rafmacalaba/data-use-mentions-extended huggingface dataset:
>with occurrence of respiratory<br>disease symptoms<sup>*</sup>|17.3|9.1|

Sources: Bensch and Peters (2015); Tijdens et al (2012); USAID (n.d.)

*Based on self-reported information of symptom occurrence over the past three months for at least one woman in the household involved in cooking.  Total number of observations = 227.

The drop in wood use is somewhat below the estimate obtained from controlled cooking tests (around 40%). This may in part reflect a typical difference between controlled and in-the-field behaviors. It could also reflect in part to a “rebound effect:” – with a more fuel-efficient stove in effect lowering the opportunity cost of cooking, households may elect to do more cooking (which could also explain the limited impact of the ICS on collection times). Support for this supposition

> 12 This rough calculation does not account for the difference between rural and national average incomes, reflecting higher incomes in urban areas.  On the other hand, the cost per household will be smaller than indicated in the text <u>given multiple income earners in the household.</u>

**14 |** P a g e
/Sci/Engg know.|3.23|Communication in English|3.95|
|Teamwork|3.46|Use of modern tools|3.15|Basic computer|3.34|
|Entrepreneurship|3.44|Creativity|3.08|Written Communication|3.22|
|Self-discipline|3.37|System design to needs|2.95|Verbal Communication|3.17|
|Willingness to learn|3.37|Contemporary issues|2.95|Technical Skills|3.13|
|Flexibility|3.29|Problem solving|2.87|Reading|3.08|
|Reliability|3.20|Customer Service|2.65|Advanced computer|3.03|
|Empathy|3.15|||Experiments/data analysis|3.02|
|Self-motivated|3.12|||||
|Understand/take||||||
|directions|3.12|||||
|**Average**|**3.30**|**Average**|**2.98**|**Average**|**3.24**|

We compare the satisfaction levels with findings from a US employer survey of engineering graduates. The results of the two surveys should be interpreted with caution, for two main reasons: (i) The survey questionnaires differed slightly. The US employers were asked to rate five combined types of skills on a three point scale, while the Indian employers were rating using a five point scale on a series of individual skills (these differences were not present for the importance questions analyzed above); (ii) satisfaction rates depend critically upon the respondent‘s expectations. For example, it may be that the competences of the Indian and the US graduates are identical, but the Indian employers have high expectations than the US employers, and the former is therefore less
This absence of a conflict effect in the household data can be accounted for by three factors. First, it may be related to the sample time span (2012–2013), as for this earlier period the surge in international jihadism in the region had only started to materialize, and hence the mechanisms linked to sabotage and rent-seeking were less salient.<sup>25</sup> Second, the findings from the household data (based on domestic respondents only) are consistent with the notion that the likelihood of within-village conflict has barely changed, and the lion’s share of the overall conflict surge can be attributed to international jihadist groups.<sup>26</sup> Finally, the smaller sample size of the household data may matter as well. In particular, note that a traditional, ”small-scale” RCT based on 150 clusters of villages may not be powered to detect impacts on the incidence of (rare) conflict events. Our main results at the village level rely on a much larger set of observations (nearly 4,000 villages).<sup>27</sup> Another key distinction is that the household data is bound to rely on self-reported conflict information, while the village-level data draws on reports and articles from a broad set of media and NGO sources.

Taken together, the household-level results show no evidence of a decrease or increase in conflict between community members, while the village-level results highlight an increase in events perpetrated by foreign terrorist actors. This suggests that the cash transfer program affected conflict mostly by shifting the incentives of actors outside the village rather than by altering opportunity costs to engage in conflict for local populations.

# **4 Discussion and Conclusion**

This paper has presented novel, large-scale evidence of the impact of a government-led unconditional cash transfer program on the likelihood of conflict in Niger. Causal identification relies on the randomization of the program through public lotteries within communes. The results show that, during treatment, there was a temporary increase in conflict events,
## **Reference**

Ahluwalia, Isher **J.** 1996. India's Opening Up to Trade and Investment, in Policy Reform in India, Development Center Seminars, Development Center, OECD, Paris France.

Bajpai, Nirupam and Jeffrey Sachs. 1996. India's Economic Reforns: The Steps Ahead. Development Discussion papers no. 532. Harvard Institute for International Development, Havard University. Lockheed, Marlaine, and Adriaan Verspoor. 1991. Improving Primary Education in Developing Countries. New York, Oxford University Press.

Verspoor, Adriaan. 1989. Pathways to Change: Improving the Quality of Education in Developing Countries. World Bank Discussion paper no. 53. Washington, DC.

. 1997c. Primary Education in India. Development in Practice Series. The World Bank, Washington DC.

. 1997d. India: Achievements and Challenges in Reducing Poverty. A World Bank Country Study. The World Bank. Washington, DC.

. 1996. India: Five Years of Stabilization and Reforn and the Challenges Ahead. A World Bank Country Study. The World Bank, Washington DC.

. 1995a, Economic Developments in India: Achievements and Challenges. A World Bank Country Study. The World Bank. Washington DC.

1995b. India: Recent Economic Developments and Prospects, The World Bank, 1995.

. 1988. Center State Relations in Financing Indian Education, The World Bank, 1988.

. 1989. Trends in Public and Private Finances for Education in India, The World Bank, 1989.

. 1987. Education and Basic Needs in India, The World Bank, 1987.

# Korea: Major Reform Events

In retrospect, Korea's development has passed four main stages. Unlike several of the larger economies in East Asia, which evolved from protectionism, inward-looking trade regimes towarcd relatively open economies, Korea did not have a sufficiently large population to contemplate a strategy other than export-led development, which consists importing raw materials and intermediate goods for processing and export with value-added. It development experience shows that
>yes<br>Time FE<br>yes<br>yes<br>yes<br>**_Panel B: Panel Estimates_**<br>Notes: Panel A: robust standard errors in parentheses; Panel B: standard errors clustered at country level are in<br>parentheses; ** significant at 5%; *** significant at 1%; Dependent variable,_Private Credit/GDP,_is credit to the<br>private sector by banks and other financial institutions as a share of GDP;_EFNX_is the external finance need of exports;<br>_Log(Trade/GDP)_is log of exports and imports as a share of GDP;_Log(Income)_is log of PPP-adjusted real per capita<br>income from Penn World Tables;_French, German, Scandinavian, and Socialist Legal Origin_dummies are as defined<br>originally by La Porta et al. (1998)._Predicted EFNX_is the predicted external finance need of exports constructed by<br>first estimating sector-level gravity regressions, and then generating predicted export shares for each country and sector.<br>Panel A: all of the variables are average values over 1970-99. Panel B: all of the variables are five-year averages 1970-<br>74; 1975-97, ... 1995-99. Variable definitions and sources described in detail in the text.|
|---|---|---|

33
‘Distance to Policy Frontier’ implies being farther away from policy best practices. GCI data are from 2007 to 2017 due to data constraints from WEF’s GCI database (the GCI scores are only methodically consistent and comparable across years during this period).

# III. Population’s Engagement and Support for Economic Reforms: Insights from Social Media

Drawing insights from social media, this section examines the people's engagement and support for current economic reforms in the GCC. As demonstrated by the long-lasting consequences of the Arab Spring, this aspect is critical for the long-term sustainability of economic reforms. Our thesis is that social media can reflect the population’s true sentiment toward economic reforms. To our best knowledge, this paper is the first systematic effort to shed light on the general attitude towards economic reforms in the GCC countries.

6
pre-FPE.

Further, I present evidence in table 5 to show that the pre- and post-FPE cohorts do not come from different wealth groups, and even if they do, such differences would attenuate rather than overstate the FPE effect. Column (1) of the table presents the determinants of the probability of completing secondary or higher education, while column (2) presents the determinants of completed years of education. From column (1), we can see that, all else constant, the probability of completing secondary or higher education did not increase post-FPE. And the interaction of year and household wealth dummies indicates that, while household wealth increases the chances of completing secondary or higher education, the influence of household wealth did not change post-FPE. That is, pre- and post-FPE cohorts come from the same wealth groups.

From column (2), we can see that, everything held constant, 16-24 year-old children completed 0.5 more years of schooling post-FPE and those from wealthier households completed more years of schooling. However, the influence of household wealth on completed years of schooling attenuated post-FPE as indicated by the negative coefficients on year-wealth interaction dummies. The gap in completed years of education between those from rich and poor households was reduced. Consequently, if those who completed more years of education are the ones more likely to proceed to acquire higher education, the pool of students admitted into university post FPE is relatively poor compared to that admitted pre-FPE. This may have downward biased the FPE effect. Thus, taken together, the estimated effect could not have been an artefact of pre- and post-FPE differences in student composition.

# **6.3.2 Pre-FPE effect of FPE on student academic performance**

Here I estimate the placebo FPE effect using the pre-FPE data (i.e. 2010-2011), assuming that the FPE-treated first enrolled in university in 2011. Because the FPE-treated students were not at the university in those years, we do not expect to see any positive placebo FPE effect on performance because
# **1. Introduction**

In recent decades, the need for detailed demographic and socioeconomic data to measure living standards in developing countries has increased dramatically. Data used for measuring living standards are traditionally collected through household income and expenditure surveys or through integrated household surveys. The data these surveys generate are critical for monitoring welfare and informing the design of policies to reduce poverty and promote equitable distribution of economic growth. In most developing countries, integrated household surveys are a central component of national statistical systems, which provide key inputs in social and development policy making and national accounts.

Collecting expenditure or income data for welfare monitoring is expensive and time consuming. Generally, households are visited several times and need to report on their food and non-food expenditures for often more than 50 items over the past days or weeks either using a recall or a diary method. The complexity of the data collection can cause households to make mistakes when reporting their incomes or consumption or enumerators to make mistakes when recording their responses. As a result, collecting consumption or income data from an entire population in a country to monitor trends of key welfare statistics such as poverty headcount rates or mean income is very time-consuming and resource intensive to carry out.

To overcome these challenges, National Statistical Offices (NSOs) conduct household income and expenditure surveys for only a sample of the population. The selection of a sample, which is called “sampling,” is an important subject in statistics and needs to be done carefully so that the select sample of individuals or households represent the entire population and data collected from the sample can therefore be used to estimate characteristics of the population with a reasonable level of precision.

Sampling provides two clear advantages – substantially reducing the cost and time needed to collect data and producing statistics that are representative of the entire population. For example, a sample of a couple of thousand households or individuals is often enough to estimate nationally representative statistics of a country with a population of one hundred 2
The effects of camps on host communities on different outcomes – which may in turn affect attitudes – have been evaluated in a limited number of studies. Maystadt et al. (2020) investigate the refugee-driven landscape changes in Africa. The authors find that refugees cause a small increase in vegetation condition, while contributing to increased deforestation. In a related study, using satellite data on forest cover and loss, Salemi (2021) shows that refugee camp openings are associated with a small reduction in the extensive margin of forest loss (i.e., land clearing) and a small increase in intensive margin forest loss (i.e., gradual reductions in canopy cover). Using global data from 1990 to 2018 on locations of refugee communities and civil conflict (including but not limited to camps), Zhou and Shaver (2021) find no evidence that hosting refugees increases the likelihood of new violent conflict, prolongs existing conflict, or raises the number of violent events or casualties in the area. Ginn (2021) examines camps in Jordan and Iraqi Kurdistan and argues they could play an important role for both refugee and host communities in expanding the stock of housing, which could be a key driver of social cohesion in displacement settings.

Another important dimension is _de jure_ policies. Blair et al. (2021a) significantly advance the study of policies toward refugees in low- and middle-income countries by coding _de jure_ policies in more than 90 countries. They show that policies towards refugees are liberalizing over time in LMICS, unlike in high-income countries. They furthermore argue that policy changes have occurred when neighboring states are in civil war, and hence the country is likely to receive refugees. These policy changes are more likely to be liberal (reductions in restrictions) when the political elites are ethnic kin with a victimized group. In related work, the authors argue that more liberal hosting policies attract additional refugees (Blair et al. 2021b).

These _de jure_ policies relate to attitudes. Figure 1 plots an index of these data
## **Table 9. Log earnings returns to noncognitive skills, adults aged 25-54**

<u>Time preference 0.002 0.004</u> -0.025 -0.023 Notes: These results derive from the full specifications (Table 4, columns 2-5 and Appendix Table 4, columns 3 and 6 for men and women). Those specifications control for age, age-square, education (measured as spline variables), and cognitive skills (measured as plausible values of literacy assessments). Results for all countries pool the samples of Armenia, Bolivia, Colombia, Georgia, Ghana, Kenya, Ukraine and Vietnam and are estimated with country fixed effects. [1] No data on time preference for Colombia; no data on hostile attribution bias, risk-taking and time preference for Serbia. Statistical significance is denoted as * p < 0.10, ** p < 0.05, *** p < 0.01.

Data sources: STEP data for 9 countries, 2012-13

# **VI. Decomposing the Gender Earnings Gaps and the Role of Skills**

Finally, in this section, we draw upon the above results to explore three questions that focus on gender differences: Does estimating a model that allows the slope coefficients of the elements of human capital to differ by gender (that is, moving from a constrained model to an unconstrained model) change our estimates of the conditional gender earnings gap? Does expanding women’s fuller set of skills, beyond completed years of schooling, help to close the gender gap? What could be the effects on the gender earnings gap of policies that equalize observed covariates versus policies that equalize wage structure? We explore these questions here.

# **_Estimating gender disadvantage: Constrained and unconstrained models_**

Using the pooled sample of men and women in all countries to estimate the log-earnings functions, with gender as a dummy variable, we find that women’s log earnings are 31 percent lower than men’s in
Ebola impact these age groups differently: younger girls are relatively more impacted by school closures and barriers to re-enrolling, while the older cohort are more impacted by the disruption to economic and health facilities; (iii) an informal institution in rural Sierra Leone is gender specific ‘secret societies’: the role of the women’s society is to initiate girls into adulthood, and there is a traditional separation of older and younger women in these institutions [Bledsoe 1990].

Our main results are as follows. On the impacts of the intensity of the Ebola shock in control villages (so absent ELA clubs), there are two key dimensions along which the economic lives of young women are impacted: economic activities engaged in, and time spent with men.

On the first dimension, we find that residing in a high disruption village leads younger girls to stay out of schools post crisis, i.e. long after schools have reopened and when our endline survey is conducted. This is a large extensive margin response: enrolment rates fall by 16pp in more disrupted villages, or 32% of the baseline mean. The result indicates that in high disruption villages there are persistent impacts of the shock so that it is harder for young girls to re-enrol even long after the policy of school closures ends. Their reallocation of activities is largely towards engagement in income generation: this rises by 19 _._ 1pp (or 238% of the baseline mean). This accelerated school-to-work transition is far higher than what we would have predicted at baseline as these girls become older.

On the second dimension, residing in a high disruption village absent ELA significantly increases the time young women spend with men. The magnitude of the impact is 1 _._ 27hrs/wk, or a near 48% increase over the baseline mean. As a consequence of this increased exposure to men, we see subsequent impacts on pregnancy and risky behaviors. Moving from a low to a high disruption village is associated with a 10 _._ 7pp increase
The error term reflects both mieasurement error in per capita incomes as well as all other determinants of cross-country differences in per capita incomes that have been excluded from this simple specification. To the extent that these are correlated with govemance, this will introduce omitted variables bias in simple ordinary least squares (OLS) estimates of Equation (1).

Governance itself is not randomly distributed across countries. Good govemance requires time and resources to develop, suggesting that richer countries are more likely to enjoy good govemance. Govemance also depends on a country's political and social history, especially in those countries that inherited a set of institutions from the former colonial powers. B3ased on this motivation we write governance as a function per capita income yj, as well as a set of additional observable determinants N: (2) gj = **+8*yj +** **_'xj_** _+vj_ where v} is a zero-mean error term capturing other unobserved determinants of governance. Depending on the sign of the feedback from income to govemance, simple OLS estimates of (1) will be biased upwards or downwards.

Finally, we have already observed that govemance itself is not very precisely measured. In particular, our observed govemance aggregates, gj*, provide a noisy signal of "true" governance: (3) gj* =g + uj where u1 is a zero-mean disturbance term reflecting measurement error. This will introduce a downward bias in regressions using observed governance as the explanatory variable.

Substituting Equation (3) into Equation (1) yields our empirical specification, in which we regress the log-level of per capita income, yj, on observed govemance, gj*: 13
poor in the face of natural disasters_ . (World Bank Publications, 2016).

27. FAO. _The status of women in agrifood systems_ . (Food and Agriculture Organization of the United Nations, 2023). doi:10.4060/cc5343en.

28. Philippon, N., Mougin, E., Jarlan, L. & Frison, P.-L. Analysis of the linkages between rainfall and land surface conditions in the West African monsoon through CMAP, ERS-WSC, and NOAA-AVHRR data.

- _J. Geophys. Res. Atmospheres_ **110** , (2005).

29. EM-DAT Guidelines. https://public.emdat.be/about.

18
common in most Post-Soviet Republics. Post-1990s housing is similar to much of the modern high rise development throughout Europe with mid-to-large towers that may or may not have small balconies for each unit. In general, housing quality is lower than in the United States or Western Europe but higher than in many other lowand middle- income countries. Our baseline survey indicates that most houses use natural gas for cooking and gas or electric sources for heating and that less than 0.5% of households own an indoor air quality monitor or an air purifier.

# **2.2 Experimental design**

Our experiment was implemented in 3 stages: installation of outdoor air pollution monitors, baseline household survey, and treatment followed by the end-line household survey (Figure A3 shows the

> 4Our household survey indicates that fewer than 5% of households in Tbilisi regularly check outdoor pollution levels at baseline.

> 5We provide images of examples of each housing type in Figure A2.

6
**Figure 2. FDI and Exports before and after Large Devaluations (constant sample, see appendix table for list of countries)**

<!-- Start of picture text --> 0.4<br>N<br>o<br>r 0.3<br>m<br>a<br>l 0.2<br>E<br>i<br>x<br>z<br>p<br>e 0.1<br>o<br>d<br>r<br>t<br>F 0<br>s<br>D<br>I<br>-0.1<br>a<br>n<br>-0.2<br>d<br>t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1 t0 t1 t2 t3 t4 t5 t6 t7 t8<br>Quarters before and after Dramatic Devaluations<br>FDI constant sample Exports constant sample<br><!-- End of picture text --> Source: Author’s calculations based on data from the IMF. See text for details.

11
increase in a country’s real income growth is associated with a 2.2% increase in EV market shares. Other than income, other demographic variables, such as the rate of urbanization (shown in Figure S5d), do not exhibit a strong correlation with EV market shares. These correlation plots suggest that substantial variation in EV diffusion cannot be accounted for by the demographic variables. In the following, we use panel regression models to examine the role of various market and policy factors in the spatial and temporal patterns of EV diffusion.

# **Methods**

**Data** Our analysis is based on seven major data sets for 13 countries from 2013 to 2020: (1) model-level EV sales by country by year, (2) model-level vehicle attributes by country by year, (3) charging ports by charging speed by country by year, (4) model-level financial incentives for

4