rafmacalaba/data-use-mentions-extended huggingface dataset:
friends. According to you, how many of them<br>think that a husband is justifed in hitting or beating his wife if he suspects her of being<br>unfaithful?<br>0.630|
|Imagine to pick 10 of your closest Facebook friends. According to you, how many of them<br>think that a husband is justifed in hitting or beating his wife if she goes out without telling<br>him?<br>0.598|

Notes: The _Global Index_ (Cronbach’s alpha = 0.74) is created using all variables reported above with the exception of those contained in the _Beliefs on others_ index. All variables were re-oriented so that the impact of treatments on each component of the index should be positive. The column on the right reports the loading factors used for the construction of the indexes with principal component.

45
[19] China Electricity Council. China Power Statistics Compilation. 2012. Beijing: 2012

[20] China Electricity Council. China Power Statistics Compilation. 2013. Beijing: 2013

[21] China Electricity Council. China Power Statistics Compilation. 2014. Beijing: 2014

[22] Tsinghua University.Wang.China's Energy Science and Technology Development Outlook for the 21st Century.Tsinghua University Press Publishing.

[23] National Electricity Regulatory Commission. Electric Power Supervision Annual Report 2010

[24] National Bureau of Statistics. 2006. China Statistical Yearbook 2006. Beijing: China Statistics Press, 2006.

[25] National Bureau of Statistics. 2007. China Statistical Yearbook 2007. Beijing: China Statistics Press, 2007.

[26] National Bureau of Statistics. 2008. China Statistical Yearbook 2008. Beijing: China Statistics Press, 2008.

[27] National Bureau of Statistics. 2009. China Statistical Yearbook 2009. Beijing: China Statistics Press, 2009.

[28] National Bureau of Statistics. 2010. China Statistical Yearbook 2010. Beijing: China Statistics Press, 2010.

[29] National Bureau of Statistics. 2011. China Statistical Yearbook 2011. Beijing: China Statistics Press, 2011.

[30] Department of Industrial Statistics, National Bureau of Statistics. China Industrial Statistical Yearbook 2010. Beijing: China Statistics Press, 2010.

[31] Department of Industrial Statistics, National Bureau of Statistics. China Industrial Statistical Yearbook 2011. Beijing: China Statistics Press, 2011.

[32] Department of Industrial Statistics, National Bureau of Statistics. China Industrial Statistics Yearbook 2012. Beijing: China Statistics Press, 2012.

[33] Department of Industrial Statistics, National Bureau of Statistics. China Industrial Statistics Yearbook 2013. Beijing: China Statistics Press, 2013.

[34] China Iron and Steel Association. China Steel Statistics 2011. Beijing: China Iron and Steel Industry Association Information Statistics Department, 2011.

[35] Editorial Office of China Chemical Industry Yearbook. China Chemical Industry Yearbook 2005-2006. Beijing
business training and coaching.<sup>13</sup> A few students from the treatment groups completed CEFE training between graduation and the midline survey. Therefore, the actual take-up of CEFE training among the treatment group included in the endline sample is 71 percent.

In the context of the medium-term analysis, some control group individuals may have taken the CEFE entrepreneurship training after graduation. Administrative data shows that take-up of CEFE training was low, with only approximately 3.4% of the control group completing it between graduation and the midline survey. Administrative data also show that only approximately 10 percent of the control group took CEFE training between midline and endline.

> 13 More details on drop-out patterns can be found in World Bank and MFPE (2012).

12
25

|Coumtry,<br>Last m ils<br>economy,<br>or<br>P<br>territory<br>Date|hed censu<br>opulati<br>(1000) *|s<br>on<br>Source<br>for population<br>(mid-1985)|Source<br>for fertility<br>(1985-90,<br>unlessIndicated)|Source<br>for mortality<br> (1985-90,<br>unlessindicated)|
|---|---|---|---|---|
|Yeen, People'sKay73<br>Dem.Rep. of|1590 F|U.N. 1988revision<br>(prelim.)|<br>Basedon U.N. 1984<br>assessment|Based on UNPVSR<br>4/87|
|Yugoslavi<br>Mar 81<br>Zaire<br>Jut 84<br>Zambia<br>Sep 80|22425 J<br>29671F<br>**5680**F|WIPVSR 7/86 (officiat<br>est.)<br>U.N. 1988 revision (prelim.)<br>Bank projection from census|Based on UNPVSR<br>4/87<br>U.N. 1988 revision (prelim.)<br>1980-85, Bank sector report;<br>1985-90, Bank projection|Based on UNPVSR<br>4/87<br>U.N. 1988 revision (prelim.)<br>1975-80, Bank sector report;<br>1985-90, Bank projection|
|Zimbabwe<br>Aug 82|7550 F|Bank projection<br>from<br>census|Bank est.<br>based<br>on officiat<br>contraceptive prevalence<br>rate<br>of 26X|U.N
, “Why the industrial revolution was british: commerce, induced invention, and the scientific revolution 1.” _The Economic History Review_ , 64, 357–384.

- Almås, Ingvild, Maximilian Auffhammer, Tessa Bold, Ian Bolliger, Aluma Dembo, Solomon M Hsiang, Shuhei Kitamura, Edward Miguel, and Robert Pickmans (2019), “Destructive behavior, judgment, and economic decision-making under thermal stress.” Working Paper 25785, National Bureau of Economic Research, URL `http://www.nber.org/papers/w25785` .

- Alpert, Pinhas, Olga Shvainshtein, and Pavel Kishcha (2012), “Aod trends over megacities based on space monitoring using modis and misr.” _American Journal of Climate Change_ , 1, 117.

- Ambasta, Pramathesh, PS Vijay Shankar, and Mihir Shah (2008), “Two years of nrega: The road ahead.” _Economic and Political Weekly_ , 41–50.

- Anand, Greeta (2016), “Farmers’ unchecked crop burning fuels india’s air pollution.” _The New York Times_ , URL `https://www.nytimes.com/2016/11/03/world/asia/ farmers-unchecked-crop-burning-fuels-indias-air-pollution.html` .

- Andini, Ade, Sebastien Bonnet, Patrick Rousset, and Udin Hasanudin (2018), “Impact of open burning of crop residues on air pollution and climate change in indonesia.” _Current Science_ , 115, 2259–2266.

- Arrow, Kenneth, Bert Bolin, Robert Costanza, Partha Dasgupta, Carl Folke, Crawford S Holling, BengtOwe Jansson, Simon Levin, Karl-Göran Mäler, Charles Perrings, et al. (1995), “Economic growth, carrying capacity, and the environment.” _Ecological economics_ , 15, 91–95.

- Asher, Sam, Tobias Lunt, Ryunosuke Matsuura, and Paul Novosad (2019), “The Socioeconomic Highresolution Rural-Urban Geographic Dataset on India (SHRUG).” Working paper.

- Asher, Sam and Paul
## _INTRODUCTION_

Standard benefit-incidence analysis assumes that the subsidy and the quality of educational services are the same for all income deciles. This is a strong assumption that has the tendency to minimize the distributional inequity within educational levels. This paper uses a new approach that prevents this drawback. The marginal willingness to pay analysis measures the government's provision for public schools effect on the educational spending behavior of an average household. What would an average household with a given set of characteristics be willing to spend on an individual child, if subsidized public education facilities were not available? What would the household have "saved" by sending the child to public schools instead of private schools? How large are these "savings" for various income groups?

In addition, this paper investigates the link between the government's educational policy and the household's decision-making with regards to its educational expenditure and schooling enrollment. Consequently, this paper focuses on answering the following questions: Which are the determinants for enrollment by income groups and location? How do individuals' educational expenditures affect enrollment patterns?

The study is structured as follows: Section 1 provides a brief description of the data. Section 2 examines private expenditures on education. Section 3 studies the determinants of upper secondary enrollment. Section 4 analyses the marginal willingness to pay for educational services. Section _5_ presents the concluding remarks.

## **_1 THE_** _DA TA_

This paper uses the data from the National Household Income and Expenditures Survey (ENIGH) for 1996. The ENIGH is collected by the _Instituto Nacional de Estadistica, Geografia e Informdtica_ (INEGI). This survey is available for 1984, 1989, 1992, 1994 and 19963. Each survey is representative at national level, urban and rural areas. For 1996, the ENIGH is also representative for the states of Mexico, Campeche, Coahuila, Guanajuato, Hidalgo, Jalisco, Oaxaca and Tabasco.

The survey design was stratified, multistage and clustered. The final sampling unit is the household and all the members within the
34 14 While international remittances are (on average) about 58 percent larger than internal remittances, it should be noted that about twice as many households receive internal remittances (1063 households) as opposed to international remittances (593 households).

15 According to the World Bank, this is a “full” poverty line because it includes both food and nonfood expenditure. For more details on how the World Bank calculated this expenditure-based poverty line for Guatemala, see World Bank (2004: Annex 3).

16The poverty and inequality results in Table 4 are based on including remittances – internal or international – in <u>predicted per capita household expenditure. However, as shown in Appendix A, the poverty and inequality results</u> are similar when remittances are included in observed – rather than predicted -- per capita household expenditure. See Appendix A and Appendix Tables 1 and 2.

17 These results are different from those reported by Adams (1995) for rural Pakistan, where internal remittances were found to reduce income inequality, and international remittances represented an inequality-increasing source of income.

> 18Since the decile rankings in Table 5 are based on per capita household expenditure (excluding remittances), the findings in this table may appear to differ from those of Table 4 and Figure 1, where the ranking of households is based on per capita household expenditures (including remittances).  As noted in the text, households receiving internal or international remittances make considerable changes in their decile rankings between the excluding and including remittances situations.

19One possible reason for the disappointing logit results in Table 7 is the limited number of households (49) in the lowest decile group that are receiving international remittances.
These tariff changes are measured from 1987 to 2000, the sample period available to us from Topalova and Khandelwal (2011). Trade reforms are established to be exogenous to the Indian formal manufacturing sector, particularly in the period prior to 1997 (Topalova, 2007; Topalova and Khandelwal, 2011). Since we measure the change in trade reforms up to the year 2000, we are not overly concerned about the endogeneity of these reforms. When estimating the impact of tariff changes, we measure the change in the female activity from 1994-2010, thereby taking advantage of the full sample period available to us.

For industry de-reservations, our explanatory variable of interest is simply the increase in the share of de-reserved products for SSI. These changes are measured from 1999-2009, as made available from Tewari and Wilde (2015). As in the case of trade reforms, we measure change in female activity and segmentation in these regressions from 1994-2010, including industry level information from both the formal as well as the informal manufacturing sectors. Although these reforms are mostly considered exogenous, particularly to formal manufacturing activity (Martin, Nataraj and Harrison, 2014; Tewari and Wilde, 2015), we are not aware of any study that considers the impact of de-reservation on the informal manufacturing sector in India or establishes its exogeneity in this particular context. Looking ahead in our work, we do expect de-reservations to impact the informal manufacturing sector as most plants in this sector are, by definition, small. Therefore, any policy that lifts protection off the relevant industries in this sector is likely going to have a large impact on competition. Future work that hopes to study the impact of these reforms on the informal sector should also aim to establish the exogeneity of these reforms to the informal sector.

�� in equation (1) is the set of other controls in our estimating equation, such as the initial value of the dependent variable, the share of female employment in total employment in the given industry _i_ or the change in this share over the considered
4. The Global Distribution of Endemism responsibilities for species that are heavily concentrated within their borders. Endemic status is traditionally assigned to a species if its range lies entirely within one country. However, a country’s stewardship could remain critical for a species that resides almost entirely within its borders. To explore the implications, we overlay the World Bank’s country map with our GBIF species occurrence maps and classed as endemic if the conventional 100% criterion is applied. When the criterion is changed to 95% and 90%, species classified as endemic increase to 317,815 (52%) and 339,656 (55.6%), respectively. As and the maximum to 62.1%. In this paper, we will employ the traditional 100% criterion for subsequent analyses. However, the results in Table 2 are cautionary because they show that countries assume stewardship responsibilities for about 339,656 species if the criterion is reduced to 90%.

major role in raw counts, so we standardize by total country species to highlight the relative importance New Zealand, Madagascar, Japan and Costa Rica. Even for the top 30 among 297 entities tabulated,
net effect of policy change 1 2) from that of systematic registration 3 1 2)�. It suggests that 35% and 29% of changes in female (γγ + γγ and joint vs. male individual registrations and 22% of the change in joint vs. female individual registration �γγ −(γγ + γγ can be attributed to policy changes and the remainder to systematic first-time registration. The importance of first-time registration to enforce policy changes on female land ownership is consistent with the notion that changes in women’s legal rights to land alone may not become fully effective -and may even result in effects opposite to what was intended (Anderson and Genicot 2015)- unless they are disseminated in a way that makes them socially acceptable (Bhalotra _et al._ 2018). Systematic registration increased the share of jointly registered land plots by reducing the share of those registered individually by either men or women.

Results from estimating (3) for the number registered mortgages at block level are in table 6 panel A with estimates for all registered plots in column 1 and those owned exclusively by men, women, or jointly in columns 2, 3, and 4, respectively. In contrast to results from the DID specifications using household survey data, estimated coefficients for sporadic and systematic registration are precisely estimated with point estimates suggesting significant impacts. Focusing on sporadic registration first, we note that 2, the estimated marginal effect for plots sporadically registered after 2010 is, with 0.0273 well above 1 (0.0160), the effect for plots sporadically registered θθ before 2010 which is also the upper bound of the ‘pure’ effect of reducing the transaction costs of mortgage θθ 15
3.2 Alternative Composite Indicators While species occurrence region size is commonly cited as the best predictor of extinction risk comparative exercise includes 87,718 IUCN-assessed terrestrial and marine species that we have mapped with GBIF data. Among these, many species with tiny species occurrence regions (grid ) are rated Least Critical (LC) by IUCN. However, it would be reasonable to assert that species with such tiny species occurrence regions are in constant jeopardy, because spatial economics evolve unpredictably and modern systems can clear large areas in time periods At the same time, the econometric results in Table 2 show that habitat size is the most significant determining factor for IUCN threat ratings in all five estimation groups. Figure 6 reveals the source of this apparent contradiction. To create the figure, we have computed the percent of LC species <!-- Start of picture text --> @<br>60- ee”<br>= e* e<br>Oo r) e<br>oD e<br>& 50- oe<br>aa e@<br>3 ooo°<br>w eo”<br>7 ee<br>@ 40- @ ee<br>vann e®<br>o@<br>°ry<br>oO e<br>oa 30- @<br>Con”<br>0 50 400 150 200<br>Species Occurrence Map Scale (km)<br><!-- End of picture text --> In this subsection, we explore the implications of assigning greater priority to small- species
**Figure 3.7: Predicted returns to fertilizer use under bad weather conditions**

<!-- Start of picture text --> 150<br>100<br>50<br>0<br>5 25 50 75 95<br>‐50<br>Price percentile<br>‐100<br>Maize Beans Matooke Coffee<br>%<br><!-- End of picture text --> Source: Staff calculations using yield responsiveness and cost of inputs estimated in the OFRA Response Function Database (2017) from the University of Nebraska and in Bold et al. (2017) and wholesale prices from UBOS, 2000 ‐ 2012.

# _3.3. Quality of input use_

The quality of inputs in markets frequented by farmers is commonly low in Uganda. A 2014 Deloitte study finds that low quality inputs in Uganda are most common in the maize herbicide market, followed by maize seeds and fertilizer. In the country, low quality has been associated with bulk breaking – that is, when smallholder farmers demand quantities of fertilizer or seed that are smaller than the contents of the package. For example, fertilizer is sold in 50 kg bags, but farmers will often demand fertilizer in 1 kg, 2kg or 5kg bags. Similarly, seed companies package seed in 2kg bags while farmers often demand smaller quantities than this. When selling these smaller quantities, the input can either be diluted and/or the label is counterfeited. This problem is particularly worrisome, as a large majority of farmers report buying their agricultural inputs from the market. According to the 2015 National Service Delivery Survey (NSDS), a little more than 90 percent of the households engaged in agriculture obtain their herbicides, fungicides, pesticides and fertilizer from the market, shops or local vendor. In the case of hybrid seeds, the share drops to 64 percent (see Figure 3.8.1).

Recent economic studies have carefully quantified the magnitude of the counterfeiting problem in
# **6. Conclusions**

The ongoing research on Covid-19 has predominantly revolved around the macro-economic impact, labor market implications, and mitigating social aids or policies undertaken by developed countries. Yet, the pandemic and the associated lockdown measures were observed across developing and developed countries, regardless of the number of confirmed Covid-19 cases (Dunford et al., 2020). Although overall findings point to reduced economic growth at the macro level (see Alon et al., 2020), the lockdown policies have a potentially heterogeneous impact on countries’ socio-economic and labor markets, providing dynamic outcomes from country to country.

This paper examines the impact of the Covid-19 pandemic on household expenditure, poverty status, asset ownership, and labor market outcomes for the Comoros, a small island developing country that was already grappling with a recent climatic shock to its economy. We use unique door-to-door household survey data collected during the Covid-19 outbreak in the Comoros, covering the pre-lockdown and post-lockdown periods. The data provide detailed information on household expenditure, asset count and monetary value, livestock ownership, and relevant household and individual labor market outcomes. In addition, the availability of other household and individual characteristics allowed us to address endogeneity concerns in the estimation of the effect of the national lockdown policy on the welfare of households in the Comoros.

We first evaluated the impact of the national lockdown implemented on March 23, 2020 by the Government of the Comoros on our welfare indicators and labor market outcomes. Then, we extended our analysis to evaluate the distributional impact on household expenditure. Our empirical research benefitted from descriptive analysis and causal estimation methods. Our empirical study found a negative effect of the national lockdown on household expenditure, 40
9 days of log volume as the trend. Similarly, we measure stock market volatility by first demeaning each daily stock return, squaring this residual and then subtracting the past 60-day moving average of the squared residual.

Finally, we use (i) the S&P Goldman Sachs Commodity index to measure daily changes in commodity prices (in %) and (ii) the CBOE VIX to proxy for global volatility.

# **C.** **<u>Capital flows</u>**

We assess the impact of news sentiment shocks on capital flows using daily equity fund flows from EPFR. The EPFR global dataset, which tracks the asset allocation of equity and debt funds domiciled in developed countries and key offshore financial centers, has become an important source of high-frequency capital flows.<sup>7</sup> Because of its extensive industry coverage and quality, the EPFR global has been widely used recent academic contributions on funds behavior (e.g., Raddatz and Schmukler (2012), Jotikasthira et al. (2012), Fratzscher (2012), and other references therein).<sup>8</sup> In policy circles, fund flows reported by EPFR have been increasingly used as a (high frequency) proxy for foreign capital inflows, especially in Emerging Markets.<sup>9</sup> In practice, we use the “equity country flows” data set, which reports, every day, the estimated amount of equity funding in US dollars that came (or left) each country, each day, because of international funds’ portfolio reallocation. Overall, our dataset of equity flows covers 16 emerging markets between 2005 and 2015.<sup>10</sup> After estimating the effect of sentiment shocks on the total equity inflow (or outflow) into (or out of) the country, we then 7 Its coverage has increased significantly over time, reaching currently a wide industry and geographic coverage. As of 2013, the
2018). To calculate these effects, we use their Commitment to Equity (CEQ) database which contains information on income and estimates of taxes paid (consumption taxes, direct taxes and social security) for each income decile in 25 developing countries.<sup>43</sup> Importantly, their methodology does not systematically consider the possibility that some places of purchase may be informal,

> 41Formally, in the top panels we set the uncompensated price elasticities in expressions equal to -1 for all countries when calibrating expressions (6), (8) and (9). In the bottom panels we calibrate these elasticities using expression (4) and the parameter values detailed in Table 4.

> 42In Appendix Figure A13 we also show the calibrated optimal uniform rates on all formal varieties. Consistent with our theory, the optimal uniform rate falls over development for redistributive purposes. As predicted, allowing efficiency costs to vary with development lowers the optimal rates, especially in the poorest countries and more so the higher the cross-variety elasticity of substitution.

> 43In the first row of Panel B, we calculate the change in Gini from applying general consumption and excise taxes to disposable income plus indirect subsidies. In the second row of Panel B, we calculate the change in Gini from applying the direct tax and social security contributions to market income plus direct cash transfers. These exercises allow us to calculate the marginal Gini impacts of the indirect and direct tax systems, respectively.

28