rafmacalaba/data-use-mentions-extended huggingface dataset:
0.61)|(6.45)|(4.61)|
|Obs.|11267|4228|11286|4222|

Note: The table gives the mean for widows less that for non-widows. Ever-widowed includes currently widowed as well as previously widowed, currently married women. t-stats in parentheses. Source: 2006 Mali DHS.

**Table 9: Percent of women who reported obtaining permission and lack of money as impediments to seeking health care for themselves when sick, by marital status**

||**Permi**<br>|**ssion**<br>|**Mo**<br>|**ney**<br>|
|---|---|---|---|---|
||**Rural**|**Urban**|**Rural**|**Urban**|
|**Married once**|20.2|13.1|59.0|39.1|
||(40.2)|(33.8)|(49.2)|(48.8)|
|**Married, previously widowed**|25.2|29.4|65.5|60.7|
||(43.5)|(45.8)|(47.6)|(49.1)|
|**Married, previously divorced**|17.4|10.5|54.4|35.5|
||(38.0)|(30.6)|(49.8)|(47.9)|
|**Widowed**|18.4|10.1|80.5|62.7|
||(38.9)|(30.3)|(39.8)|(48.
.005_|0.509|_0.005_|0.459|_0.006_|
|**_All_**|0.532|_0.004_|0.455|_0.004_|0.522|_0.004_|0.489|_0.005_|

_Source_ : NFHS I and II.

> 28 Children aged 0-4 in the 1992 survey, and 0-3 in the 1998 survey.

19
Pritchett (2001), using the first principal component that weights more heavily those assets that vary the most across households. An asset common to all households receives zero weight, as it explains none of the variation across the households. Intuitively, commonly held assets are also likely to be of lower value. Accordingly, the resulting indices would also reflect some underlying monetary valuation. In order to make comparisons over time, I pooled the data for 1998 and 2003 to calculate the first principal component. This created constant weights that explained any variation across time, as well as across households.

The asset indicators common to both KMS rounds can be divided into housing quality and ownership of durable goods. There were 11 indicators for housing quality, encompassing different types of homes (5 separate indicators for various building materials and the number of rooms), cooking methods (4 unique indicators), use of electricity, and presence of a landline telephone. The KMS surveys categorized home types into five categories: (i) luxurious (three or more bedrooms with attached bathrooms, concrete roof, mosaic floor), (ii) very good (two bedrooms with attached bathrooms, concrete roof, mosaic floor), (iii) good (one bedroom, brick or cement walls, concrete or tile roof), (iv) poor (brick walls, cement floor, tin or asbestos roof), and (iv) _kutcha_ (mud walls, mud floor, thatched roof). For ownership of durable goods, there were 6 indicators, covering ownership of a car, taxi or truck, scooter, television, VCR, and refrigerator.

I created a separate index for each of the two categories of asset indicators—broadly, housing quality and durable goods. I then added an overall assets index, in which all 17 indicators are used together. Each of the two specialized indices has a correlation of 0.9 or more with the overall assets index. The first component accounts for 26 percent, 40 percent, and 27 percent
# **6 Annexes**

## **1 – The Social Accounting Matrix**

The Social Accounting Matrix for Nicaragua has been estimated using the 2000 input output table provided by the Central Bank of Nicaragua as a starting point. The Central Bank base year for the national accounts is 1994 and the latest update for the input output table is the year 2000.

Given our interest in income distribution and trade, the input output table has been modified to include more factors of production and multiple trading partners. We have disaggregated the value added accounts and estimated payments from the various production sectors to three separate factors: skilled labor, unskilled labor and composite capital (which includes payments to land). The sectoral employment and average wages for these factors have been derived from the 2001 LSMS survey and the classifications of sectors used in the survey and the input output table have been merged in a consistent common classification shown below in the list of the SAM accounts. Using the household survey to modify the value added reported in the input output table is a crucial step given that the main link between the macro and micro modules is given by the sectoral factor returns. Inconsistencies in the estimation of value added between the input output and the household survey – each uses different definitions, sampling, and reference moments in time (recall for instance that the National Accounts are based on some technical coefficient estimated for the year 1994) – cannot be fully eliminated. However, our simple ‘reconciliation’ approach assures a minimum of compatibility across the two data source so that our macro-micro model can produce sensible results.

The imports and exports accounts as well as the tariff rates have been estimated using the UN Comtrade database for different trading partners, including the U.S., the rest of Central America, the European Union and others (although in the model version used here, we just distinguish between the U.S. and the Rest of the World). Tariff rates for the commodities included in the model have been calculated as
Estimating the above equations is challenging because of endogeneity driven by the fact that the export decision is not made at random. Reverse causation is a factor when considering the relationship between export status and employment. Indeed, firm size has been cited as an important determinate of exporting (e.g., Söderbom and Teal 2000, 2006). Conditional on exporting, unobserved factors such as bargaining conditions or technology potentially affect how much a firm exports (Abbey et al. 2017). These unobserved variables are captured in and i . ii While fixed effect estimation eliminates the first, time-invariant, source of error, it does not address μμ εε time-varying sources of endogeneity.

Guided by the empirical literature, we address these endogeneity issues by estimating the models by Fixed Effects—which eliminates time-invariant sources of bias—and the Heckman Correction to address sample selection bias (Heckman 1974). That is, we estimate equation (5.1) to obtain the inverse Mills ratio and include it in equation (5.2) as an additional control. In the second stage, we restrict attention to those firms that export and include as controls the value of machinery, the cost per worker, age of the firm, and the inverse Mills ratio. For details of the Heckman Correction, see Appendix B.

- **_5.3 Main Results_**

This section reports the results of the econometric analysis of the 2013-2016-2020 panel of 198 firms. Below, we review the findings from the extensive margin to better understand what characteristics make a firm more likely to export; and on the intensive margin, focusing on export sales as the left-hand variable of interest. Next, we explore the relationships between the change 20
# **References**

Adida, C.L., Ferree, K.E., Posner, D.N. and Robinson, A.L. (2016). Who’s asking? Interviewer coethnicity effects in African survey data. Comparative Political Studies, 1–31 Anker, R., Khan, M. E., and Gupta, R. B. (1987). Biases in measuring the labour force: Results of a methods test in Uttar Pradesh. International Labour Review. 126, 151–67 Axinn, W.G. (1989). Interviewer and data quality in less developed setting. Journal of Official Statistics, vol. 5, 265-280 Baird, S. and Özler, B. (2012). Examining the reliability of self-reported data on school participation. Journal of Development Economics, vol. 98, Issue 1, Pages 89-93 Bardasi, E., Beegle, K., Dillon, A., Serneels, P. (2011). Do labor statistics depend on how and to whom the questions are asked? Results from a survey experiment in Tanzania. World Bank Economic Review, 25 (3), pp. 418-447 Barrera-Osorio, F., Bertrand, M., Linden, L. and Perez-Calle, F. (2011). Improving the Design of Conditional Transfer Programs: Evidence from a Randomized Education Experiment in Colombia. American Economic Journal: Applied Economics, 3(2): 167–95 Beaman, L. and Dillon, A. (2012). Do household definitions matter in survey design? Results from a randomized survey experiment in Mali. Journal of Development Economics vol. 98, 124-135 Beegle, K., De Weerdt, J., Friedman, J. and Gibson, J. 2012. Methods of household consumption measurement through surveys: Experimental results from Tanzania. Journal of Development Economics, vol. 98(1), pages 3-18.

Benstead, J.L. (2014). Does
underlying riders’ demand for the reserved space. Section 7 tests for norms against women riding in the public space as a mechanism underlying a fixed-amenity value of the reserved space. Section 8 discussed robustness to alternative mechanisms, and Section 9 concludes.

# **2 Study Context**

We study sexual harassment on the public transit system of Rio de Janeiro, Brazil. Sexual violence on the transit system is pervasive in Brazilian cities. A recent survey in S ̃ao Paulo suggests that public transport is the most common place where women suffer harassment and 35 percent of women respondents reported ever being sexually harassed while using public transport (Datafolha, 2015).

Issues of sexual harassment on the transit system have led Rio de Janeiro state government to pass legislation to reserve a space for women in its rail system. The 2006 law requires the train and metro operators to reserve one carriage in each train for women during rush hours (6-9AM and 5-8PM).<sup>5</sup> Rio de Janeiro’s public transit system connects many low-income families to economic opportunities: most low-income households reside in the periphery, while jobs are concentrated in the city center (Motte et al., 2016). Rio’s metropolitan area has an extensive public transport system that includes bus, metro, a suburban rail, bus-rapid-transit and ferry system. Commutes are long, with a 95-minute average transit time (Moovit, 2018).

In order to capture the behavior of households living in the periphery, and for whom commuting to opportunity is particularly critical, we focus on Rio’s suburban rail system, the SuperVia. This system comprises seven lines that connect downtown Rio with its outskirts, including many low-income areas. All lines radiate out of the central station, Central do Brasil (cf. Figure A1 for a map of the SuperVia network). The SuperVia carries around 700,000 passengers a day, or 10 percent of all public transport trips in the Rio metropolitan

> 5Lei No 4.733, de 23 de
how they correlate with any discrepancy between ESPS and PSF total household consumption aggregates.

Nevertheless, two conjectures can be made. First, if the PSF design is better able to apprehend food consumption than that of ESPS and if household or surveyor fatigue does not impact too much the ability to capture consumption of food commodities, then one can expect to find a higher level of food consumption in the PSF survey. Moreover, since in more than 80% of households all members take their meals together, the gap between surveys should not depend on the household structure. Second, for total household consumption, one cannot conjecture whether any difference should be observed on average between surveys, but we may expect the ratio of PSF to ESPS household consumption for a given household type to increase with the complexity of the household structure.

Testing these conjectures is not straightforward. Contrary to PSF, the ESPS survey has not been designed to capture the entire complexity of Senegalese households. One can only rely on the usual “relationship to head” question to recover, in as much as possible, the household structure and identify the number of budget decision units. The basic strategy is to count the number of household members of the different types, as defined by their relationship to the head: spouse, child, grand-child, parent, grand-parent, brother/sister, nephew/niece, other parent, unrelated etc. and then to compare that number to the household size. For instance, a simple two cell household can be constituted of a male head, his (unique) spouse and his children. The children can be those of the spouse, in which case they belong to her cell, or they can be born from another marriage, in which case they belong to the head’s cell. In any case, households with the head, one spouse and children and with no other members are two cell households. The difficulty is that these nuclear families are not the only kind of two cell households, but they are the only
We then apply panel econometric methods to provide estimates of war effects on two outcome variables, namely (i) the extent of area grown with winter crops; and (ii) for winter crop fields, peak NDVI as a predictor of crop yield, both at VC level. Estimates of direct effects are obtained from the coefficients on war-related disturbances (damaged agricultural fields or conflict indicators) that we include as independent variables. Village fixed effects are then used to control for time invariant effects (e.g., soil quality, infrastructure access, or social cohesion and effectiveness of local leadership) and data on precipitation and accumulated temperature control for time varying effects, a 2022 dummy can be interpreted as an upper bound estimate of indirect conflict effects.

Our key finding is that direct conflict effects are relatively modest but that they are dwarfed by indirect ones: Conflict reduced the area grown with winter crops by up to 0.755 million ha, 88.4% of which due to indirect effects. On winter wheat fields, changes in NDVI suggest a conflict-induced drop in yield of up to 1.5%, a loss of 4.7 mn. t of which 4.4 mn t. are attributable to indirect and 0.3 mn. t to direct effects. Findings are robust across a range of alternative sensors (Landsat and MODIS). Also, expected winter wheat yields drop most in rayons with a larger share of small producers, suggesting that conflict hurts small farmers most given their more limited access to diversification options.

Our findings relate to and contribute to three strands of literature. First, while a large body of studies discusses the link between conflict and food security, the case most prevalent in developing countries is one where shocks to food production–or the value of agricultural endowments more generally–trigger conflict. We show that a case like of Ukraine where causality runs from conflict to food production, can be analyzed using satellite imagery to generate measures of agricultural production as outcome variables as well as
and include the date, time, location (longitude and latitude) of the attack, and basic characteristics of the vessel being attacked, such as type, gross registered tonnage (GRT), etc. The IMB data broadly classify the outcome of the attack into “attempted”, “boarded”, “detained”, “fired upon”, “hijacked”, “missing”, and “suspicious” categories. We define those attacks that ended in hijacks as “successful” attacks.

The second source is the data on ransoms. Information on ransoms comes from the joint data set compiled by the U.N. Office of Drugs and Crime and the World Bank (thereafter referred to as the UNODC-WB data set). The data are compiled from open-source information such as newspaper and reports from national and international law enforcement agencies. Some observations were obtained from interviews with the law firms directly in charge of ransom negotiations. The data set covers 233 vessels hijacked by Somali pirates between April 2005 and December 2012, and contains information on vessel and incident characteristics such as vessel name and date of which allows us hijacking, to match observations with the IMB database. In addition, the data provides information on the most recent (up to December 2012) status of the vessel (whether released, captive, liberated, or sunk), the amount of ransoms paid and the length of the negotiation.

# **4.2 Estimation**

We jointly estimate the model parameters with the method of simulated moments following the ideas outlined in McFadden (1989). There are 23 structural parameters in the model, which we collectively denote as the Θ vector. Our estimation strategy consists of finding the vector Θ that minimizes the weighted distance between a set of simulated moments and their counterparts in the data. Specifically, Θ is defined as: where _n_ = 1 _,_ 2 _, · · ·_ 57 indexes the moments to be matched, and _j ∈{_ 1 _,_ 2 _,
A. (2018). Higher education and prosperity: Catholic missionaries to luminosity in India. _The Economic Journal_ , _128_ (616), 3039–3075.

- Chandler, T. (1987). _Four thousand years of urban growth_ . Lewiston, NY, Edwin Mellen Press.

- Chen, Y., Wang, H., & Yan, S. (2014). _The long-term effects of Protestant activities in China_ (MPRA Paper No. 53531). Munich University Library. https://mpra.ub.uni-muenchen.de/53531/1/MPRA_paper_ 53531.pdf

- Conley, T. G. (1999). GMM estimation with cross sectional dependence. _Journal of Econometrics_ , _92_ (1), 1–45. https://doi.org/10.1016/ S0304-4076(98)00084-0

- Coppedge, M., Gerring, J., Lindberg, S. I., Skaaning, S.-E., Teorell, J., Altman, D., Bernhard, M., Fish, M. S., Glynn, A., Hicken, A., Et al. (2017). V-Dem Dataset v7.

42
those with no education.

**Fig 2: The estimated quit rates among wage workers in Ghana.**

<!-- Start of picture text --> 12 _ .---------<br>4) 1D , X<br>4) 2<br>0<br>0-1 3-4 6-7 9-<br>yr yr length of service yr 10<br>yr<br>-- x- 1987 1988 -0 _ 1991<br><!-- End of picture text --> Source: Authors' own calculations using GLSS 1,2,3.

**Note:** Quit rates are estimated using the years of experience. The quit rates for those with experience t, to t2 is estimated as the percentage of the labor force with atleast t, experience minus the percentage with atleast **t,** experience divided by the percentage with atleast **t,** experience.

24
results in three treatment subgroups—those who overestimated their household’s position on the income distribution, those who underestimated their household’s position, and those for whom their prior was confirmed. Note that households that responded to the inequality module had previously been interviewed for the CPHS several times. Hence, they were familiar with the scope and credibility of the survey.

After the treatment, the module collected information on beliefs about inequality. Respondents were asked, _“There can be a big gap between the rich and the poor in the country. Do you think this gap is a problem for society?_ ”, with responses coded yes or no. They were also asked, _“Do you think the government should do something to reduce this gap between the rich and the poor?”,_ with responses coded yes or no.<sup>6</sup> These questions mirror the survey-based measures of beliefs about inequality that have been extensively used by scholars (Alesina, Stantcheva, and Teso 2018; Cruces, Perez-Truglia, and Tetaz 2013; Hoy and Mager 2020; Kuziemko et al. 2015).

We use Equation (1) to estimate the average effect of the treatments on responses to these two questions. We estimate this average effect separately for the three treatment subgroups mentioned above. As a robustness check, we also estimate a version of Equation (1) that excludes all controls.

that excludes all controls. (1) i 0 1T T i 2 i 3 T i i ii ii ii ii<sup>+ XX</sup> ii<sup>γγ+ εε</sup> ii YY = ββ + ββ TT + ββ TTRRT RR + ββ SST Here, i is the outcome for respondent _i_ in district _j_ in state _k_ ; T ii ii<sup>is a vector of binary</sup> variables indicating the respondent’s state; YY i is a binary variable that equals one if the SST ii household was in a
**Table 2. Coefficient of variation of yield in the ten largest cereal producers and in the world, 1960—2012 (%)**

||Maize|Rice|Wheat|
|---|---|---|---|
|Argentina|6.02|5.52|7.81|
|Bangladesh|--|2.32|8.56|
|Brazil|3.63|2.32|10.89|
|Canada|5.40|--|8.41|
|China|3.70|2.62|3.14|
|European Union (27)|7.35|5.74|4.27|
|Indonesia|3.97|2.93|--|
|India|4.95|3.64|2.97|
|Russia|21.04|6.31|12.33|
|United States|5.84|3.43|4.97|
|World|2.82|1.29|2.32|

Source: Obtained after HP-filtering (smoothing parameter of 400) of original yield data from USDA (2012).

47
line. While there is a larger concentration of sampled clusters in and around major urban centers, the survey provides a complete coverage of the country including rural and more remote areas. Poverty rates are seen to vary considerably across space. On average however, poverty levels tend to be higher in more remote areas that located further away from the urban centers. Note that this does not imply that poverty is less of a concern in urban centers. In fact, the majority of the poor plausibly reside in urban centers due to the higher concentration of population there, i.e. despite the lower prevalence of poverty.

||2008 census|IHS3|
|---|---|---|
|# regions|3|3|
|# districts|32|31|
|# Wards/TAs|353|279|
|# EAs|12,575|767|
|# Households|2,859,720|12,271|

Table 2: Descriptive statistics of 2008 census and IHS3

# **3.2 Population census**

The enumeration for the 2008 Population and Housing Census for Malawi was carried out between June 8 and 28, 2008. It covers regular households, institutions, and the homeless. For the purpose of this exercise, institutions and the homeless are omitted. The total population was found to be 13,077,160. Table 2 reports the number of households in the census and number of sampled households in the survey at different levels of aggregation. The most disaggregated level at which poverty will be estimated is the Traditional Authority (TA) level, which we may also refer to as the‘small area level.

The administrative boundary data are taken from the National Statistical Office (NSO) of Malawi in order to match the boundaries with the census data at the enumeration area level. Malawi has 12,666 enumeration area boundaries from the 2008 census. The IHS3 household survey has 12,271 observations from 768 enumeration areas.<sup
# **2 Distribution of Deaths by Age**

COVID-19 mortality data were gathered from official government sources. See the Appendix for a complete listing. Data were used for countries with at least 100 reported COVID-19 deaths and for which a breakdown of mortality by age was available.<sup>3</sup> In nearly all high-income countries, people age 70 or older constitute the large majority of deaths attributed to COVID-19. For the set of high-income countries considered, on average 87 percent of COVID-19 deaths are of those age 70 or older. In contrast, in the low- and middleincome countries, on average just 37 percent are age 70 or older (Figure 1).

High-income countries have on average younger populations than low- and middle-income countries. Individuals over age 70 constitutes on average 15 percent of the population in the highincome countries analyzed in this paper vs. just 4 percent of the population in the low- and middleincome countries. To what extent can the wide variation in the age distribution of deaths be explained by the fact that high-income countries have substantially older populations who are more at risk from the disease? To consider this question, age-specific mortality rates were calculated by dividing the number of deaths attributed to COVID-19 for each 10-year age group by the population in each age group. Population data by age were taken from United Nations (2019), using the estimated populations corresponding to July 1, 2020. Because most countries group all COVID-19 deaths for those ages 80 and above in their reporting, 80+ is shown as a single category.

Next, for each country, a standardized population was created. The standard was generated using the mean population shares for each age group across high-income countries. Finally, a hypothetical distribution of deaths for each country was calculated by multiplying the standard population shares by the corresponding age-specific COVID-19 death rates. The resulting distributions are shown in Figure 2. A comparison of the true and hypothetical distributions is summarized in Table 1.

> 3 The data used in this paper are