets (computed as the ratio of profits to total assets), and return to sales (calculated as the ratio between profits and total sales).<sup>9</sup> Except for market share, return to assets and return to sales, all variables enter in a logarithmic form. We define _global_chainit_ as a dummy taking on the value of one if the retailer _i_ is one of the 9 identified global retail chains and zero otherwise. The estimate of α 1 is, therefore, the premium associated with global retail chains. We control for the logarithm of _ageit,_ defined as the number of years since establishment to capture the learning-by-doing effects. To control for size differences between different retailers we include one period lag of employment (also in logarithmic form). To control for regional differences in economic conditions, we include region fixed effects _αr_ . We also include year fixed effects, _αt_ , to take into account macroeconomic shocks, such as for instance, the 1998-1999 Russian financial crisis. As it does not seem meaningful to compare global retail chains to one-person kiosks or family-run street vendors, we limit the sample to retailers and wholesale traders with an average employment over 50, which leaves us with the top 1 percent of all the observation or 932 firms (see Table 5 for summary statistics).

We present the estimated premium associated with being a global retail chain for ten performance indicators in Table 6. We find that global retail chains differ significantly from other retailers in Romania. The estimated premium for the eight indicators is positive and statistically significant at the 5% level or better. As for the scale, foreign chains are much larger in terms of employment, capital stock and sales. They are more capital intensive (as measured by capital-

> 9 Note that we use company-level data, as outlet-level information is not available for the variables of interest.

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When the “unpacking” of the ethnicity variable begins—when one asks questions such as “in what way does ethnic fragmentation manifest itself, and makes policy-making or coalition-formation different from what it would have been in ethnically homogeneous societies” and tests for this directly—we would have made more progress in the understanding of the problem. Such understanding is more easily obtainable from individual case studies. Unfortunately, it is very difficult to move from case studies, each of which is often a _sui generis_ case,<sup>51</sup> to a generalization that can be measured, and used as a variable in an empirical study.

A useful approach for future research would be to try to “unpack” ethnicity, in order words to try to find out what exactly the variable represents and through what channels it affects inequality, growth and other variables which it has found to be associated with.

> 51 A very useful case study is the one by Nkurunziza and Ngaruko (2002) that assesses the role of ethnic conflict in destroying basis for any growth in Burundi. But again, however, useful for Burundi, it is difficult to see how one could move from that one study to a more generalized approach.

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