rafmacalaba/data-use-mentions-extended huggingface dataset:
32 World Health Organization. 2010. _World Malaria Report 2010_ . Geneva: World Health Organization.

# **_References for infant and under five mortality figures shown in Table 1_**

Agence Nationale de la Statistique et de la Démographie (ANSD) [Sénégal], et ICF International. 2012. Enquête Démographique et de Santé à Indicateurs Multiples au Sénégal (EDS-MICS) 2010-2011. Calverton, Maryland, USA: ANSD et ICF International.

Central Statistical Agency [Ethiopia] and ICF International. 2012. Ethiopia Demographic and Health Survey 2011. Addis Ababa, Ethiopia and Calverton, Maryland, USA: Central Statistical Agency and ICF International.

ICF International. Statcompiler website. http://www.statcompiler.com. Accessed April 30, 2012. Instituto Nacional de Estatística and ICF Macro. 2012. Moçambique Inquérito Demográfico e de Saúde 2011. Calverton, Maryland: Instituto Nacional de Estatística, Ministério da Saúde, and ICF Macro.

Ministry of Health and Social Welfare (MOHSW) [Lesotho] and ICF Macro. 2010. Lesotho Demographic and Health Survey 2009. Maseru, Lesotho: MOHSW and ICF Macro. National Institute of Statistics of Rwanda (NISR) [Rwanda], Ministry of Health (MOH) [Rwanda], and ICF International. 2012. Rwanda Demographic and Health Survey 2010. Calverton, Maryland, USA: NISR, MOH, and ICF International.

Uganda Bureau of Statistics (UBOS) and ICF Macro. 2012. Uganda Demographic and Health Survey 2011 Preliminary Report. Kampala, Uganda: UBOS and ICF Macro.

Zimbabwe National Statistics Agency (ZIMSTAT) and ICF International. 2012. Zimbabwe Demographic and Health Survey 2010-11. Calverton, Maryland: ZIMSTAT and ICF International Inc.
earnings of those who went to school many years ago. But even for experimental studies such as Duflo, Dupas, and Kremer, any eventual estimates of the impact of additional years of schooling on future labor earnings will be for students who went to school circa 2012. These may not be externally valid for students in school at the time when the researchers eventually gather data on labor earnings for the past students in the study sample.

Overall, we find that public spending toward the quantity and quality<sup>5</sup> of schooling offers good value for money: All our benefit-cost ratios exceed one. But we also find that all our benefit-cost ratios are modest when compared to some of the more spectacular ratios found in a typical Copenhagen Consensus study like the Ghana Priorities project. However, most of the

> 5 This being limited to quality improvements we can study carefully: remedial education and school feeding.

3
# **PASS-THROUGH OF COMPETITORS’ EXCHANGE RATES TO US IMPORT AND PRODUCER PRICES***

STEVEN PENNINGS

# 1. Introduction

Standard empirical and theoretical models focus on the bilateral exchange rate (between exporting and importing countries) as the key international macroeconomic variable determining prices. However, since the 1980s (e.g. Dornbusch 1987) it has been appreciated that competitors’ prices will be an important determinant of international prices in models where firms price strategically. In this paper, I extend the literature to show that in a multi-country (>2) world, this general class of model (where markups depend on other firms’ prices) implies that US import prices and producer prices will respond to the exchange rates of the producer’s foreign competitors (Section 2). Using US microdata at the product level, I show that pass-through of competitors’ exchange rates to import prices is large — often larger than pass-through of the bilateral exchange rate — and that US producer prices also respond significantly to competitors’ exchange rates (Section 3). Consistent with this mechanism, pass-through of foreign competitors’ prices is sizable. I develop a multi-country extension of the model of Atkeson and Burstein (2008) (henceforth AB), which is able to explain a large share of pass-through of competitors’ exchange rates when regressions are run on simulated data (Sections 4 and 5). While some of these facts for import prices have been mentioned briefly elsewhere (e.g. Gopinath and Itskhoki 2011, henceforth GI2011), this paper is among the first to analyze them systematically.

**An example** To fix ideas, consider four car producers from the US, Europe, Japan and Canada competing in the US market. Standard bilateral exchange rate pass-through suggests that a real appreciation of the yen against the USD will raise the real price of Japanese cars imported into the US. However, models of strategic pricing also predict that the Japanese car producer will respond to the prices — and hence exchange rates — of its
Figure 9: Projected diarrhea incidence in children under two climate scenarios (SSP1.19 and SSP3.70) assuming constant toilet ownership at the 2014 level <!-- Start of picture text --> 23.0<br>22.0<br>21.0<br>20.0<br>19.0<br>18.0<br>17.0<br>16.0<br>2015 2020 2025 2030 2035 2040<br>Year<br>(a) SSP1.19<br>23.0<br>22.0<br>21.0<br>20.0<br>19.0<br>18.0<br>17.0<br>16.0<br>2015 2020 2025 2030 2035 2040<br>Year<br>(b) SSP3.70<br>Projected diarrhea incidence (%)<br>Projected diarrhea incidence (%)<br><!-- End of picture text --> Note: Each small transparent grey line represents the projection obtained using the output from a given climate model. The ensemble allows us to measure climate uncertainty stemming from the limitations of climate models. The black line is the ensemble median, and the dashed grey lines represent the 10-90% confidence interval.

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We consider now the implication of the escaping-poverty property when the value of median income _<u>y</u>_ varies. The key observation is that self-centered individuals escape welfare poverty when their income surpasses the subsistence income, even if median income is simultaneously increased. For instance, in Figure 2, the self-centered individual 1 is absolutely income poor in distribution **y** , and thus welfare poor. In contrast, individual 1 is only-relatively income poor in distribution **y**<sup>_′′_</sup> , and thus not welfare poor. Hence, the escaping-poverty property is satisfied only if any only-relatively income poor individual is attributed a strictly smaller poverty score than any absolutely income poor individual, even if they live in societies with different values of median income. The view that only-relatively income poor individuals should always be attributed a smaller poverty score than absolutely income poor individuals seems to be largely shared, as survey evidence suggests (Decerf and Ferrando, 2022). We have thus shown that the escaping-poverty property provides a preference-based foundation for this view.

This second implication of the escaping-poverty property is violated by the societal headcount ratio but satisfied by the hierarchical headcount ratio. Index _HS_ violates it because _HS_ attributes a poverty score equal to one to all income poor individuals. Index _HHS_ satisfies it because _HHS_ attributes a poverty score equal to one to absolutely income poor individuals and a smaller poverty score to only-relatively income poor individuals.

We conclude this section with three remarks. First, index _HHS_ need not always satisfy the escaping-poverty property. Indeed, _HHS_ violates that property if some absolutely income poor individuals are not welfare poor.<sup>16</sup> Thus, even if index _HHS_ comes closer than index _HS_ to satisfying the escaping-poverty property, it does not fully do so.<sup>17</sup> Unfortunately, this is the price to pay for considering additive indices that do not treat individuals differently based on their sensitivity to relative income. No
A4 reproduces the main results of our DiD analysis with the merged sample.

Table 5 establishes a causal relationship between pollution and infant mortality. Column 1 shows the OLS relationship between BOD pollution (i.e., exceedance of the bathing-class standard) and neonatal mortality. It is a statistical zero, which may reflect the upward bias that would result from positive correlations between pollution, urbanization, and economic activity. Columns 2-5 replace the key independent variable with its upstream counterpart – the reduced-form analog of 2SLS with upstream BOD as the instrument. The specifications in these columns vary only by either sample time period or the definition of upstream. In stark contrast to the OLS result, the coefficient on upstream water quality is positive and significant across all four specifications. The interpretation of these coefficients is that exceeding the BOD standard for bathing-class water quality upstream increases the downstream likelihood of neonatal mortality by 2-3 percentage points – a significant spatial externality.

Our empirical strategy hinges on the assumption that upstream river pollution is exogenous once we condition on weather, policy, and district and year-month fixed effects. Implicit in this assumption is that these controls fully capture the endogenous location of households. To bolster this argument, we employ a placebo test that provides additional evidence supporting our exclusion restriction. In columns 6 and 7, each upstream district is replaced with the nearest off-river neighbor with nonmissing pollution data. If the resulting ‘falsified’ upstream pollution variable remains a significant predictor of downstream mortality, then we are likely capturing spurious, spatial correlation between a given district and its neighbors due to the regional nature of many health and environmental shocks. This, however, is not the case: the falsified upstream pollution variable loses both its magnitude and significance, thereby suggesting that our true upstream variable is isolating quasi-random variation in pollution that originates upstream and yet flows downstream to other districts.

The next step is to estimate the dose-response function and use it to test the importance of the pollution mechanism. The results of this step are documented in Table 6, which details
viewpoint of poverty reduction.

Of course, the advantages of the parametric approach matter only if the chosen parameterization fits the data well. We find that the null hypothesis of lognormality cannot be rejected when applied to the distribution of per capita income, regardless of whether income is measured in gross terms (i.e. before taxes and transfers) or net terms (after taxes and transfers). However, the same null hypothesis is unambiguously rejected when applied to per capita consumption data. We conjecture that this rejection may be due to consumption smoothing, under which the log of consumption may not be normally distributed even if the log of income is.

The paper derives some implications of this result for the relative roles of growth and inequality in poverty reduction under alternative initial conditions. We highlight four main points: (i) inequality hampers poverty reduction, both because of its negative impact on the growth elasticity of poverty (as stressed in the literature) but, in most scenarios, also because of its negative impact on the inequality elasticity of poverty; (ii) for a given poverty line, the impact of growth on poverty is stronger in richer than in poorer countries, and hence the latter will find it harder than the former to achieve fast poverty reduction; (iii) the share of the variance of poverty changes attributable to growth should be generally lower in richer and more unequal countries; and (iv) given the initial levels of development and inequality, the relative poverty-reduction effectiveness of growth and inequality changes depends on the poverty line -- the higher the poverty line, the bigger the role of growth and the smaller the role of distributional change.

-2-
# 6.2. Early signs of an uneven jobs recovery

A key area of concern about the impacts of a shock is that economic recovery may occur more slowly among those who were most affected by it, thus exacerbating inequality over a longer term (as illustrated by the conceptual framework in Figure 4). This appears to be the case globally (see section 2) as recovery in 2021 appears to be concentrated in the upper part of the global income distribution, while the lower end of the distribution who suffered the greatest losses in 2020 fall further behind (Figure 1). In 2021, the top 60 percent are projected to return to pre-COVID welfare levels, while the bottom 40 percent will have incomes 2 percent smaller than that before the pandemic. In these distribution-neutral simulations, the widening gap during recovery is the consequence of GDP growth in low-income countries lagging growth rates in advanced economies in 2021.<sup>27</sup> What can we discern about the pattern of early recovery _within_ countries? Analysis of harmonized HFPS data from surveys conducted since July 2020, in those developing economies where policy restrictions became less stringent, allows an early look at the pattern of recovery across different groups in the population to provide some indication of what is likely to happen when economies rebound. To our knowledge, this is the only database that allows analysis of changes over time in the pandemic period in a consistent manner across many developing countries.

We study the recovery between May-June 2020 (Round 1) and August-September 2020 (Round 2) — periods chosen in a way that maximizes the number of countries in our HFPS sample with at least one wave in each round, while ensuring that the two surveys of each country are roughly equidistant. Around 40 percent of the countries (23 out of 59) with harmonized HFPS data have survey rounds conducted within the Round 1 and Round 2 windows.<sup>28</sup> To focus on the patterns associated with economic recovery, we restrict our attention to 20 countries
large increase in investment by treated firms in the post-treatment period (consistent with the later results in Table 3 below). This effect is apparent in the year in which the marginal incentive for the firm changes, consistent with the ambitions of the AIA policy, and continues in the five years post-treatment. Our event study plots are robust to alternative similar definitions of firms’ treatment status based on their historic investment.<sup>30</sup> For example, Figure A1 uses 2-year average investment data for the years 2006/07 while Figure A2 uses 3-year averages for the years 2005/06/07.

As a further check on the parallel trends assumption, we similarly examine the pretreatment trends for the wages of data analytics workers (Figure A3), exploiting that these are measured on an annual basis. In the periods before the AIA changes we do not observe a differential trend in wages of data analytics workers between the treated and control firms (although the worker-level estimation is somewhat more noisy than the preceding firm-level plots). Around two years after treatment we observe a decline in the wages at treated firms, mirroring later results in Table 6. These results are consistent with the existing literature which finds both that technology adoption is linked with subsequent labor demand (Bresnahan et al., 2002) but that this organizational change occurs overtime (Juh ́asz et al., 2020).

# **5 Results**

## **5.1 The effects of AIA on cloud and capital investment, 2007-13**

This section examines econometrically the treatment effect of changes in the AIA allowance on firm investment in IT capital and cloud adoption. The AIA was designed and implemented by policy makers with the objective to increase physical capital investment, including the stock of digital technologies through investments in traditional tangible IT capital. We find evidence that the policy was successful in this regard. The results in Table 3 find that firms

> 30The absence of pre-trends is also apparent if we estimate these regressions over the period
# **1. Introduction**

Nepal’s first comprehensive poverty assessment was published in 1991. The report – based on the Multi-Purpose Household Budget Survey conducted in 1984/85 – depicted dire living conditions in Nepal more than 30 years ago. Using what was considered then a very conservative poverty line, at least 40 percent of the population was deemed absolutely poor.<sup>2</sup> Except for a very few professionals and business persons, however, everyone in Nepal then would have been regarded poor by any reasonable international standard (World Bank 1991). Nepal was one of the poorest countries in the world at that time.

The report attributed Nepal’s poverty to four prominent factors. First, Nepal is landlocked, and borders on China and India, the two most populous countries in the world which were also two of the poorest countries at the time of that assessment. Second, Nepal has limited resource base and arable land. While known for its stunning natural beauty, Nepal’s mountainous and rugged terrain posed a significant challenge for rural development. Third, Nepal had a high population growth rate; the population had almost doubled between 1960 and 1990 (World Bank 2016b) and was projected to double again between 1990 and 2015 (World Bank 1991). Fourth, Nepal’s overall economic growth experience to date had been sluggish at best, presumably as a result of the aforementioned factors.

Poverty in Nepal was predominantly rural. Rural areas accounted for 93 percent of the population and 95 percent of the poor in 1984/85 (World Bank 1991; World Bank 2016b). The share of labor force dependent on agriculture was 93 percent, the highest of any country listed in the 1989 World Development Report (World Bank 1989). The overwhelming majority of poor Nepalis made their living by subsistence farming. As of 1984/85, most poor households barely met the minimum caloric requirement<sup>3</sup> by depending heavily on rice and dal – almost
right, suggesting it remains a potential concern.<sup>37</sup> The use of anchoring vignettes may provide a possible promising avenue to address different types of response patterns (Hopkins and King, 2010; Primi et al, 2016) and more research is needed to test this approach in large rural household surveys.

Finally, “social desirability bias” may lead a respondent to answer what he believes would give the best impression, or what the enumerator may want to hear. In surveys related to impact evaluations, respondents may also believe their answers could affect the probability to receive benefits and attempt to answer strategically. Such biases are difficult to avoid when using selfreports rather than observed outcomes and the extent to which they affect the measures and generate a non-random noise remains difficult to assess. While this holds for many outcomes other than skills, in the case of skills such response patterns provide an additional challenge for the interpretation of the findings, as the way of answering questions may be related to personality itself. The use of forced choice questions or situational judgment tests (Lipnevich, MacCann and Roberts, 2013; Kyllonen, 2016) may provide a potential answer to these concerns, but the extent to which they improve validity or reliability in large rural household surveys is an open question.

# **4.5 Anchoring and the use of other sources of information**

The previous sub-section raises the possibility of different response biases affecting the answers. Another important response pattern comes from the fact that each respondent may interpret the Likert Scale differently and use thresholds to decide between answer categories. Anchoring vignettes possibly can help to address this challenge too. Another way of breaking the relationship between answer patterns and skills is to ask another person about the skills of the person of interest, not unlike the use of recommendation letters to evaluate skills in other settings. A priori, the random measurement of a person’s skills should be noisier when asking somebody else, as the other person is likely to have asymmetric information about
annual data for GDP per capita in the purchasing power parity terms (GDP pc in PPP terms) is taken from the World Development Indicator Report 2006, and is reported in constant 2000 US dollars. The data for real exchange rate is also taken from the World Development Indicators database. The trade openness variable (the sum of a country’s exports and imports divided by its GDP) is from the Penn World Tables. The IMF dummy capturing whether a given country was a subject to the IMF program (i.e., received an official IMF loan) in a given year is constructed based on data from the 10
as completed years of education. Reference categories for _Self-employed_ and _Gender_ variables and for computer complexity are those not self-employed, males, and no computer use, respectively. Scores on the reading proficiency assessment and personality and behavior traits have been standardized to a mean of 0 and standard deviation of 1. The wage model controls for gender, self-employed workers, experience and occupation. The Heckman method is used to correct for selection bias.

30
# **1 Introduction**

Average income per capita and the poverty headcount are among the most common measures used to summarize living standards and to assess progress towards national and international development goals. The popularity of these welfare measures is in no small measure due to their simplicity, which makes them easy to understand and to communicate to wide audiences. However, these measures also have a fundamental limitation: they are silent on the extent of inequality among all individuals (in the case of average income) and inequality among the poor (in the case of the headcount).

This limitation does not sit well with the widely held view that inequality matters: specifically, that welfare measures should be more responsive to changes in income that take place at the bottom of the distribution. This view has at least four justifications. First, with decreasing marginal utility of income, the social preferences of a utilitarian policymaker give more weight to those at the bottom of the distribution.<sup>1</sup> Second, most major philosophical traditions and religions express a special concern for the worse-off (World Bank, 2005). Third, the fact that several of the Sustainable Development Goals have distributional elements reveals a preference among international policymakers for lower inequality.<sup>2</sup> Fourth, direct empirical estimates suggest a high degree of inequality aversion, although the variance of estimates is large (Clark and D’Ambrosio 2015, Kot and Paradowski, 2022).

This limitation is well-understood, and a long literature has generated an impressive variety of distribution sensitive welfare measures to address it.<sup>3</sup> However, these measures are rarely used in practice. To illustrate this, Table 1.1 displays the results of a simple keyword search for leading poverty and welfare measures, in three bodies of documents: academic research papers in EconLit; policy documents of major multilateral development banks as curated at nlp4dev.org; and general writing in the Google N-gram Viewer. Across all three categories of