rafmacalaba/data-use-mentions-extended huggingface dataset:
on unemployment was far less than in the US and UK context.

Compared to the voluminous literature from high-income countries, there is scant evidence from the developing world on the labor market impacts of COVID-19, largely due to lack of data. Studies in high-income countries use a variety of data sources, ranging from surveys to government administrative data sets, from private sector transactions to data from social media and search engine companies. With some exceptions, work in developing countries needs to rely on survey evidence that would have to be collected for the specific purpose of studying the crisis and addresses specific questions. Recent work has found substantial impacts on employment and energy consumption in India (Beyer, Bedoya and Galdo, 2020; Deshpande, 2020; Dhingra and Machin, 2020; Lee, Sahai, Baylis and Greenstone, 2020), on family businesses in Nigeria (Avenyo and Ndubuisi, 2020), and simulated aggregate consumption in Uganda (von Carnap et al., 2020). While these papers consider individual countries and various specific situations in these countries, our data allow us to consider a much wider set of countries and to compare countries to each other.

Several sets of papers offer regional or even global assessments of the impacts of the crisis on different dimensions, often based on firm surveys, simulations, or Google search or mobility data. For example, Adian et al. (2020) find that small and medium enterprises in 13 countries were more affected by the crisis than larger firms. Apedo-Amah et al. (2020) confirm this using data from a wider set of countries, and also find that most adjustments occurred on the intensive margin of hours reductions or temporarily work stoppage. Bachas, Brockmeyer and Semelet (2020) simulate the shock on firms using administrative data from 10 countries and predict an annual payroll reduction of 5%-10%.

A smaller set of global studies explicitly considers distributional effects. Bargain and Aminjonov (2020) document that poorer regions across nine countries in Latin America and Africa were less likely to
populous cities in each country in addition to all the cities in the world with population greater than 500,000. Second, we compute the shipment times between each city-pairs using the shortest path algorithm applied to the existing network of railways and the location of major ports using data from Atlas of the Earth (DAE), 2015 release, and Global Shipping Routes (CIA, ESRI, 2012) respectively. Finally, we identify the effect of BRI infrastructure projects on shipment times at city-pair level by applying the shortest path algorithm to an improved network that includes manually digitized information on BRI projects based on Reed and Trubetskoy (2018).

The BRI transport infrastructure investments considered in this analysis are those related to new or improved rail and port projects.<sup>14</sup> To obtain more accurate time estimates, we complement the georeferenced data with proxies for port quality using data from Slack, Comtois, Wiegmans and Witte (2018) on the amount of time spent in port by vessels. Additional data on

> 14 See de Soyres et al (2018) for the full list of projects.

15
2

# **1 Introduction**

The aim of this paper is to analyze if, and in what way, mass violent conflict affects fertility. It is well known that shocks can have significant effects on fertility, with the nature of the shock driving the nature of the impact. In the case of mass violent conflicts, very little is known about their effects on fertility. Hence we aim to understand the effects of off-spring and sibling mortality, widowhood and sex imbalance on short- and long-term changes of fertility in the post-conflict period. This paper – a product of the Gender and Development Unit, Poverty Reduction and Economic Management Network – is part of a larger effort in the department to study gender differentials in response to conflict, with generous funding from the Government of Norway.

Our analysis contributes to two literatures. First, it teaches us how conflict affects individual human behavior and well-being, which is a small but growing area of research. Second, we learn more about the drivers of fertility in times of sudden structural change. The occurrence of mass violent conflict can be seen, from a methodological perspective, as an opportunity to analyze fertility responses to aggregate and individual shocks, and to understand fertility as a coping strategy. Given the importance of relative population shares of various ethnicities in conflict-prone societies and given the importance of fertility for individual and household well-being in very poor economies, understanding the relationship between conflict and fertility is also important from a policy perspective.

We study the specific case of the 1994 Rwandan genocide, using three waves of Rwanda Demographic and Health Surveys (RDHS) collected in 1992, 2000 and 2005. Our research strategy includes cross-sectional analyses of the post-genocide data as well as analyses of pooled data from before and after the genocide. This allows us to compare the determinants of fertility for sub-samples of women of the same age groups across the survey years. The most challenging identification of an effect of conflict is at the micro-level – especially if the survey does not ask about an individual’s exposure to
carried out the learning curriculum in grades three, four, and five, and assisted with strengthening School Management Committees and community support for girls’ education. Appendix A presents additional details about the intervention and implementation.

# **3. Research Design**

# **3.1 School Sample and Randomization**

The study consists of 230 primary schools located in 98 villages in Rajasthan. In four administrative blocks, villages with at least one government primary school were selected for the study. In 2011, prior to the implementation of the program, researchers randomly assigned villages to either treatment or control groups (49 treatment and 49 control), stratified by Administrative Block, using a random number generator. This results in 117 treatment schools and 113 control schools. On average, there are 2.3 government primary schools per village.

# **3.2 Data and Outcome Measures**

# _Baseline Data_

We use data collected in 2011 – prior to the implementation of the program – to check for baseline balance and as covariates in the analyses. At the school level, we use school infrastructure data (such as the presence of electricity and computers and the number of students at each school) collected by the NGO staff. At the individual level, enrollment rosters collected in 2011—prior to program implementation—list each student’s gender, grade, age and whether he or she belongs to a Scheduled Caste, Scheduled Tribe, or Other Backward Caste. We also construct school-gradelevel enrollment for boys and girls in 2011 that we use as controls.

10
of propensity scores—the probability of being enrolled in VHI.

Section II outlines the salient features of Vietnam’s health insurance program. Section III sets out our estimation method, based on a combination of propensity score matching and double differencing on pre-intervention and post-intervention data. Section IV outlines the model used to generate our propensity scores, and compares the distributions of the propensity scores of the insured and uninsured pre- and post-matching. Sections V-VIII contain our results for health outcomes, health utilization, out-of-pocket expenditures and nonmedical consumption, respectively. Section IX contains our conclusions.

# II. **VIETNAM’S HEALTH INSURANCE PROGRAM**

Prior to the _Doi Moi_ (or ‘renovation’) reforms of the late 1980s, health services in Vietnam were provided at the taxpayer’s expense, with patients paying only the cost of drugs (Bloom 1998). The decollectivization of agriculture and the liberalization of the economy led to a swift erosion of the government’s role in financing health care in Vietnam. Following changes in 1989 government facilities were allowed to charge fees, and in 1991 legislation was passed that paved the way for a private health sector (Bloom 1998, Glewwe 2003). The share of the cost of health care borne directly by households is likely to have increased considerably following these reforms. This prompted concerns about the affordability of health care, especially among the poor, which in 1993 still accounted for 58% of the population (Glewwe 2003). Subsequent analysis of data from the 1993 Vietnam Living Standards Survey (VLSS) bore these concerns out: a single visit in 1993 to a public hospital by someone from the bottom quintile would have cost the equivalent of 41% of their _annual_ non-food consumption (World Bank et al. 2001). Even for the population as a whole, the figure was close to 20%.

3
of GDP, logs)_|(0.0853)|(0.0461)|(0.00665)|(0.0930)|(0.0566)|(0.0230)|(0.00474)|(0.0598)|
|Financial Openness|0.00922|-0.0197*|-0.000277|0.0263|-0.00583|-0.0124*|-0.000146|0.00672|
|_(Chinn‐Ito Index)_|(0.0268)|(0.0104)|(0.00150)|(0.0285)|(0.0198)|(0.00651)|(0.00136)|(0.0204)|
|_External Factors_|||||||||
|Foreign Growth|0.0219**|0.000746|-0.000204|0.0206**|0.0206***|-0.000373|-0.000177|0.0194***|
||(0.00856)|(0.00333)|(0.000466)|(0.00926)|(0.00696)|(0.00240)|(0.000501)|(0.00718)|
|VIX Index|-0.110*|0.0129|-0.00872*|-0.105|-0.0782|-0.0284|-0.00872*|-0.0556|
||(0.0582)|(0.0360)|(0.00516)|(0.0644)|(0.0614)|(0.0240)|(0.00494)|(0.0648)|
|US Economic Policy|0.00176***|-0.000188|5.95e-05|
motivations for participating, their attitudes towards civic participation, and the nature of CBO activities. The end-line survey differed slightly for treatment and control CBOs as it aimed to collect data to allow us to gauge the awareness of control CBOs about OQP and whether or not this had influenced their activities, in order for us to construct a measure of potential spillovers.

The baseline survey was carried out in July 2015.<sup>20</sup> The end-line survey was carried out during November 2016. The attrition rate was low. Out of the 2,400 households surveyed at baseline, only 115 could not be included in the end-line survey (an attrition rate of less than 5%).

# _Baseline characteristics_

Table 4 presents the average values for a range of baseline household characteristics for the treatment group and the control group, along with the difference between groups and the p- value of a t-test of whether this difference is statistically significant at conventional levels (pvalue < 0.10; this is identifiable by the presence of one or more stars next to the p-value).

The first set of variables relates to the characteristics of the household head. Approximately 70% of household heads are male and the vast majority (96%) are Muslim. Most are married

> 19 Phase 1 covers Dalifort-Thiouorour (communes of Wakhinane Nimzatt, Djeddah Thiaroye Kao, DalifortFoirail, Hann Bel Air and the Western halves of Yeumbeul Nord and Yeumbeul Sud) and Phase 2 covers Yeumbeul-Mbeubeuss (Keur Massar, Malika and the Eastern halves of Yeumbeul Nord and Yeumbeul Sud). 20 The baseline survey took place before neighborhoods were assigned to the treatment and control groups, to avoid any anticipation effects from CBOs and households.

11
seminars in Queens University Belfast, Groningen University, University of Copenhagen, Graduate Institute Geneva, and Copenhagen Business School. We thank Molly OfferWestort, Violaine Pierre, Felipe Dunsch, and Ababacar Gueye for invaluable assistance with project coordination and data collection. Our thanks also to our research assistants Margaryta Klymak, Michell Dong, and Qiao Wang. This work benefitted from generous funding from the UK/DfID through the i2i Trust Fund, the Bank-Netherlands Partnership Program, the Nordic Development Fund, and the World Bank. The RCT was registered on the AEA RCT Registry on July 7, 2015 (AEARCTR-0000754). Computational reproducibility was confirmed by DIME Analytics.
; Ariely, 2016), but the agricultural development community has been slow to do so.

Second, we know little about whether and how farmers incorporate objective information to correct mistaken beliefs, although this surely matters for information-based interventions, such as agricultural extension programming, cadastral surveys, and market information services. If there exists heterogeneity or asymmetries in learning – i.e., the updating of reported beliefs – or learning

> 1 Berazneva et al. (2018), Burke et al. (2020), Wineman et al. (2020), Michelson et al. (2021) and Wossen et al. (2021) provide similar evidence of misperception in soil quality, plot size, crop variety, chemical fertilizer quality, and crop variety, respectively.

2
||enrollment rate values for the fiscal year<br>covered by the Enterprise Survey were taken.<br>In cases where the fiscal year spanned more<br>than one calendar year, the enrollment figure is<br>the weighted average over the calendar years<br>covered, where the weights used are the<br>proportion of months covered in the calendar<br>year. In the second step, annual values were<br>taken from the first step and averaged over the<br>three years prior to the (final) year covered by<br>the Enterprise Survey. Three years average<br>was taken to avoid too many missing values of<br>secondary enrollment rate. The final figure is<br>divided by 100 to avoid too small coefficients<br>in the regressions.<br>Source: World Development Indicators,<br>World Bank.|
|---|---|
|Rule of Law (lagged)|Rule of Law indicator from Worldwide<br>Governance Indicators. Higher values of the<br>variable imply better enforcement of rules and<br>laws. The variable is lagged by 2 years from<br>the fiscal year covered by the Enterprise<br>Survey in the country. In cases where the fiscal<br>year spanned more than one calendar year, the<br>Rule of Law figure is the weighted average<br>over the calendar years covered (lagged by 2<br>years), where the weights used are the<br>proportion of months covered in the calendar<br>year.<br>Source: Worldwide Governance Indicators,<br>World Bank.|
|Freedom From Regulation (lagged)|<br>Freedom from Regulation indicator from<br>Fraser Institute’s Economic Freedom of the<br>World database. Higher values of the variable<br>imply less regulation (of formal
1|0.307|0.008|0.313|0.000|
|_βT C_ ⩽_−_1|0.000|0.000|0.000|0.000|
|_βT C_ ⩾0|0.658|0.066|0.239|0.347|
|Countries|94|94|103|102|
|Observations|921|819|1024|912|

Note: fixed effects (FE) and first-differenced OLS (FD) results, annual data, 1990-2003, reduced sample. All regressions include time dummies, coefficients not reported. Heteroskedasticity-robust standard errors, clustered by country, in brackets. _∗_ , _∗∗_ , and _∗∗∗_ denote significance at 10, 5 and 1%, respectively. In the case of FE estimation, _R_<sup>2</sup> refers to the within _R_<sup>2</sup> . Hausman shows the p-value of a generalized Hausman test of the null hypothesis that _ηi_ is uncorrelated with the regressors. _βSP_ ⩽ 0 ( _βSP_ ⩾ 1) and _βT C_ ⩽ _−_ 1 ( _βT C_ ⩾ 0) are p-values for the test of full (no) fungibility for sector programme aid and technical cooperation, respectively. Source: Author’s analysis based on data described in the text.

20
by three reference households (consisting of children and people of working age, in good health, without disabilities, and living in Bucharest<sup>16</sup> ). The basket includes a food budget and kitchen equipment required to prepare, serve, consume, and preserve this food. Furthermore, it considers the necessary budget for physical activity and other food functions, such as its social function. The food basket has been developed per nutritionist recommendations and national dietary guidelines, considering the cultural specificities of Romanian eating habits. The feasibility and acceptability of the basket have been verified through focus group discussions involving citizens with different socio-economic backgrounds. Estimates show that in 2015, the monthly budget required for a healthy diet in Romania was EUR 128 for a single person and EUR 499 for a family of two adults and two children. If the budget needed for physical activity and other food functions (eating out, holidays, etc.) is also considered, the total monthly food budget amounted to EUR 156 for a single person and EUR 578 for a family of two adults and two children.<sup>17</sup> When converted to 2020 Euros using official food CPIs, these monthly food expenditures correspond to 181 EUR for a single person (able or unable to work) and EUR 670 for a family of two adults and two children. These estimates suggest that the average monthly per capita food basket cost in 2020 euros is approximately 126 EUR and 180 EUR when considering non-food components/other functions. This is equivalent to 4.1 EUR per capita per day and 5.9 EUR (considering other functions). Table 1 presents these estimates.

**Table 1. Estimates of Food Basket based on European Commission (2015), 2015 and 2020 euros.**

**Panel a. By Household composition (2015 euros)**

||**In 2015**<br>**Euros**|
|---|---|
|Single Person
specific sub-components of generic management scores and energy intensity in value and quantity terms, we find that the practices that matter most seem to differ for manufacturing and services sectors. These practices also influence energy intensity in expenditure and physical units differently. Specifications (1)-(3) in Table 4 show the relationship between management sub-scores (target-setting, monitoring, operations and incentives) and energy intensity in value terms. While target-setting practices matter for reducing energy intensity (expenditure) in manufacturing, these practices have statistically insignificant correlation for energy use in services. None of the other management practices seem to matter for energy use in services. Our results differ from Boyd and Curtis (2014) for the United States, who document that effective monitoring, incentive structures and particularly lean manufacturing operations are associated with a reduction in energy use, while the implementation of production targets (conditional on other practices) is found to increase energy consumption. The reason may be due to the fact that the World Management Survey, which was used to measure management practices in the United States in Boyd and Curtis (2014), refers to production targets, whereas the WBES definition is broader and explicitly includes examples such as “production volume, quality, efficiency, waste, or on-time delivery” in the question on target-setting practices. Appendix Table A10, columns (1)-(5), reveals that the target-setting practices matter especially for moderate and low energy intensive firms.<sup>15</sup> Specifications (4)-(6) in Table 4 show that there is a positive relationship in manufacturing firms between monitoring practices and energy intensity in physical quantity terms. While target-setting improves energy efficiency in value terms, monitoring production and performance of firms is actually associated with a worsening of the physical energy intensity in manufacturing, perhaps because more energy-intensive firms are more motivated to invest in monitoring capabilities. As for services, we do not find evidence of association of management with energy efficiency, both in value and in physical unit terms.

>
# **Table 4.  The Impact of Conflict-Related Shocks by IPC Classification**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|ln (FCS)|ln (rCSI)|Indicator<br>for Water<br>Source<br>Being<br>Insufficient|Indicator<br>for Using a<br>Primitive<br>Energy<br>Source|Indicator<br>for No<br>Municipal<br>Garbage<br>Collection|Indicator for<br>Experiencing<br>3 or More<br>Deprivations|
|Post Blockade Indicator|-0.031|0.176**|0.048|0.009|0.023|0.065**|
||[0.021]|[0.072]|[0.028]|[0.014]|[0.024]|[0.027]|
|Interaction Between IPC Status and the Post<br>Announcement Indicator|||||||
|____________________<br>Post Announcement Indicator*IPC 4 Indicator|-0.014|-0.009|0.033|0.064**|0.062|0.078*|
||[0.043]|[0.111]|[0.037]|[0.029]|[0.058]|[0.045]|
|Observations|2,422|2,422|2,422|2,422|2,422|2,422|

Note: This table estimates how households were impacted by the complete air and sea blockade that began during November 2017. The post blockade indicator
|1713|10|51|2570|
|Mauritius|5|14|737|6|14|689|
|Mozambique|7|23|1100|10|30|1475|
|Namibia|11|29|1686|9|24|1813|
|South Africa|8|30|1531|9|35|1807|
|Swaziland|9|21|2184|11|33|2249|
|Tanzania|5|24|1262|7|31|1475|
|Zambia|6|53|2664|9|64|3335|
|Zimbabwe|7|53|3280|9|73|5101|

Source: World Bank Doing Business Survey. http://www.doingbusiness.org/ExploreTopics/TradingAcrossBorders/ [Accessed 10 June 2010] Yet simple unconditional cross-country comparisons such as these do not take into account the interdependent relationship between geography and trade costs. African countries are often far from developed markets and many countries are landlocked, all of which raise internal and external transport costs (Behar and Venables, 17
discipline. Typically we would expect the effect of the change in capital to be smaller for banks which have higher initial leverage ratios (i.e. Capital/Total assets). Correspondingly, we expect the estimates of the a3 and b3 coefficients to be negative, proving that the effect of the change in capital decreases when the ratio of capital over total assets increases.

Due to the panel structure of regressions (6) and (7), we cannot expect to catch the specific effects associated to undercapitalized banks or banks owned by foreign Western investors by means of specific dummies. For this reason we have integrated our panel estimations with cross section regressions, where banks' fixed effects have been replaced by the control variables _Dlow_ and _Djb._ The first variable regards undercapitalized banks suffering a drop in total assets (here the dummy _Dlow_ = 1)2 which represent 35% of the crisis-sample banks (Table 4). The second variable concerns banks that are owned by foreign Western investors (here the dummy Djb = 1), which represent one fourth of our sample (Table 4). For the reasons outlined above, these banks may cope better with the regulatory restriction.

The methodology embedded in equation (6) and (7) does not deliver equally clear results for non-crisis countries. There is, in fact, no guarantee that negative capital shocks will prevail for banks in countries that are enforcing CARs during a non-crisis period. Any attempt to select banks exposed to negative capital shocks falls into a small sample problem, given that the non crisis sample includes 146 banks, only 37.5% of which has

> 19 Source: International Financial Statistics by the IMF.

> 2 0 The dummy _Diow_ takes the value 1 if the bank has a capital asset ratio lower than the country median the year before the enforcement, 0 otherwise.

13