rafmacalaba/data-use-mentions-extended huggingface dataset:
Table E9: Share of Significant Temperature Coefficients in Niger

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.50|0.58|0.58|
|Median Daily Temperature|0.50|0.67|0.67|
|Variance of Daily Temperature|0.50|0.83|0.50|
|Skew of Daily Temperature|0.50|0.50|0.58|
|Growing Degree Days (GDD)|0.50|0.50|0.75|
|Deviation in GDD|0.50|0.50|0.75|
|Z-Score of GDD|0.33|0.67|0.67|
|Maximum Daily Temperature|0.83|0.58|0.83|
|Observations|96|96|96|

_Note:_ The table presents the share of statistically significant temperature coefficients in Niger.

Table E10: Share of Significant Temperature Coefficients in Nigeria

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.58|0.67|0.58|
|Median Daily Temperature|0.67|0.75|0.67|
|Variance of Daily Temperature|0.75|0.83|0.58|
|Skew of Daily Temperature|0.50|0.42|0.33|
|Growing Degree Days (GDD)|0.75|0.92|0.83|
|Deviation in GDD|0.67|0.67|0.83|
|Z-Score of GDD|0.67|0.83|0.67|
|Maximum Daily Temperature|0.67|0.58|0.58|
|Observations|96|96|
a childcare shock and increased responsibilities within the home disproportionately impacted women entrepreneurs with children. Our results are directly relevant to the ongoing policy debate on advocating for care-related policies, as well as programs addressing gender norms in unpaid domestic and care work, and engaging men in fatherhood care duties (Devercelli

> 8The average closure rate over the six survey waves in 2020 was 17% for male-led businesses.

> 9Norms and personal beliefs towards gender equality were collected prior to the start of the pandemic from entrepreneurs in the FoB survey in December 2019 from 108 countries.

5
4.3). The impact of the relative safety treatment remains of a similar magnitude as those presented in Section 5.3.2 as this treatment had similar effects across respondents with different demographic characteristics (see Figure A19 in Appendix A). In contrast, the impact of the social norms treatment becomes substantially larger and is now statistically significant. This is primarily because female respondents and those in the Southern region were under-sampled in the online experiment and the weights corrected for this. Similar results hold after controlling for the background characteristics of respondents (see Table A8 in Appendix A).

# **6. Discussion and conclusion**

In this section we first outline the areas that our findings suggest policy makers should and should not prioritize to address COVID-19 vaccine hesitancy. We follow this with a broader discussion on the generalizability of the results and areas for further research.

# <u>6.1 Issues to focus on to address vaccine hesitancy</u>

Our study highlights three key areas for policy makers to prioritize as they attempt to reduce vaccine hesitancy. Firstly, it provides comprehensive evidence that boosting people’s trust in COVID-19 vaccines appears to be a fundamental step in increasing people’s willingness to get vaccinated. The phone survey showed strong correlations between willingness to get vaccinated and trust in the vaccine as well as believing it has important health benefits. The online experiment illustrates that information about the relative safety of COVID-19 vaccines substantially increases willingness to get vaccinated and reduces opposition to receiving the vaccine. Collectively, these results suggest that many people make some form of basic “cost benefit analysis” about whether the consequences of COVID-19 justify the risk of being vaccinated. Further evidence that this is the case can be seen by the fact that respondents who believed many people had died from COVID-19 in PNG were much more likely to want to get the vaccine (see Figure A20 in Appendix A). The implication for policy makers from this is that they should prioritize efforts to inform the general population that COVID-19 vaccines
# REFERENCES

- Altinok, Nadir, Noam Angrist, and Harry A. Patrinos. _Global Dataset on Education Quality 19652015_ . World Bank Policy Research Working Paper No. 8314, 2018.

-

- Barro, Robert J., and Jong Wha Lee. “International data on educational attainment: updates and implications.” _Oxford Economic Papers_ 53, no. 3 (2001): 541-563.

- Barro, Robert J., and Jong Wha Lee. “A new data set of educational attainment in the world, 1950– 2010.” _Journal of Development Economics_ 104 (2013): 184-198.

- Becker, Gary S. “Investment in human capital: A theoretical analysis.” _Journal of Political Economy_ 70, no. 5, Part 2 (1962): 9-49.

- Campbell, Susanna G., and Murat Üngör. 2020. “Revisiting human capital and aggregate income differences.” _Economic Modelling_ .

- Caselli, Francesco and Antonio Ciccone. “The Human Capital Stock: A Generalized Approach. Comment.” _American Economic Review_ , 109, no. 3 (2019): 1155-74

- Gennaioli, Nicola, Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. “Human capital and regional development.” _The Quarterly Journal of Economics_ 128, no.1 (2013): 105164.

- Friedman, Joseph, Hunter York, Nicholas Graetz, Lauren Woyczynski, Joanna Whisnant, Simon I. Hay, and Emmanuela Gakidou. “Measuring and forecasting progress towards the educationrelated SDG targets.” _Nature_ (2020): 1-4.

- Hanushek, Eric A. and Dennis D. Kimko. “Schooling, Labor-force Quality, and the Growth of Nations.” _American Economic Review_ 90, no.5 (2000): 1184-1208.

- Hanushek, Eric A., and Ludger Woessmann. “Do Better Schools Lead to More Growth? Cognitive Skills, Economic Outcomes, and Causation.” _Journal of Economic Growth_ 17, no.4 (2012a): 267-321.

- Jones, Benjamin F
**Table 8.  Values of Adjusted R**<sup>**2**</sup> **for Regressions With and Without Public Data**

||Surveyregression|Full regression|Public data regression|
|---|---|---|---|
|Overall|.262|.287|.077|
|Poor households (bottom 2/5)|.155|.188|.048|
|Very poor households (bottom quintile)|.100|.128|.030|
|Net crop sellers|.187|.216|.075|
|Urban households|.252|.274|.036|
|Household size:||||
|One person|.222|.243|.050|
|Two|.315|.343|.078|
|Three|.315|.346|.100|
|Four|.349|.390|.136|
|Five|.333|.374|.144|
|Six or more|.351|.392|.145|

Variables included:

**Survey regression:** As in Table 1: size of household; household runs a business; most income from wages; most income from profits; no. of males; no. aged under 5, 5-9, 10-14, 15-19, 20-29, 30-59, 60-64, 65-74, 75 and over; head is a widow; head is separated; no. with just primary education, just secondary education, just upper vocational education, university education, post-graduate education.

**Public data regression:** Dummies for region (N, C, NE, S, Bangkok is reference), and for urban residence; monthly series on: unemployment rate; consumer price index for south, northeast, north, center, Bangkok; U.S. dollar value of exports of agricultural products, manufacturing, overall; index of
(0.062)|(0.021)|(0.019)|(0.011)|
|_R_<sup>2</sup>|0.187|0.215|0.302|0.183|
|Hausman||0.000||0.000|
|_βSP_ ⩽0|0.032|0.015|0.026|0.004|
|_βSP_ ⩾1|0.870|0.645|0.621|0.307|
|_βT C_ ⩽_−_1|0.000|0.000|0.006|0.001|
|_βT C_ ⩾0|0.621|0.466|0.363|0.508|
|Countries|108|108|108|108|
|Observations|1082|1082|1087|1087|

Note: OLS and fixed effects (FE) results, annual data, 1990-2003. 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.

17
away from this, as these results are not robust across violence datasets.

The completion of road projects when mining revenues increase has a very different impact if the revenues come from small artisanal gold mines or from large cobalt mines (Table A8). The completion of roads is associated with a decrease in violence when mining gold revenues are very low, but an increase in violence as the revenues increase. The opposite happens for cobalt mining revenues and road projects.

# **5.3 Discussing the Possible Channels**

There are at least three possible non-exclusive channels through which better roads can reduce violence. First, better roads allow government forces to maintain order. Second, better roads increase economic opportunities and may convince some current or future members of armed groups to stop fighting. Third, better roads can increase the incentives of armed forces to settle in a specific territory, to unilaterally control it, and to generate income from the population (i.e., “the stationary-banditry” theory). From discussions with World Bank practitioners, roads seem to play an important role in allowing government forces to bring back order and reduce violence.

We test the hypothesis that road projects increase economic opportunities using the same specification as in Table 1 replacing violence with nighttime lights. We use the average calibrated nightlights from the DMSP-OLS nighttime lights Time Series Version 4 for the period 27
are equicorrelated at the block level and estimate all equations in feasible GLS (FGLS) to improve efficiency (Cameron and Miller, 2015).<sup>30</sup> To guard against misspecification of the error structure, I additionally calculate Eicker-White standard errors clustered at the sub-district (block) level or district level depending on the level of aggregation of the regressors.

Since the number of clusters is fairly small, these standard errors are likely to be downward biased. Inference on the main hypothesis is therefore based on p-values obtained from performing a wild cluster-bootstrap with Rademacher weights as suggested by Cameron, Gelbach, and Miller (2008).<sup>31</sup> In a more recent paper, Cameron and Miller (2015) suggest the use of Webb’s (2014) weights if the number of clusters is smaller than ten, which seems reasonable when using a district level treatment variable. P-values of the respective treatment variable using both versions of the bootstrap with 4,999 replications are reported in the results section.<sup>32</sup>

# **5 Results**

This section starts by presenting estimates for an agricultural production function. It proceeds by assessing the extent to which the NREGS can actually support households in this sample in coping with shocks, which is the precondition for expecting any insurance effect. This section then analyzes the effects of the NREGS on households’ crop choices and presents a number of robustness checks.

## **5.1 Identifying profitable production strategies**

To understand inhowfar households’ crop choice can improve their income from agricultural production, I estimate an agricultural production function, linking the total value of

> 30Estimation if FGLS is more efficient than OLS as it specifies a model for the error variances.

> 31This approach was applied, inter alia, by Adrianzen (2014) to data clustered in 26 villages and by Akosa Antwi, Moriya, and Simon (2013) to 28 quarter-year groups.

> 32The wild cluster-bootstrap calculates t-statistics for each bootstrap
# 4.  Measures of Inequality Using the Individual Wealth Data

## 4.1  Comparing measures of inequality

Using the total value of assets across different asset classes, Figure 3 presents estimates of competing measures of inequality among individuals as measured by Lorenz curves based on (i) household per capita expenditure, (ii) household per capita wealth (scaling individual wealth up to the household level), and (iii) individual wealth as measured by specification (C). A striking factor is how high inequality is in wealth overall, as compared to household per capita consumption expenditure (see Boyer, 2020, for a discussion of measurements of wealth and consumption inequality across countries, as well as underlying data issues). The results underscore the importance of individual-level data collection on wealth: across countries, as compared to the Lorenz curves for household per capita expenditure and household per capita wealth (aggregating individual wealth to the household level and dividing by household size), inequality as measured by individual wealth is significantly greater in all countries. With the exception of Tanzania, urban inequality tends to be somewhat greater than rural inequality across countries. Greater inequality as measured through individual-level wealth data is consistent with other recent studies that have examined how consumption-based poverty measures can be underestimated when examining outcomes only at the household level (Brown et al., 2018; Bose-Duker et al., 2021).

28
To control for these effects we include in our specifications the volume of merchandise exports to the US, drawn from the IMF Direction of Trade Statistics, and Leamer’s (1984) index of natural resources endowments, net exports of

> 8 See Lederman and Saenz (2003) for a complete discussion of the data.

> 9 The U.S. PTO demands that the invention be “novel and nontrivial, and has to have commercial application” (Jaffe and Trajtenberg 2002, 3-4). 10 See Trajtenberg (2001).

5
Table 5. Death events attributable to the risk of smoking and other premature deaths, 2017

|Disease|Males|Females|All|
|---|---|---|---|
|Aortic aneurysm|76|4|80|
|Bladder cancer|87|2|89|
|Chronic obstructive pulmonary disease|472|49|521|
|Colon and rectum cancer|72|7|78|
|Ischemic heart disease|2,281|263|2,544|
|Larynx cancer|131|2|133|
|Lip and oral cavity cancer|61|3|63|
|Liver cancer|66|5|70|
|Lower respiratory infections|133|12|145|
|Stomach cancer|134|7|142|
|Stroke|1,067|139|1,206|
|Tracheal, bronchus, and lung cancer|973|36|1,009|
|Other attributable|621|97|718|
|Total attributable|6,175|623|6,799|
|Non-attributable|19,247|24,518|43,765|
|Allpremature deaths|25,422|25,142|50,564|

_Source:_ Author’s calculations based on 2017 data from the Global Burden of Disease (GDB). Includes death events related with the risk of smoking.

Table 6. Calculation of YLL because of smoking-related deaths, 2017

||Smokin<br>|g-related death<br>|events<br>|Years ofpr<br>|oductive life lo<br>|st(YLL)<br>|
|---|---|---|---|---|---|---|
|Age group|Males|Females|All|Males|Females|All|
|30 to 34|34|3|37|1,131|86
include: (i) to better target public educational services;<sup>(ii) charge a fee for public educational</sup> services to the non-poor; and (iii) increase the quality<sup>of educational services for the poor.</sup>

**Table 8. Effect " of public schools provision**<sup>**on household education spending by poverty status'2**</sup>

||Extreme|Moderate|Non Poor|
|---|---|---|---|
|MMWPv|2114.63|2963.22|7229.76|
|MMWPu|849.23|1241.22|1073.99|
|Effect|1265.40|1722.00|6155.77|

_Source:_ Own calculation based on ENIGH<sup>96</sup> 1/ Annual 1996 pesos As follows from table 6 in appendix 2,<sup>at the primary level, all explanatory variables turned</sup> are significant for explaining the _MMWPu._<sup>In lower secondary level, the variables household per</sup> capita income, region, number of rooms,<sup>type of floor and number of children are important in the</sup> determination of the _MMWPu._ While for upper<sup>secondary instruction, household per capita</sup> income and age are relevant. Interestingly,<sup>household per capita income is also a significant</sup> variable in explaining _MMWPv_ (see table<sup>7 in appendix 2). This suggests that parent's valuation</sup> for private educational services relies solely on income<sup>while for public educational services there</sup> are other important factors in addition to income<sup>that determines</sup><sup>_MMWPu._</sup> In table 9, the effect of government provision
**ns)**|
|Soil moisture||||0.693|0.610|
|||||(0.497)|(0.173)|
|NDVI||||0.396|0.161|
|||||(1.038)|(0.852)|
|Rainfall||||0.205|0.334|
|||||(0.942)|(0.826)|

# _Source:_ World Bank.

_Note:_ NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; WRSI = Water Requirement Satisfaction Index. Countries and surveys included are: Ethiopia (2011, 2016), Lesotho (2017), Malawi (2010, 2016, 2019), Mauritania (2014, 2109/20), Mozambique (2014), Niger (2018), Nigeria (2011, 2013, 2016), Zambia (2015), and Zimbabwe (2017).

hazard as a level along with the fixed effects method or it uses the specification of the hazard as a standardized anomaly from the local normal (over the 20-year period). In fact, in the extreme case in which only one crosssectional round of survey data is available, then the specification of the hazard as a standardized anomaly is the only sensible option, since in this instance, use of the level of hazard with the fixed effects for districts (for example) would simply express the hazard as a deviation from the district mean of the hazard values experienced in the same survey round by other households in the survey.

16
Figure O3: Grocery store price indices by quantile of change in log synthetic benefits per population, 2006-2010 <!-- Start of picture text -->
1.15<br>1.1<br>1.05<br>1 FarmBill ARRA<br>2006m1 2007m7 2009m1 2010m7<br>Year-month<br>Above Median Below Median<br>Price Index<br><!-- End of picture text -->

Notes: This figure plots the revenue-weighted average grocery store price indices by the quantile of change in log synthetic benefits per population for the first and second quantile, respectively. To obtain these quantiles, the total change in the log synthetic benefits per population during the Farm Bill and the ARRA for each state, residualized by control variables, is used to separate the states into quantiles. The first quantile denotes the 24 states with the largest changes in residualized log benefits per population, and the second quantile denotes the 24 states with the smallest changes.

Figure O4: Housing price and unemployment rate by quantile of change in log synthetic benefits per population, 2006-2010 <!-- Start of picture text --> (a) Housing price (b) Unemployment rate<br>1.05 2<br>1<br>1.5<br>.95<br>.9<br>1<br>.85<br>.8 .5<br>2006m1 2007m7 2009m1 2010m7 2006m1 2007m7 2009m1 2010m7<br>Year-month Year-month<br>Above Median Below Median Above Median Below Median<br>Housing price, normalized to 2006m1<br>Unemployment rate, normalized to 2006m1<br><!-- End of picture text --> Notes: This figure plots the housing price and unemployment rate, normalized to 2006m1, by the quantile of change in log synthetic benefits per population for the first and second quantile, respectively. To obtain these quantiles, the total change in the log synthetic per population
size.

Third, the entrepreneur could set fees 33 percent higher than the current levels. Using data from Pakistan, Carnero et al. (2010) structurally estimate the elasticity of private school market shares to fees and nd that a 1 percent increase in prices reduces the market share per private school by 1.2 percent. Given this high price elasticity, private schools would therefore not be able to increase prots by raising fees, ruling out this arbitrage opportunity, as well.

28
.15|0.09|-0.06|(0.05)|147|
|Reason to repay loans is penalties|0.17|0.11|-0.06|(0.06)|147|
|Reason to repay loans is future loans from lender|0.03|0.21|0.18<sup>_∗∗∗_</sup>|(0.05)|147|
|Reason to repay loans is credit score|0.65|0.59|-0.06|(0.08)|147|

Notes: The table shows the results from a follow-up survey that elicited beliefs about the offer and beliefs about loan repayment and creditworthiness from a random sample of survey participants who had received either the relationship or regulator forbearance offer.

39
_Figure 4. Border effect and protectionism in government procurement by sector_ _Notes: The dot is the point estimate of the coefficient on the interaction between the internal trade dummy and an indicator for a protectionist feature of the importer’s law in government procurement. The bars are 95% confidence intervals. The dependent variable is bilateral imports in the primary, manufacturing or services sectors, as indicated. The gravity specification includes the full set of bilateral controls and interactions between the internal trade dummy and other importer-specific variables (see column (4) in Table 3 and odd-numbered columns in Table 4). All regressions include importer-sector-year and exporter-sector-year fixed effects. Standard errors are three-way clustered by undirected country-pair, importer and exporter._ We also estimate the gravity specification using the TiVA data and distinguishing between government procurement and private bilateral imports. The results are reported and discussed in section A.2 in the Appendix. They are consistent with the evidence from the cross-country regressions suggesting that the negative relationship between trade and protectionism in government procurement is present for public and private purchases. If anything, the enlarging effect of protectionism on the border effect is slightly stronger for bilateral trade in private markets than in government procurement.

# V. Concluding remarks

In this paper, we introduce a novel dataset on the protectionist content of national laws across 141 countries and investigate how it relates to trade openness. The information on national laws concerning government procurement was organized into three areas representing the main policy tools that governments use to discriminate against foreign firms, goods and services (lack of a national treatment clause, preferential treatment for domestic firms, and domestic sourcing). For each of these areas and for the combination of the three, we constructed a dummy variable equal to one if the protectionist character emerges from the law.

The data reveals that protectionism in government procurement is widespread. Around 36 percent of the countries lack a general provision of equal treatment between domestic and foreign firms when it comes to government procurement. Only in 17 out of 141 countries 19
shifting either women’s labor force participation or the actual distribution of household labor especially challenging.

The results also suggest that what may matter most for altering the distribution of labor is not abstract attitudes toward gender roles, but men’s reactions to their own life circumstances and how these intersect with conceptions of masculinity. Research from other contexts has shown that high-earning women may compensate for their violation of gender role norms by taking on a disproportionate share of household labor (Bittman,

> 19. These figures are not reported in the dissertation but are calculated from responses to the telephone survey described therein.

32