rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 4: Impact of Anti-Pest Treatments (APT)

||(1)<br>TFPQ|(2)<br>Markup|(3)<br>TFPR|
|---|---|---|---|
|Anti-pest treatment (output)|-0.126|-0.353|-0.479|
||(0.219)|(0.298)|(0.297)|
|Anti-pest treatment (input)|3.756***|5.478**|9.235***|
||(0.885)|(2.167)|(2.559)|
|Observations|59,791|59,791|59,791|
|R-squared|0.981|0.695|0.955|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of anti-pest treatments (APT), applied either to output or inputs, on plant’s TFPQ (col. 1), markup (col. 2) and TFPR (col. 3) in the years between 2008 and 2015. _Output APT_ is the share of HS-10 digits products subject to at least one APT within the corresponding five digit industry. For each five digit industry, _Inputs APT_ is the weighted sum of output APT in all five digit supplying industries. The coefficients are obtained by the estimation of Equation 1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted
time, and more likely to have higher years of completed schooling as they begin to enter school. As in Gertler and Fernald, program impacts are generally apparent in the comparisons between the treatment and matched comparison groups, and not in the comparisons between the two treatment groups that were randomly assigned to different amounts of program exposure.

Three recent papers analyze the effects of daycare or preschool attendance in Bolivia and Argentina. Behrman, Cheng, and Todd (2004) use non-experimental data to evaluate the impact of a Bolivian pre-school program, the _Proyecto Integral de Desarrollo Infantil_ , or PIDI. The outcome measures include “a battery of tests of bulk motor skills, fine motor skills, language and auditory skills, and psychosocial skills”. (The authors do not provide details on the exact nature of these tests.) The PIDI program provides full-time daycare, nutritional and educational services to children between the ages of 6 and 72 months in low-income areas. Behrman, Cheng, and Todd use propensity score matching to estimate program impacts. First, they compare program participants with a matched comparison group of non-participants—they refer to these as average treatment impacts. Second, they compare participants with different amounts of exposure to the program—they refer to these as marginal treatment impacts. The identifying assumption for the estimates of average treatment impacts is that there is no selection into the program on the basis of unobservable characteristics of households or children. This restriction is loosened somewhat in the estimates of marginal treatment impacts although, conditional on program participation, unobservables are still not allowed to determine the duration of exposure.

Behrman, Cheng, and Todd (2004) find that, in the comparison of treated and untreated children, there is some evidence of positive program impacts on bulk and fine motor skills, psychosocial skills, and language acquisition. These impacts are concentrated among children ages 37 months and older—for the younger children, ages 6-36 months, the estimated program effects are generally insignificant, and are as likely to be positive as negative. When
Does economic growth improve the human lot? Some empirical evidence. In P. David and M. Rider (Eds.), Nations and Households in Economic Growth. Essays in Honor of Moses Abramovitz. New York: Academic Press. Frey, B. and A. Stutzer (2000). Happiness, economy and institutions. The Economic Journal 110, 918-938.

- Glewwe, P. and G. Hall (1998). Are some groups more vulnerable to macroeconomic shocks than others? Hypothesis tests based on panel data from Peru. Journal of Development Economics 56, 181-206.

- Graham, C. and S. Pettinato (2001). Frustrated achievers: Winners, losers, and subjective well being in new market economies. The Brookings Institution, Center on Social and Economic Dynamics Working Paper Series 21.

- Greene, W. H. (2000). Econometric Analysis. London: Prentice-Hall. Hayo, B. and W. Seifert (2001). Subjective economic well-being in Eastern Europe. Essen University, mimeo.

- Hoetker, G. (2001). Confounded coefficients: Accurately comparing logit and probit coefficients across groups. University of Illinois, mimeo.

- Inman, R. and F. Oberholzer-Gee (2003). Democratic institutions and subjective well-being. University of Pennsylvania, mimeo.
payment defaulted for more than 90 days. In other countries, such as Mexico and Peru, the overdue days for recording NPLs depend on the type of credit portfolio, usually varying from 15 to 90 days. In addition, while some countries classify the sectoral exposures of their banks’ credit portfolios based on the International Standard Industrial Classification of All Economic Activities (ISIC), not all of them use the same revision and most countries employ a self-defined classification of economic activities. This affects sector comparability since the high level of aggregation of the publicly available information does not allow leveraging on standard correspondence tables. Therefore, to ensure comparability of banking exposure to economic sectors across countries, we associate them manually. See Annex C for more details.

Information on hazard mapping at the level of country-province is sourced from Thinkhazard!, which is a web-based tool enabling users to consider the impacts of disasters on development projects. It was developed by the Global Facility for Disaster Reduction and Recovery in partnership with various partners, including the World Bank Group. The hazard levels are derived from modeling information that combines the spatial distribution (e.g., flood depth, ground shaking, etc.) at a given frequency or return period.<sup>17</sup> We gather disasters at the level of countryprovince from the Emergency Events Database (EM-DAT) maintained by the Center for Research on the Epidemiology of Disasters. The data set includes the worldwide natural disasters of all types and records the dates, human death toll, material damages, and geographic locations of the disasters.

Our transition risk assessment, in addition to credit outstanding data, relies on firm-level information and CO2 emissions. Firm-level data is gathered from Orbis, a worldwide firm-level database with detailed information of balance sheets and income statements of firms compiled by Bureau Van Dijk from various national sources. After data cleaning, our sample covers 38,227 firms with total outstanding debt of US$ 2,112 billion. Data is as of the most recent available for each firm
# **1 Introduction**

Household surveys are the dominant tool to measure and monitor welfare in low- and middle-income countries. In these contexts, measures of material well-being typically consider a combination of household consumption and expenditures, of which food comprises a large share. Indeed, global monitoring of extreme poverty relies on food-based estimates of welfare, such as the Cost of Basic Needs approach. Proxy means-tested targeting of interventions also derives from these estimates of welfare. Many programs worldwide target food consumption and nutrition, ranging from school meals to food subsidies like the Supplemental Nutrition Assistance Program (SNAP). Because of its centrality to policy and welfare measurement, a large body of the literature has questioned the accuracy of different modes of collecting consumption data to assess household well-being. We revisit this issue at a time when developing countries have increasingly moved to recall modes to collect food consumption in their national expenditure surveys.

Acquisition diaries have become the workhorse for benchmarking comparisons of household measurements from alternative collection modes. Beegle et al. (2012) randomize households in Tanzania to individual assisted diaries to define a benchmark against alternative survey designs that use recall questions. This is possibly the most important experiment on consumption measurement in low- and middle-income countries in the last decade (a more recent study for Niger is Backiny-Yetna et al., 2017). The accuracy of consumption measurements is also of concern in the developed world. For example, Brzozowski et al. (2017) compare recall questions on food spending from the Canadian Food Expenditure Survey to data from expenditure diaries. Battistin and Padula (2016), among others, do so using the Consumer Expenditure Surveys in the United States. In these instances, too, the accuracy of reports is assessed against diaries.

The assumption that diaries provide an error-free benchmark has little statistical justification. Diaries arguably minimize various types of reporting errors, including recall errors, telescoping, rule of thumb, and errors of omission (Lyberg and Kasprzyk, 2004). However, diaries are far from perfect. Most
Policy Research Working Paper 6159

# **Abstract**

This paper provides relevant indicators and measurements useful for public policies seeking the expansion of equitable human development opportunities for Egyptian children and youth. To measure equitable access to opportunities, the authors use the Human Opportunity Index to examine the evolution of 16 basic opportunity indicators grouped in four sectors: education, basic housing services, early childhood development, and nutrition and hunger. The main findings show that during the last decade most opportunities for children and youth improved unambiguously,for the first two sectors, but were stagnant for nutrition and early childhood development opportunities. Although the urban-rural and interregional gaps were partially reduced, there are still substantial opportunity gaps between children in favorable and unfavorable circumstances. Parents’ education, income per capita, urban-rural location, number of siblings, and regional location, are the five most important factors affecting equality of opportunity, although their impact varies across indicators.

This paper is a product of the Poverty Reduction and Economic Management Unit, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at selshawarby@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 is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(2009) and Helpman, Istkhoki and Redding (2010) that hypothesizes a relationship between ...rm-speci...c wages and how the ...rm responds to globalization.

Bustos (2007) posits that an examination of wage inequality that examines the interaction between trade and technology— as opposed to selecting one explanation in preference over the other— will o¤er a better explanation of the relationship between global trade and inequality. Bustos therefore presents a model of the relationship between trade liberalization and demand for technology and skill among ...rms in developing countries that accounts for ...rm heterogeneity. She tests this model using panel data from Argentine manufacturing ...rms. The dataset spans 1992-1996 and therefore captures a period of trade and capital account liberalization in Argentina.

Bustos ...nds a strong relationship between exporting and increases in technology investment and skill upgrading. Speci...cally she ...nds that, prior to trade liberalization, continuing exporters and foreign owned ...rms employed higher skilled labor than those ...rms that were domestically owned and that had never exported. Those ...rms that began exporting after liberalization upgraded worker skill more quickly than those ...rms that remained exclusively in the domestic market; they also upgraded technology more quickly than all other ...rms. Further, after trade liberalization, new and continuing exporters as well as foreign owned ...rms spent 53-69 percent more on technology than their domestic non-exporting counterparts. Those ...rms that invested more in technology upgrading also realized a faster increase in skilled labor. Bustos concludes that, due to the consequences of rising demand for technological investment, trade liberalization can have a strong impact on worker skill upgrading.

While much of the literature (for example, Verhoogen (2008) and Bustos (2007)) looks at the impact of the act of exporting on ...rm behavior, Brambilla, Lederman, and Porto (2010) focus on
00|
|Construction|0.06|0.07|0.10|0.28|0.21|0.00|0.15|0.10|0.00|
|Retail, hotel, restaurant|0.12|0.13|0.37|0.15|0.16|0.60|0.19|0.19|0.75|
|Others|0.29|0.26|0.14|0.25|0.33|0.00|0.21|0.27|0.00|

_Source:_ Bangladesh RMS for employment type of returnees and the HIES 2016-17 for other statistics; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18.

_Note:_ Sample is restricted to males aged 20-59. Sample weights are applied to summary statistics.
in the RCRE sample villages between 1988 and 2002.<sup>16</sup> During the period with rapid expansion in migration, from 1995 to 2002, a one-percentage point increase in out-migration from the villages is associated with consumption per capita increases of 10% and 7% percent for households in the low and middle terciles of initial consumption distributions, respectively. More affluent households in the village also experienced consumption growth of 4% though this increase appears attributable to the accumulation of durable goods and housing.<sup>17</sup>

> 15 Recent debates on the returns of rural-to-urban migration on income and consumption in developing countries focus on the income and consumption of migrants themselves and their households in the cities (Hamory et al., 2021; Lagakos et al., 2020). The estimated impact of migration on household consumption for migrants in urban China is 23%.

> 16 To identify the migrant impact, the authors use differences in the timing of availability of new ID cards (which were important for legal temporary migration) across counties of rural China. The authors show that timing is exogenous to local shocks and preferences for migration (de Brauw and Giles, 2018, 2017).

> 17 These findings are consistent with an earlier study suggesting that income from work outside the village (by both migrants and commuters) contributes to reducing inequality (Benjamin et al. 2005).

16
.36 billion people), subnational poverty estimates highlight another regional hotspot of risk: At least 71 million people in Sub-Saharan Africa are estimated to live in both extreme poverty (using a $1.9 a day definition) and significant flood risk – thus making them particularly vulnerable to prolonged adverse impacts on livelihoods and well-being.

The remainder of this study is structured as follows. Section 2 offers an overview of the existing evidence base, especially on flood risk and poverty. Section 3 summarizes the flood risk, population, and poverty data used in the analysis. Section 4 details the analytical methodology and computational process. Section 5 presents and discusses the results.<sup>1</sup>

# **2. Existing evidence**

Prior to the availability of high-resolution global flood hazard maps, studies relied on historical disaster catalogues to produce rough global exposure estimates. Peduzzi et al. (2009) use a global inventory of recorded flood events from EM-DAT to estimate an exposure indicator at the country level. Yet, the lacking data on the spatial distribution and coincidence of flood risk and population, this approach does not allow a robust estimation of exposure headcounts. Jongman et al. (2012) provide the first global flood exposure analysis that utilizes spatial hazard and population data, albeit at relatively coarse resolution by current standards (10 x 10 kilometers for population data, and 1 x 1 kilometers for flood data). The authors estimate that in 2010, 805 million people worldwide were exposed to fluvial flooding with a 1-in-100 year return period; 271 million people to coastal flooding with the same return period. By accounting for overlap between coastal and fluvial flood risk zones, they further estimate that the global flood exposed population was 992 million in 2010, and project this number to reach 1.3 billion by 2050.

In this study, we show that this projection has already been exceeded in 2020, with 1.47 billion living in high-risk flood zones. Several factors can explain this increase in estimated exposure headcount:
ensure that the consumption to output ratio in the model equaled the 1996 value of that ratio in the data, roughly 0.5.

"<sup>7</sup> Bank of Thailand data was obtained from their World Wide Web site at http://www.bot.or.th/.

'<sup>5</sup> This figure was obtained by taking a standard measure of total gross external debt, subtracting public sector external debt, and then subtracting the foreign assets of the banking system. As in the Korean case, these are the only private sector foreign assets for which information is generally available.

19
Estimating Extinction Threats with Species Occurrence Data from the Global Biodiversity Information Facility Conservation planning, global biodiversity, species’ occurrence region, Species’ Extinction Bank program managed by the authors, with Dr. Nagaraja Rao Harshadeep, Global Lead for Disruptive Technology. We are also thankful to the participants of the webinar on this subject conducted during the Practice and the Environment, Natural Resources, and Blue Economy Global Practice. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World
the returns to schooling, for increases in wage inequality is cliché. In fact, in many countries, such as Chile and Mexico, the market-determined income inequality (that is, without transfers) has improved, as the trends in skill premiums turned downwards. The graphs in Figure 2, taken from Aedo and Walker (2011, p. 46), show the evolution of education wage premiums for several LCR economies since the early 1990s. The era of rising premiums for finishing secondary school seems to have ended at the beginning of the 21<sup>st</sup> Century, although in Chile tertiary education premiums might have risen a bit since.

Nonetheless, these trends do not say much about the role of trade in determining relative wages of skilled workers. First, as highlighted by Broda and Romalis (2009), trends in relative incomes and wages would need to be adjusted by household-specific price indexes, with trade playing a potentially important role in determining consumption prices across households. As mentioned, most studies ignore this first-order price effect, probably because it is difficult to get detailed price data at the household or locality level, but it is not impossible as evidenced by the paper on Chile by Duran et al. (2010). In addition, unit values computed from household survey data could be used as an imperfect proxy.

Second, even if trade raises skill premiums, other factors, such as increases in the supply of high school graduates, the quality of educational services, and numerous other economic fluctuations unrelated to trade could be affecting trends in skill premiums. Furthermore, skills might affect not only the earning potential of workers, but also their capacity to change jobs when hit by a negative shock.

Finally, it is worth noting that recent literature has emphasized that the act of exporting per se and of exporting to high-income destinations in particular might require skilled workers, although the types of skills (e.g., knowledge of foreign languages) demanded by successful exporting firms remain murky (see
PotLfI(CY RlSFARCH WORKING PAPER 2487

# Summary findings

Countries with more developed financial sectors experience fewer fluctuations in real per capita output, consumption, and investment growth. But the manner in which the financial sector develops matters.

The relative importance of banks in the financial system is important in explaining consumption and investment volatility. The proportion of credit provided to the private sector best explains volatility of consumption and output.

Denizer, Iyigun, and Owen generate their main results using fixed-effects estimation with panel data from 70 countries for the years 1 956-98.

Their general findings suggest that the risk management and information processing provided by banks may be especially important in reducing consumption and investment volatility. The simple availability of credit to the private sector probably helps smooth consumption and GDP.

This paper-a product of the Poverty Reduction and Economic Management Sector Unit, Europe and Central Asia Region-is part of a larger effort in the region to understand the links between finance and macroeconomic volatility. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Irina Partola, room H4-346, telephone 202-473-5759, fax 202-522-2751, email address ipartolaC@Lworldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The authors may he contacted at cdenizer@Cworldbank.org, murat.iyigun@;frb.gov, or aowen@'hamilton.edu. November 2000. (28 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An ohjective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should he cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the vieu of the World Bank, its Executive Directors, or the countries they represent._ Produced by the
# **Appendix 7: Data Availability**

Appendix Table 2 shows data availability by treatment status.

|**Appendix Table 2: Data**|**Availability**<br>Round 2|**by Treatm**<br>Round 3|**ent Statu**<br>R2 or R3|**s**<br>Round 4|Round 5|R4 or R5|
|---|---|---|---|---|---|---|
|**Panel A: Interviewed**|||||||
|Assigned to Training|0.034***|0.030***|0.023***|0.009|0.023*|0.010|
||(0.012)|(0.011)|(0.008)|(0.013)|(0.013)|(0.011)|
|Spillover Group|0.014|‐0.013|‐0.003|‐0.026*|0.002|‐0.009|
||(0.013)|(0.014)|(0.010)|(0.014)|(0.015)|(0.012)|
|Pure Control Mean|0.886|0.889|0.943|0.894|0.876|0.923|
|**Panel B: Data on Surviv**|**al Available**||||||
|Assigned to Training|0.011*|0.016**|0.002|0.005|0.010|0.003|
||(0.006)|(0.006)|(0.003)|(