rafmacalaba/data-use-mentions-extended huggingface dataset:
13. Other (specify) 2bOth. Please specify other perpetrator for **EVENT** in **2010**

# 3a. Did EVENT occur in **2011** ?

3b. Who was the perpetrator of EVENT in **2011** ?

3bOth. Please specify other perpetrator for **EVENT** in **2011** .

# A.1.2.   Consumption aggregate

The consumption aggregate is the per capita total household food and nonfood consumption expenditure collected in the household survey of both post-planting and postharvest visits during the three waves of the GHS survey. The wave-based consumption aggregate is the median consumption per capita of the two visits.

The consumption aggregate has been deflated using a monthly Consumer Price Index as well as adjusted with spatial variation in prices using prices derived from the survey. Details of the survey instruments are available from the NBS (NBS 2016) regarding the third wave. The reports and questionnaires can be downloaded from the World Bank Microdata Library <u>(http://microdata.worldbank.org/index.php/catalog/2734).</u>

# A.1.3.   Questions on food insecurity (CSI) from GHS data set

1. In the past seven days, how many days have you or someone in your household had to: (if no days, write “0”)

- a. Rely on less preferred foods? b. Limit portion size at mealtimes?

- c. Borrow food or rely on help from a friend or relative? d. Reduce number of meals eaten in a day?

- e. Restrict consumption by adults in order for small children to eat?

# A.1.4.  Geographical control variables

The geographical control variables used in the analysis are obtained from the GHS data sets. The original sources of these variables vary. The LSMS team has merged these data with the GPS coordinates of the GHS households.

ii
is reliable and property rights and responsibilities of companies and financial institutions are properly defined. Reforms in these areas may be difficult to accomplish in the short term, but there are many other dimensions where progress comes at lower costs: Djankov, McLiesh and Shleifer (2007) use a panel of aggregate data for 129 countries over 25 years to confirm that creditor rights and the existence of credit registries (public or private) exhibit a strong correlation with higher ratios of private credit to GDP. Interestingly, the information infrastructure (for instance, credit registries) matter relatively more (compared to creditor rights) in poorer countries than in rich countries.

8
these cities are growing in people but without the commensurate investments in human and physical capital that enhance the returns from density (Lall, Henderson and Venables 2017; Ellis and Roberts 2016).

Avner and Lall (2016) find that jobs are often located far from where people live; heavy congestion and high rates of walking and informal transportation fragment the labor market and lead to low employment rates and the misallocation of labor in Nairobi. For instance, _Matatu_ (privately owned minibuses) users on average can access only 4 percent of jobs within 30 minutes, 10 percent within 45 minutes, and 20 percent within 60 minutes. In metropolitan Buenos Aires, equivalent accessibility figures using public transportation are only 7 percent, 18 percent, and 34 percent for the same time thresholds (PeraltaQuiros 2015). In Ugandan cities, 70 percent of work trips are on foot (Uganda Bureau of Statistics 2010), with only 19 percent of the city’s jobs being accessible within a one hour commute (Bernard 2016). Further, many cities particularly in Sub-Saharan Africa and South Asia produce non-tradable goods and services (Lall, Henderson and Venables 2017, Venables 2017). This is consistent with what has been called “pre-mature urbanization” (see for e.g. Gollin et al., 2016), that is, people are concentrating in developing country cities but not because industrial dynamism is attracting them.

To shed light on the productivity advantages of agglomeration in developing country cities, we make the following contributions. _First_ , we expand the sample size of existing meta-analyses to systematically examine 1,242 elasticity estimates, originating from 70 studies covering 21 developing and 12 advanced countries. _Second,_ we construct novel estimates of density elasticity on urban costs, with respect to crime, congestion and pollution by collecting data from hundreds of cities around the world, including several in developing countries. This fills a critical knowledge gap as relative to studies measuring the 2
|2|
|Health & Biotech<br>1.77|0.93|1|5|2|
|IT<br>2.37|1.28|1|8|2|
|Language<br>2.59|1.09|1|5|2|
|Law<br>3.04|1.62|1|8|3|
|Logistics & Transport<br>2.37|1.45|1|7|2|
|Management<br>3.17|1.59|1|8|3|
|Manual occupations<br>2.00|1.19|1|8|2|
|Manufacturing<br>2.43|1.18|1|5|2|
|Marketing, PR & Media<br>2.34|1.51|1|8|2|
|Others<br>2.67|1.57|1|8|2|
|Public sector<br>3.14|1.58|1|8|3|
|Sales<br>1.62|0.94|1|6|1|
|Science and Research<br>2.20|1.17|1|4|2|
|Telecommunication<br>2.21|1.28|1|5|2|
|Unclassified<br>2.78|1.03|1|5|3|

_Source:_ World Bank calculations based on job portal data _._ We explore this aspect more in depth below. The extent of under-specification differs across industries as shown in Figure 40 in Appendix C, while jobs in Finance & Accounting, Law and the Public Sector specify experience requirements in more than 50 percent of the cases, hardly any requirements are found among jobs posted in Gastronomy and Hoteling or Telecommunication. For management jobs the specification rate
5<br>1.0 54<br>0.5 52<br>0.0 50<br>-0.5<br>48<br>-1.0<br>-1.5 46<br>-2.0 44<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21<br>Oil exporters (LHS) Frontier markets (LHS)<br>Fragile states (LHS) Others (LHS)<br>Actual poverty rate in SSA (percent of population, RHS)<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> _Source_ : Authors’ calculation based on WDI database We checked to see if there are also differences in mortgage market policy and development across SSA countries. Additionally, we compared the effective performance of housing finance policy, depth, penetration, housing need, cheapest price, and input price with the benchmark performance.

# Wealth-based breakdown

From a wealth point of view, expected income levels may affect housing finance indicators. In fact, table A.1 in the appendix shows that upper-middle-income countries perform better on housing finance indicators.

# Breakdown based on legal origins

The origin of a country’s legal system may affect its housing market and finance policies. Intuitively, colonized countries seem likely to have inherited their legal systems, and thus the way their housing markets 9
OECD (2009). Creating effective teaching and learning environments: First results from TALIS.

Terrier, C. (2016). Boys lag behind: How teachers' gender biases affect student achievement. IZA Discussion Paper No. 10343. Available at SSRN: <u>https://ssrn.com/abstract=2868309</u>

- Tharp, R. G., & Gallimore, R. (1988). _Rousing minds to life: Teaching, learning, and schooling in social context._ New York, NY, US: Cambridge University Press.

- Tishman, S., Jay, E., & Perkins, D. N. (1993). Teaching thinking dispositions: From transmission to enculturation. _Theory into Practice_ , _32_ (3), 147-153.

- <mark>Tyler, J. H., Taylor, E. S., Kane, T. J., & Wooten, A. L. (2010). Using student performance data to identify effective classroom practices.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>100</mark>_ <mark>(2), 256-60.</mark>

- Vosniadou, S. (Ed.). (2009). _International handbook of research on conceptual change_ . Routledge.

Vygotsky, L. S. (1978). _Mind in society_ . Cambridge, MA: Harvard University Press.

- Wallin, D. (2003). Student leadership and democratic schools: A case study. _National Association of Secondary School Principals NASSP Bulletin_ , _87_ , 55–78.

- Walsh, J. A., & Sattes, B. D. (2011). _Thinking through quality questioning: Deepening student engagement_ . Corwin Press.

- Walsh, J. A., & Sattes, B. D. (2016). _Quality questioning: Research-based practice to engage every
health outcomes—nearly 100 percent chances of prenatal care, delivery care, and immunizations and a low chance of infant mortality. In contrast, as of 2012, the least advantaged child has only a 54 percent chance of prenatal care, a 40 percent chance of skilled delivery, a 65 percent chance of full immunization, and a 2.2 percent chance of dying in the first year of life. With the exception of immunizations, there have been improvements over time in the least advantaged children’s prospects, but gaps remain large.

**Figure 11. Most Advantaged and Least Advantaged Simulations for Health and Survival by Year (Percentages)**

<!-- Start of picture text --> 2003/4 2011 2012<br>99 99 99 100 99 100 98 96<br>100<br>90 83<br>80<br>70 65<br>60 54<br>50<br>40<br>37 36<br>40 34<br>30 24<br>20<br>10 4.5 2.2 1.1 0.3<br>0<br>Prenatal Delivery Immunizations Infant mortality<br>Percentage<br>Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged<br><!-- End of picture text --> _Source:_ Authors’ calculations based on DHS 2003/04, MICS 2006/07, and ENPSF 2011.

# 4.3.2 Nutrition

Inequality in nutrition in terms of being stunted, underweight, and wasted is low and has not increased over time (Figure 12). Differences in some years are likely due to chance (not statistically significant). Although only one year of data is available (so we are unable to assess trends), the level of inequality in access to adequately iodized salt is very high. For children to have equal chances, 30 percent of the opportunities to access adequately
|tatistic)|0.000000|

# **_5.3 Analysis of Dispersion of MWRs_**

An efficient and transparent annuities market should produce similar prices (or MWRs) for customers with similar characteristics. The results above indicate that annuity pricing is influenced by the characteristics of the annuitant such as age and gender, but the regression does not explain a relatively large share of the variations of MWRs across individual annuitants and over time. The unexplained variations in MWRs could be simply due to the absence of key explanatory variables, such as the level of education of individual annuitants and their geographical location, as well as variables capturing provider characteristics. These limitations could not be overcome, as the dataset on individual annuities used to compute MWRs does not provide information on providers or further information on the characteristics of annuitants, beyond those explored above.

The dispersion of MWRs could also be due to institutional and regulatory inefficiencies, such as the lack of a transparent pricing system and the excessive influence of brokers. The influence of these factors may be examined, because during this period there were substantial efforts to improve market transparency. As mentioned before, a major development in the annuities market was the passage of a new Pensions Law in 2004 that, among other factors, introduced a cap on broker’s commissions and an electronic quotation system that allows easy and transparent comparisons of annuity and PW prices. The draft of the Pensions Law was first submitted to Congress in 2000, and it is possible that the market started changing behavior in anticipation of the Law’s approval. Such
when the boat owner went fishing at least three days during that week. This latter sample restriction further excludes periods of absence from the labor market due to sickness or some other special temporary condition that interferes with boat owners normal labor market participation. Our third sample is even more restrictive and focuses on weeks when boat owners were active at least for four days. All these samples exclude extended absences from fishing due to temporary migration or long periods of inactivity. Note that the samples are unbalanced because individuals become boat owners at different points in time. In addition, the samples exclude boat-owner-weeks with missing participation data, so that all boat-owner-weeks have participation data for all 6 working days.<sup>20</sup> Perhaps due to their access to credit, average savings (in bank deposits, cash at home, jewelry and other forms of savings) appear low at INR 2,840 which is less than the average value of catches for two days.

Table 2 shows descriptive statistics for the variables that affect boat owners’ value of catches. During 2001-2007, the average price of frozen Indian calamari at the Spanish market stays stable around EUR 3 per kilogram, and the Real Exchange Rate INR-EUR is 4 on average.<sup>21</sup> Finally, the real price at which a gallon of kerosene is exchanged in the Port of – – Singapore the main center of fuel exports to the rest of Asia is approximately INR 63 (USD 1.2) per gallon, and unlike the price of calamari, it fluctuates considerably, with a variance of 21.7.

# **3 Conceptual Framework and Empirical Strategy**

## **3.1 A Model of Boat Owner Labor Supply**

Every day, boat owners decide whether to go fishing or not, considering their expected catches, their level of fatigue, and, possibly, the cumulative catches they have obtained during that week. A full description of their optimal decision requires solving a stochastic dynamic programming problem. Instead,
|4.15|1.13|1.27|6.01|
|Z-score average precipitation|0.14|0.95|-2.46|4.11|
|Total precipitation|392.40|163.27|56.73|972.43|
|Historical total precipitation|373.46|101.33|114.26|540.54|
|Z-score total precipitation|0.14|0.95|-2.46|4.11|
|**Anomalies**|||||
|Positive temperature anomaly (heat shock)|0.91|0.97|0.00|4.42|
|Negative temperature anomaly (cold shock)|0.23|0.44|0.00|2.24|
|Positive precipitation anomaly (food shock)|0.45|0.65|0.00|4.11|
|Negative precipitation anomaly (drought shock)|0.31|0.45|0.00|2.46|
|Observations|379,170||||

_Note:_ Authors’ calculations based on ERA5 and CHIRPS data. Averages within survey sample, estimated using individual weights. The time frame refers to a time bin of 90 days before a household’s EPH interview. Historical values are calculated based on the years 1981-2003, all other values are averaged over the years 2004-2019. The number days above 30°C/35°C are calculated based on the daily maximum temperature.

# **4.2 Methodology**

Our empirical framework builds on previous work that aims to estimate micro-level effects on poverty and inequality resulting from the observed increase in frequency and severity of extreme weather events due to climate change, such as Letta et al. (2018) and Aggarwal (2021). By exploiting exogeneity in weather shocks across districts and time we are able to estimate relationships between weather shocks and
inventions is not widespread (see, for example, Lanjouw _et al_ . 1998). Moreover, firms are more likely to use patents to protect new products

> 5 For example, in the US, private R&D is available from 1972-1994 for air pollution control, but it is not broken down by pollutant.

> 6 For example, US patent classes 423/235-423/239 pertain to control of “nitrogen or (a) nitrogenous component”, and patent classes 423/242 – 423/244 and 423/569 – 423/570 pertain to control of sulfur compounds.  Using patent databases, it is possible to download all patents in these classes.

> 7 Accessible at http://stats.oecd.org/index.aspx, accessed September 21, 2011.

7
*Population Vulnerability**

We consider a range of population vulnerability curves, varying the percent of the population that is affected as a function of depth. Highest vulnerability occurs when 100% of those living on the ground floor are affected by inundation of only 0.1m and 100% those living on other stories are affected by an inundation of 1.1m (1m higher than the ground floor threshold). Lowest vulnerability occurs when 2% of those living on the ground floor are affected by inundation of 1m and, correspondingly, 2% of those living on other stories are affected by an inundation of 2.1m.

## **Economic Vulnerability**

For demonstration, we use a different approach to examining a range of vulnerabilities for economic assets. Here, we assume that 0m of depth corresponds to 0% loss, while a depth of 5.5m results in 80% loss, based on initial data in the RoyalHaskoning interim study report (2012). We vary the shape of this curve, i.e. how changes in depth affect loss between these two endpoints. In the highest vulnerability curve, 22% of economic value is lost at a depth of 10cm; in the lowest vulnerability curve, essentially 0% of economic value is lost at a depth of 10cm.

_________

> 30 Tax revenue data are from the Ho Chi Minh City Statistics Book, Table 3.11.

> 31 Geographic data are from the Ho Chi Minh City Statistics Book, Table 2.01.
POtIIcy RESEARCH WORKING PAPER 2268

# Summary findings

Using internationally comparable household data sets (Demographic and Health Surveys), Filmer investigates how gender and wealth interact to generate withincountry inequalities in educational enrollment and attainmient. He carries out multivariate analysis to assess the partial relationship between educational outcomes and gender, wealth, household characteristics (including level of education of adults in the household), and community characteristics (including the presence of schools in the community). He finds that: Women are at a great educational disadvantage in countries in South Asia and North, Western, and Central Africa.

Gender gaps are large in a subset of countries, but wealth gaps are large in almost all of the countries studied. Moreover, in some countries where there is a heavy female disadvantage in enrollment (Egypt, India, Morocco, Niger, and Pakistan), wealth interacts with gender to exacerbate the gap in educational outcornes. In India. for example, where there is a 2.5 percentage ponit difference between male and female enrollment fcr children from the richest households, the difference is 34 percentage points for children from the poorest households.

The education level of adults in the household has a significant impact on the enrollment of children in all the countries studied, even after controlling for wealth. TIhe effect of the education level of adult females is larger than that of the education level of adult males in some, but not all, of the countries studied.

* The presence of a primary and a secondary school in the community has a significant relationship with enrollment in some countries only (notablv in Western and Central Africa). The relationship appears not to systematically differ by children's gender.

- This paper -a product of Poverty and Human Resources, Developrm-ent Research Group was prepared as background to, and with support from, a World Bank Policy Research Report on gender and development. Part of the study was funded by the Bank's Research Support Budget under the research project "Educational Enrollment and Dropout" (RPO 682-1 1)
in the household, community wealth index) and their interactions with gender, in addition to fixed effects for survey month and year. Each panel is a separate regression estimate. Standard errors, clustered by survey year _×_ psu shown in parentheses. ***: p _<_ 0.01; **: p _<_ 0.05; *: p _<_ 0.1.

This suggests that garment firms do some employment adjustments just around the time of minimum wage hike, hiring more workers in the months preceding the change, presumably to finish the orders committed to their clients, and take an employment cut in the first few months of the increased minimum wage regime. Within the third quarter, however, they are able to maintain the same level of employment as before.

The spike in garment sector employment is offset by opposing changes in self-employment, particularly in the agricultural sector. In the two quarters of the hike, self-employment goes up by about 3 percentage points (6 percent) for all workers, and 2.2 percentage points (4 percent) for female workers (column (4), Table 1). Self-employment also falls back to its pre-hike levels after the third quarter.
- N. Kabeer. Women, wages and intra-household power relations in urban bangladesh. _Development and Change_ , 28(2):261–302, 1997.

- N. Kibria, United Nations Research Institute for Social Development, and United Nations Development Programme. _Becoming a garments worker: The mobilization of women into the garments factories of Bangladesh_ . United Nations Research Institute for Social Development New York, 1998.

- J. Kim, G. Ferrari, T. Abramsky, C. Watts, J. Hargreaves, L. Morison, G. Phetla, J. Porter, and P. Pronyk. Assessing the incremental effects of combining economic and health interventions: the image study in south africa. _Bulletin of the World Health Organization_ , 87(11):824–832, 2009.

- S. Kishor and K. Johnson. _Profiling domestic violence: a multi-country study_ . MEASURE DHS+, ORC Macro, 2004.

- M.A. Koenig, S. Ahmed, M.B. Hossain, and A.B.M.K.A. Mozumder. Womens status and domestic violence in rural bangladesh: individual-and community-level effects. _Demography_ , 40(2):269–288, 2003.

- C.B. Lloyd, J. Young, and Population Council. _New Lessons: The Power of Educating Adolescent Girls: a Girls Count Report on Adolescent Girls_ . Population Council, 2009.

- N. Luke and K. Munshi. Women as Agents of Change: Female Income, Social Affiliation and Household Decisions in South India. _Journal of Development Economics_ , 94(1):1–17, 2011.

- R. Macmillan and R. Gartner. When she brings home the bacon: Labor-force participation and the risk of spousal violence against women. _Journal of Marriage and the Family_ , pages 947–958, 1999.

- Kaveh. Majlesi. Labor market opportunities and sex-specific investment in children’s human capital: Evidence from mexico. 2011.

- A. Malhotra
|0.26|0.42|
|**_Firm Size_**||||
|Small(5 - 19)|0.54|0.07|0.49|
|Medium(20 - 99)|0.68|0.23|0.53|
|Large(100+)|0.70|0.24|0.52|
|**_Region_**||||
|Arusha|0.73|0.04|0.71|
|Dar es Salaam|0.61|0.18|0.49|
|Mbeya|0.47|0.04|0.43|
|Mwanza|0.36|0.16|0.24|
|Zanzibar|0.73|0.08|0.69|
|**_Total_**|0.59|0.13|0.50|
|Source: 2015 Tanzania Enter<br>Note: The column “High-Sk<br>Exporters, asInnovators oras|prise Skills Survey<br>ill firms” includes fir<br> bothconcurrently.|ms that are de|fined either as|

Table 2 presents unconditional pair-wise correlations (and their statistical significance) of selected key variables that will be the focus of the analyses. The correlations relate exporter and innovator status (a proxy for skills demand), education and skills composition, in-service training, and summary measures of skill problems and job vacancies. These unconditional correlations reveal important relationships between different variables, as described below. These will be further 19
Second, we must separate the role of the CF subsidies from that of unobserved local attributes. The CF is a subsidy to promote entry into lagging regions. Regions with high subsidies are therefore likely to be unattractive for entry in a variety of other dimensions. Data to control parametrically for these other factors are incomplete, creating a problem of (negatively) correlated unobservables. We exploit useful features of the RAIS data set to control for this problem with agglomeration fixed effects.

Finally, in determining the role played by other factors in the entry decision (e.g., classical determinants like transportation cost and market size, along with agglomeration effects and other forms of local spillovers), we are confronted with problems of severe multicollinearity. In Brazil, development (as measured by the IDH) is highly correlated with measures of education, infrastructure (e.g., sewage, electrification), and even transport accessibility. This will limit our ability to deduce the separate causal role of many of these factors.

In dealing with these complications, we undertake a multi-stage estimation procedure. The following sub-sections describe each of these stages. The next section provides a discussion of the results.

# **_Stage #1:  Maximum Likelihood Entry Model_**

In the first stage, we recover non-parametric estimates of each agglomeration’s overall attractiveness for entry (separately for each sector), taking into account the possibility that a firm might consider the proximity of the new site to its headquarters.

13