rafmacalaba/data-use-mentions-extended huggingface dataset:
8 terms of monitoring investments and exerting corporate governance after providing finance, Country A scores 90, while Country B scores only 20 on a scale from 0 to 100, and so on.

But, obtaining such direct measures of these financial functions is a major challenge. Levine (2005) points out that such empirical proxy variables often do not accurately measure the concepts emerging from theoretical models.

Thus, the goal of this paper is empirically measure four important characteristics of financial systems around the world for a broad cross-section of countries over a long time period. Specifically, this paper examines the following four characteristics of financial systems: (a) depth; (b) access; (c) efficiency, and (d) stability. The focus here is on measuring financial system characteristics.

These financial system characteristics are proxies of the services provided by the financial system. For example, ―financial depth‖ is not a function in itself, but it is a proxy of the overall extent of services provided by the financial system. Similarly, the measures available for ―access‖ do not directly measure how well the financial system identifies good investments, regardless of the collateral of the individual; but it provides an (imperfect, ex post) approximation of the breadth of use of particular financial institutions and instruments.

For completeness, the accompanying database includes data on broader social welfare indicators as well as the financial sector policy indicators and the other factors that define the enabling environment.

For each of the four characteristics, this paper presents measures for financial institutions (in particular banks, but also other, non-bank financial companies) and for financial markets. Table 1 provides a summary representation of this 4x2 matrix of financial system characteristics, building on a review of empirical literature on financial systems, discussed in the remainder in this section. Principal component analysis was used to double-check the choice of variables and their assignment to the 4x2 categories from the previous research.

The Global Financial Development Database is based on this 4x2 framework. It builds on,
# **1. Introduction**

Inter-personal inequality in living standards within households is a largely uncharted territory. It is unlikely that individuals within the same household always have the same living standards as income and resources are not necessarily pooled, and members do not share in them equally. If this is the case, inequality levels might well be seriously underestimated. Further, this is a question of major policy relevance as unequal access to resources might push vulnerable household members to alarmingly low consumption levels. However, gauging the extent of intra-household inequality remains an empirical challenge, since adequate data are rarely at hand.

The purpose of this paper is to provide a measure and a description of intra-household inequality in the case of Senegal using a novel survey in which household consumption data were collected at a disaggregated level, evaluate how it alters our assessment of consumption distribution in the country and identify which categories of household members are most at risk to suffer from relative deprivation.

The distribution of consumption in West-African economies is often considered to be less unequal than in other parts of Africa and Latin America; the Gini indices are around 40%, while they reach the range 50 to 60% in many Austral African or Latin American countries (see fig 2.9, chap. 2; World Bank 2005). Still, not only is income inequality fairly high (Beegle at al. 2016), but, as pointed out by Cogneau et al (2006), it is accompanied by low intergenerational social mobility, suggesting that inequality is weighing on individual life trajectories.

The above inequality assessments are typically based on standard consumption surveys, which collect information at the level of the household as a whole, often by interviewing its head. Individual consumption levels are derived from this aggregate measure. This might be a poor approximation of reality in the context of Senegal, where households extend far beyond 2
factors comes from Vickery and Skerlj (2005). For more detailed information about the hurricane windstorm model, see Pita et al. (2015).

## **S2 Implied monetary losses**

To convert the benefit of mangrove protection (mitigation of hurricane damage), measured in nightlights, into rough monetary values, we begin by calculating a conversion factor between nightlights and economic activity as measured by Gross Domestic Product (GDP). Using World Bank data on GDP in Parity Purchasing Power (PPP) in 2011 22
import prices and import values for the United States and India, respectively.<sup>2</sup> Bown and Crowley (2006) show that imposition of AD duties also affects the prices of the targeted countries’ products in alternative destinations. Studies examining the pricing effect of AD duties also consider the impact of events (filing, decision, and termination) during the determination period of AD investigations into the pricing decisions of foreign firms (Staiger and Wolak 1994; Krupp and Polard 1996). Moreover, Prusa (1992) focuses on how AD cases might lead to collusion between domestic and foreign firms. We contribute to this literature through the innovative use of four-dimensional customs data (firms, products, destinations, and years) to determine the effects of AD duties and the threat of their enforcement on export prices.

Our retaliation analysis broadly fits into the literature that investigates retaliatory incentives in AD activity. Many existing studies propose a number of political and strategic considerations to explain the proliferation of AD duties even though the ideal AD case aims to hinder unfair competition in the international market. One proposed explanation is that countries tend to retaliate against countries by which they were previously targeted in AD investigations (Miranda et al. 1998; Prusa and Skeath 2002; Francois and Niels 2004; Feinberg and Reynolds 2006). These studies suggest that this tit-for-tat strategy in AD actions has created an “AD club,” which consists of countries that have implemented and actively utilize AD law. Blonigen and Bown (2003), however, provide another perspective on the debate. They suggest that the worldwide spread of AD regimes may dampen AD activity because countries become able to retaliate when they begin enforcing these laws. Concentrating on AD activity in the United States, these authors show that the risk of a potential reaction from the target country decreases the probability of an AD petition from U.S. industries as well as decreasing the likelihood of an affirmative AD decision from the U.S. AD agency. Whether retaliation encourages or dampens
.860***<br>(0.102)|||
|Pseudo R2|0.063|0.069|0.068|
|Obs|37402|37402|37402|

Notes: Ordered probit regressions. Robust standard errors, clustered at PSU level in parentheses. Regressions also included as independent variable age, sex, religion, marital status, area of residence and country dummies (coefficients not reported). Significance: * 0.10 ** 0.05 *** 0.01.

# **4. Measurement error in subjective well-being data: Consistency across scales and within households**

# **_4.1. Framing and scaling_**

We exploit the existence of two separate life-satisfaction questions in the LiTS (with different scales and placement in the survey) to examine the role of framing in variations in subjective 12
meta-analysis of climate change beliefs across 25 polls and 171 academic studies spanning 56 countries, <u>Hornsey et al. (2016)</u> find ideology, world views, and political orientation to be among the strongest predictors of climate beliefs. Free-market ideology is one of the strongest predictors of climate change skepticism. In Europe and Central Asia, support for the market economy tends to be greater among younger adults, which may be related, in part, to the resistance to change of older individuals, who spent much of their lives under a socialist system (EBRD 2007).

Media is an essential source of information about climate change, and more knowledge can influence concern about climate change (Lokshin et al. 2024). Malka et al. (2009) report that in the US, increased knowledge is associated with greater concern among adults who identify as

> 4 <u>Schwartz (1992) defines self−enhancement as characterized by an emphasis on the pursuit of one’s own interests</u> and dominance over others; self−transcendence emphasizes concern for the welfare and interests of others.

5
A selected group of estimates of the cost per death averted for a variety of treatments that address these conditions are presented in Table 6 (derived from Jamison and others, 1993). The cost of averting a death by treating each of the conditions are estimated using different methods (e.g. model estimates versus observed ranges) and should be used with extreme caution. The ranges for the three biggest killers are $1,000-$ 10,000 for diarrheal diseases, and a much more modest $379-$1,610 for ARI and $836-$3,967 for the complications of pregnancy. From these numbers it would appear that public sector intervention to treat each of these conditions could be done relatively cheaply and could save many lives.

III) How to reconcile the apparent and actual performance of public spending The results discussed so far imply that between the "effectiveness" of public spending on health and the "cost effectiveness" of available medical interventions there is a several orders of magnitude gap in the cost per averting a child death. The cross-national regression results suggest a range between about $50,000 and $100,000 whereas the medical intervention cost effectiveness figures suggest a range between $10 and $4000 (excluding the upper limit of $10,000 on diarrheal diseases). Understanding why public spending on health has not had a strong effect on reducing mortality is crucial to designing public policy to reduce excess mortality and morbidity in developing countries. In this section we present only an outline of the analytical possibilities. A detailed exploration, including a review of the existing empirical literature, is contained in a companion paper (Filmer, Hammer, and Pritchett, 1997).

What is of interest is the impact of spending an additional public dollar on health status, or the total derivative of health status with respect to public spending. This total derivative can 28
) and Sub-Saharan Africa (SSA) regions, the sample used in this study provides excellent coverage of the global population and the extreme poor. While the extremely low data coverage in the MENA region is a concern, it is not unique to this study. In fact, PovcalNet has for several years suppressed regional poverty estimates for the MENA region due to low survey coverage. But since the region has traditionally had a much lower incidence of poverty than the average for global and developing countries, the omission of most countries from the region has a limited impact on the characteristics of the poor.<sup>14</sup> The omission of over one-quarter of the population of Sub-Saharan Africa is a greater concern, since the region has the highest number of extreme poor (389 million)

> 10 In order to maintain consistency with the figures reported by the World Bank, we only include countries classified as “developing countries”. This excludes most of Western Europe, Canada, the United States, Australia, Japan, and the Republic of Korea.

> 11 Not every survey is fully nationally representative, due to the exclusion of rural areas or conflict areas in particular countries.

> 12 Population figures are based on population projections by the United Nations Department of Economic and Social Affairs (UNDESA).

> 13 Global poverty estimates are available on PovcalNet (http://iresearch.worldbank.org/PovcalNet). We refer to PovcalNet as a key reference point to assess the extent of our sample coverage and the validity of our estimates. As shown in Figure 1, our sample includes almost all countries covered by PovcalNet.

> 14 In 2008, the most recent year MNA estimates are reported, the region accounted for 0.7 percent of the total number of extreme poor.

6
_LF_ <u>(</u> _<u>A</u>_ <u>)</u> 1 _−F_ ( _<u>A</u>_ <u>)</u><sup>unitsof“loans”perproducer,whichisalsotheaveragesizeofloans.</sup> For the average loan, I use $550 _,_ 000, the average size of corporate and industrial loans in the US in 2007.<sup>21</sup> For _L_ , I use $400 _,_ 000 times (1 _− γ_ ). $400 _,_ 000 is the average size of US household wealth in 2007, excluding housing wealth.<sup>22</sup> A fraction 1 _− γ_ of this wealth can be lent. We therefore have: In other words, by equation 16, the estimated consumption gains from eliminating the financial system are 137.5%. This is obviously an unrealistically high number.

Part of the problem is that, in this model, _θ_ captures both the marginal cost of financial intermediation and its average cost. In the presence of a decreasing returns intermediation technology, the average cost of intermediation is lower than its marginal cost, implying some productivity gains from using financial intermediation and some profits associated with intermediation activities. However, adding decreasing returns to the intermediation technology is not a trivial extension.<sup>23</sup> To summarize, while this model highlights a price externality that potentially leads to quantitatively significant welfare losses, it is too soon to conclude that the financial system should be eliminated entirely. A richer quantitative investigation that takes into account decreasing returns (both in intermediation and in production) would be necessary in order to assess the optimal scope of financial intermediation.

> 21Data source: Average Loan Size for All C&I Loans, All Commercial Banks (EAANQ), Federal Reserve Bank of St. Luis (FRED database)

> 22Data source
References:

- Alesina. Alberto, Reza Baqir, and William Easterly, 1997. "Public Goods and Ethnic Divisions," NBER Working Paper No.6009. Cambridge, MA.

- Anand. Sudhir, and Martin Ravaillon, 1993. "Human Development in Poor Countries: On the Role of Private Incomes and Public Services," Journal of Economic Perspectives, 7(1):133-50.

- Barro, Robert J. and Jong-Wha Lee. 1996. "International Measures of Schooling Years and Schooling Quality." American Economic Review. Papers and Proceedings 86 (2): 218-23.

- Bidani, Benu, and Martin Ravaillon, 1997. "Decomposing Social Indicators using Distributional Data," Journal of Econometrics, 77:125-139.

- Caldwell, John C., 1986. "Routes to Low Mortality in Poor Countries," Population and Development Review, 12(2): 171-220.

- Caldwell, John C., 1990. "Cultural and Social Factors Influencing Mortality Levels in Developing Countries," The Annals of the American Academy, 510:44-59.

- CIA, 1994. "The World Factbook 1994" CIA.

- Deininger, Klaus and Lyn Squire, 1996. "A New Data Set Measuring Income Inequality," I h World Bank Economic Review, 10(3):565-591.

- Easterly, William and Ross Levine, 1996. "Africa's Growth Tragedy: Policies and Ethnic Divisions," mimeo (September). Washington, DC: The World Bank.

- Filmer, Deon, Jeffrey Hammer, and Lant Pritchett, 1997. "Health Policy: Weak Links in the Chain?" mimeo, Development Research Group, The World Bank. Washington, DC.

- Foster, Stanley O., Deborah A. McFarland, and A. Meredith John, 1993. "Measles," in Disease Control Priorities in Developing Countries, Jamison et al eds.

- Halstead, Scott B., Julia A. Walsh, and Kenneth S. Warren sed.,
remains strongly linked to their economic growth. Hence, to achieve decarbonization in the transport sector without prejudice to economic growth, there is a dire need to break this link.

The long-term decoupling results show considerable differences in the decoupling status among countries by income levels. While many advanced economies have managed to achieve absolute or relative decoupling, in many developing countries per capita transport emissions are increasing at a much faster rate than per capita GDP growth. Increases in transport CO2 emissions were particularly pronounced in the two largest middle-income countries: China and India. Both are still contributing less to global transport CO2 emissions than their population shares, unlike the United States or the EU countries, but they are rapidly catching up and are to a large extent driving current increases in global transport CO2 emissions.

Next, to explore the underlying drivers of CO2 emissions, we conduct both index decomposition and econometric analyses. For the decomposition analysis we specify two identities. The first decomposition breaks down emissions into three factors: emission intensity measured by transportation emissions per unit of GDP, economic growth (in terms of GDP per capita), and population growth. In many developed countries, declining emission intensity contributes to the decoupling of transportation emissions from economic growth. In most developing countries, although emission intensity also fell, population and GDP growth outstripped these efficiency-related transport emission savings.

In the second decomposition analysis, we further break down emission intensity (measured as CO2 emissions per unit of GDP) into carbon intensity of fuel consumption, energy intensity of transportation, and transport demand. We focus on the inland transportation sector, including rail and road sectors for which disaggregated data are available on travel volume measured by kilometers traveled by passengers and freight. Inland transportation is the largest source of global transport emissions, <mark>accounting for 84 percent of total emissions in 2018. The results again show that efficiency improvements hav</mark> e played an important role in flattening the emissions curve. Most countries have managed to reduce the energy intensity of inland transport. By
Figure 7 depicts fitted lines from 5<sup>th</sup> order local polynomial regressions of saving for old age on log (GDP per capita)<sup>13</sup> in Panel A and household income per capita percentile (by country) in Panel B. The figure in Panel A indicates a positive and mildly concave relationship between real GDP-per-capita levels of USD $400 and $3,000, corresponding to rates of saving for old age between 8% and 17%, respectively. The relationship then becomes steeply convex, with the maximum occurring at real GDP per capita levels of around 50,000 and rates of saving for old age just under 50 percent. The rates of saving for old age then drop for figures greater than 50,000 to figures between 40 and 48 percent. In comparison, Panel B depicts a more or less linear relationship between saving for old age and household income per capita percentile by country. The rates of saving for old age are around 13% at the bottom household-income-per-capita percentile, to around 30% at the 97<sup>th</sup> percentile.

Finally, Figure 8 presents the distribution of saving for old age across different labor market groups—self-employed, wage employed, out of workforce, and the unemployed. We find striking differences between employed adults versus adults out of the workforce. The weighted global figures for saving for old age by labor market status are 33% for those in paid employment, 27.4% for the self-employed, 17.1% for those out of workforce, and 11.2% for the unemployed.

# **4. Empirical analysis**

# **4.1 Empirical strategy**

Our empirical strategy entails three stages. In the first stage, we regress saving for old age on a rich set of individual characteristics. These include: gender, urban/rural region of residence, age [6 groups], education [3 groups], marital status [3
performance students, and about a third were among the bottom performance students. This evidence suggests that the teaching career in Brazil is attracting a high share of lower performing students. In fact, the cut-off ENEM score required for admission to public higher education institutions is much lower for _licenciatura_ courses in these universities. In year 2014 the mean cut-off scores for _licenciatura_ in specific disciplines and in pedagogy were, respectively, the 5th and the 2nd lowest in a classification of 75 courses.<sup>5</sup> In addition, _licenciatura_ offered by private institutions are among courses with less competition for admission and therefore might be the choice of students with weaker educational background.<sup>6</sup> Figure 2 - Percentage of Public Upper Secondary Schools Teachers with Higher Education <!-- Start of picture text --> 82.0 87.6 88.5 78.9 89.8 82.3 95.3 89.6 93.2 87.5 91.6 87.7 92.8 93.2 81.0 93.5 83.6<br>74.4 75.4 76.9<br>52.2 54.5 54.4<br>47.4<br>42.2<br>38.0 36.2 39.0<br>11.4<br>7.4 7.6<br>5.7 5.2 3.3<br>1995 1999 2001 2003 2005 2007 2009 2011 2013 2015<br>Higher Education Licenciatura Postgraduate<br>Pedagogy Pedagogical supplementation Private institution<br><!-- End of picture text -->

* _Private institution_ indicates percentage of higher educated teachers that attended only private higher education Institution. _Source._ School Census, microdata, INEP/MEC.

Other factors that may be affecting the quality of trained teachers are related to the nature and quality of teacher pre-service training programs. As pointed out by
Policy Research Working Paper 9867

# **Abstract**

This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationally-representative, panel household survey data from six countries in Sub-Saharan Africa. These data are spatially linked with a range of geospatial weather data sources and related metrics. The paper provides systematic evidence on measurement error introduced by (1) different methods used to obfuscate the exact GPS coordinates of households, (2) different metrics used to quantify precipitation and temperature, and (3) different remote sensing measurement technologies. First, the analysis finds no discernible effect of measurement error introduced by different obfuscation methods. Second, it finds that simple weather metrics, such as total seasonal rainfall and mean daily temperature, outperform more complex metrics, such as deviations in rainfall from the long-run average or growing degree days, in a broad range of settings. Finally, the analysis finds substantial amounts of measurement error based on remote sensing products. In extreme cases, the data drawn from different remote sensing products result in opposite signs for coefficients on weather metrics, meaning that precipitation or temperature drawn from one product purportedly increases crop output while the same metrics drawn from a different product purportedly reduces crop output. The paper concludes with a set of six best practices for researchers looking to combine remote sensing weather data with socioeconomic survey data.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at tkilic@worldbank.org and smurray@worldbank.org.

_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