rafmacalaba/data-use-mentions-extended huggingface dataset:
)|[0.00]|5.08|(0.86)|586|
|Loans given pre-frst-lockdown (=1)|0.02*|(0.01)|[0.21]|0.00|(0.06)|1,610|
|Loans given during frst lockdown (=1)|0.00|(0.00)|[0.40]|0.00|(0.00)|1,610|
|Loans given after frst lockdown (=1)|0.00|(0.00)|[0.77]|0.00|(0.04)|1,610|

**_Notes_** :

- The table shows the regression results on pre-specified COVID-19-related outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The periods referred to in the tables are before lockdown: (January 15 - March 15, 2020); during lockdown: (March 15 - May 15, 2020); and after lockdown: (May 15 - interview date). Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values
Battistin E. and B. Sianesi. 2011. “Misclassified treatment status and treatment effects: an application to returns to education in the United Kingdom.” _Review of Economics and Statistics_ 93(2): 495–509 Behrman, Jere R., Mark Rosenzweig, and Paul Taubman. 1994. “Endowments and the allocation of schooling in the family and in the marriage market: the twins experiment.” _Journal of Political Economy_ 102 (6): 1134–1174.

Behrman, Jere R., Mark Rosenzweig, and Paul Taubman. 1996. “College choice and wages: estimates using data on female twins.” _Review of Economics and Statistics_ 73 (4): 672–685.

Biemer, P., R. Groves, L. Lyberg, N. Mathiowetz, and S. Sudman. 1991. _Measurement Error in Surveys_ . New York: John Wiley & Sons.

Bingley P., K. Christensen, and I. Walker. 2005. “Twin‐based Estimates of the Returns to Education: Evidence from the Population of Danish Twins.” mimeo.

Blundell, R., L. Dearden, and B. Sianesi. 2005. “Evaluating the Effect of Education on Earnings: Models, Methods and Results from the National Child Development Survey,” (with L. Dearden and B. Sianesi) _Journal of Royal Statistical Society Series A,_ Blundell, R., M. Costa‐Dias. 2009 “Alternative Approaches to Evaluation in Empirical Microeconomics,” , _Journal of Human Resources, 44(3), 565‐640_ Bommier, A. and S. Lamber. 2000. “Education Demand and Age at School Enrollment in Tanzania.” _The Journal of Human Resources_ 35(1).

Bound, J., C. Brown, and N. Mathiowetz. 2001. “Measurement Error in Survey Data.” In _Handbook of Econometrics_ Vol. 5. ed. J. Heckman and E. Leamer. Amsterdam: North‐Holland, Elsevier
2013, Pages 52–78, https://doi.org/10.1093/wbro/lkt001 Duflo, Esther. 2003. “Grandmothers and Granddaughters: Old Age Pension and Intra-Household Allocation in South Africa.” World Bank Economic Review 17 (1): 1–25.

Dunbar, Geoffrey R, Arthur Lewbel, and Krishna Pendakur. “Children’s Resources in Collective Households: Identification, Estimation, and an Application to Child Poverty in Malawi.” American Economic Review 103, no. 1 (February 1, 2013): 438–71. https://doi.org/10.1257/aer.103.1.438.

Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen (eds.). 2022. World Values Survey: Round Seven - Country-Pooled Datafile Version 5.0. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. doi:10.14281/18241.20 Kling, J., Liebman, J., and Katz, L. (2007). Experimental analysis of neighborhood effects. Econometrica, 75(1):83–119.

Lépine, A., and E. Strobl (2013). ‘The Effect of Women’s Bargaining Power on Child Nutrition in Rural Senegal’. World Development, 45: 17–30. https://doi.org/10.1016/j.worlddev.2012.12.018 John A. List & Azeem M. Shaikh & Yang Xu, 2019. "Multiple hypothesis testing in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 773-793, December.

Lundberg, S. J, R. A Pollak, and T. J Wales. 1997. “Do Husbands and
.9|9.9|0.0|79.9|0.03|0.01|0.15|
|Medium-Low Risk|0.04|77.0|5.7|0.0|77.0|0.03|0.01|0.20|
|Low Risk|0.03|72.1|2.1|0.0|0.0|0.00|0.03|0.22|
|No Risk Data|0.10|82.5|18.2|0.0|82.5|0.08|0.02|0.26|
|Total: Formal Firms|0.27|76.4|7.4|0.0|76.4|0.21|0.06|1.07|
|_Panel B: Formal Firms + Natural Persons_|||||||||
|Formal Firms|0.27|76.4|7.4|0.0|76.4|0.21|0.06|1.07|
|Natural Persons|0.19|82.5|18.2|0.0|82.5|0.16|0.03|0.47|
|Total: Formal Firms + Natural Persons|0.45|77.8|9.8|0.0|77.8|0.35|0.10|1.54|

This table shows the distribution of the aggregate expected loss borne by the government and the banking system as a result of the credit program, for natural persons and the formal firms sample when the deductible is zero. Panel A reports statistics across the firms’ risk distribution. Panel B reports statistics separately for formal firms and natural persons. Column 1 shows the total expected loss as a share of GDP. Columns 2–
# **E.12 Placebo Test with Nearest Neighboring Out-of-sample Sections**

We calculate the distances between the centroid of a section in the sample and all out-of-sample sections. We then use the minimum distance to identify the nearest neighbor.

**Table E.12:** Placebo: Reported Cases using Nearest Neighboring Section

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Ebola Cases|||||||
|Total|0.222|0.057|0.043|0.069|-0.026|5,440|
||(0.957)|(0.063)|(0.076)|(0.072)|(0.078)||
|Confrmed|0.029<br>(0.371)|0.01<br>(0.023)|0.001<br>(0.03)|0.016<br>(0.027)|-0.015<br>(0.033)|5,440|
|Negative|0.169<br>(0.653)|0.042<br>(0.04)|0.033<br>(0.047)|0.049<br>(0.046)|-0.015<br>(0.047)|5,440|
|IHS(Ebola Cases)|||||||
|Total|0.153|0.041|0.033|0.048|-0.015|5,440|
||(0.419)|(0.031)|(0.036)|(0.035)|(0.036
prohibits the employment of contract Labor in certain categories of work in certain sectors (for example in mines, on railways, and port facilities), and variously regulates work conditions of contract Laborers (cf. Mitchell et al., 2014).

> 28 Among other provisions, the Act mandates that if the contractor fails to pay contract workers’ wages, the principal employer is liable to pay the workers (ILO, 2008).

> 29 The 2005-2006 Agricultural Census of India documented a national average size of agricultural landholdings of around 3 acres, the lowest average ever recorded. Chakravorty (2012) notes however how the national figure masks significant cross-state variation in plot sizes: while some States like Bihar and Kerala have fragmented, very small holdings, others such as Gujarat, Haryana, Madhya Pradesh, Punjab, or Rajasthan, have much larger, more consolidated ones.

> 30 A non-agriculturist is defined as an individual not involved in the cultivation of crops and lacking family ties to agriculture.

17
(one at a time) control variables for female labor force participation or the birth rate (both from World Bank, 2006c), measures of corruption, the rule of law and bureaucratic quality from the International Country Risk Guide (ICRG) (The Political Risk Services Group, 2008), the sum of these three ICRG variables (as a general measure of institutional quality), and measures of democracy obtained from Polity IV (Marshall and Jaggers, 2007). Feyzioglu et al. (1998) control for the share of agriculture in GDP rather than urbanization. Therefore, I replace urbanization with the share of agriculture in GDP (from WDI) or add the share of agriculture in GDP alongside urbanization. Many papers also control for the size and composition of the population when explaining variation in public expenditures (e.g., Baqir, 2002; Rodrik, 1998). As a result, I consider models that add the percentage of the population under 15 and/or the percentage of the population over 65 to the model, the age dependency ratio (dependents of the working-age population) or the log of population (all from WDI). Finally, Feyzioglu et al. (1998) control for lagged infant mortality, whereas Devarajan et al. (2007) control for lagged secondary and primary school enrollment in the education expenditure equation and for lagged infant mortality in the health expenditure equation. A possible concern is that such variables may be more fruitfully viewed as outcomes than as determinants of public education and health expenditures. Nonetheless, I include either the current value or the once-lagged value of primary gross enrollment, secondary gross enrollment, infant mortality or under-five mortality (mortality data from WDI and enrollment data from Edstats). In all cases, the results are qualitatively unchanged. The only exception is that when the ICRG measures are added, the coefficient of health TC decreases to approximately -0.25, and I can reject the null hypothesis of no fungibility, implying partial (but low) fungibility of health
and labor to be less than 0.5. These studies use the variables capital, labor and energy as relating to stock of fixed assets, skilled and unskilled labor, and final energy consumption, respectively.

# **III. Estimation of Nested CES production function**

# **A.  The Nested CES production function and variables**

The estimation carried out here uses national level data on Gross National Income (GNI) or economic output and sees the extent to which variations in GNI across countries at any point in time can be explained in terms of the national availability of produced capital, human resources and natural resources (energy and land resources). A Cobb-Douglas production function of the form shown above is not appropriate for this estimation because it restricts the elasticity between factors to be one. In fact, one of our objectives is to estimate the elasticity of substitution between factors or groups of factors. A form that holds the elasticity constant but allows it to take values different from one is the ‘constant elasticity of substitution’ (CES) production function. In particular, this paper uses a **nested CES** production function. For example, a two level nested CES with three inputs takes the form:<sup>4</sup>

> 4 This model makes the further assumption of ‘homothetic weak separability’ for groups of inputs. _Homothetic weak separability_ means that the marginal rate of substitution between inputs in a certain group is independent of output and of the level of inputs outside that group (Chiang, 1984)
To calibrate the joint distribution of consumption and labor income, I use data from Lakner and Milanovic [2016]. The data set contains surveybased measures of _either_ household consumption or disposable income by decile. The calibration requires the joint distribution of labor income and 1 consumption for computing the term �0<sup>_u′_(</sup><sup>_ci,t_)</sup><sup>_li,t/Ltdi_.Iwillassumethat,</sup> for the bottom 9 deciles, consumption is given by disposable income and that income consists only of labor income ( _ci,t_ = (1 _− τt_ ( _li,t_ )) _wtli,t_ ). The top 10% is assumed to have no labor income and consumes only capital income. Formally, using _q_ ˆ _i_ to denote the estimated consumption or income of decile _i_ , I approximate: Note that ignoring the labor income of the top 10% vastly _understates_ the benefits from additional redistribution. The highest decile likely receives a relatively large share of aggregate labor income, and has the lowest marginal utility of consumption. Thus, instituting transfers from this group to the rest of the population is highly desirable from a welfare perspective. By ignoring the top 10%, the burden of financing the transfers falls only on the bottom 90%, which is less favorable. The assumption that the top decile receives no labor income is thus highly conservative in this context.<sup>2</sup> In addition, if taxes are progressive, then the approximation _li/L ≈ q_ ˆ _i/_<sup>�</sup> _j_<sup>_q_ˆ</sup><sup>_j_understatesthebenefitsfromadditionalredistribution.Underthe</sup> assumption that taxes are progressive, disposable income is more equally distributed than gross labor income. Note that the marginal benefits from

> 2At the same time, including the top 10% under the assumption that _ci,t_ = (1 _− τi,t_ ) _li
to income and its temporal evolution. Section 4 analyzes the linkages between financial integration and the sensitivity of consumption to income, and undertakes a battery of robustness tests. Section 5 provides a discussion of alternative explanations. Section 6 offers some concluding remarks.

# 2 Database and Econometric Framework

## 2.1 Database

We examine the evolution of the sensitivity of consumption to income changes using a rich database that includes 88 countries, including 22 advanced and 66 developing economies, over the period 1960-2011 for our baseline specification. The database includes time-series of income, consumption, measures of financial integration, world and country specific interest rates, and variables capturing various country-specific features.<sup>6</sup> The data series for income and consumption are from the Penn World Tables (PWT 8.1). Per capita real income and per capita consumption correspond to the measures of national output and private consumption, respectively. It is widely known that aggregate output and consumption data are nonstationary, whereas first differences of these variables are not. We employ a wide range of panel unit root tests to check the statistical features of output and consumption series in our database. The results indicate that the (log) level of income and consumption series are nonstationary, but their first (log) differences are stationary.<sup>7</sup> Hence, we follow the previous literature and carry out the analysis using first differenced series.

An ideal dataset to evaluate the impact of financial integration on the sensitivity of consumption to income changes should include direct measures of the effectiveness of restrictions on the capital account. Unfortunately, the available measures of restrictions on the capital account are much cruder for the broad set of countries in our sample. To maximize the information from available datasets, we use an array of rule-based (de jure) measures of financial integration. We also check the robustness of our findings by controlling for outcome-based (de facto) measures.

Our database includes multiple de jure measures of financial integration that are based on the
619|0.601|0.618|0.001|−0.017|
||(0.01)|(0.01)|(0.01)|(0.03)|(0.03)|
|_p_-value from_F_-test of joint significance on all above var|iables|||0.79|0.62|

_Source_ : Authors’ analysis from student survey. _Note_ : Columns (1)–(3) report means, with standard errors in parentheses. For columns (4) and (5), each row is one regression of the characteristic on treatment and spillover indicator variables, with the coefficient (standard error, clustered at the school level) on treatment and spillover reported. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. Panel A variables were provided by the schools before the baseline survey took place. The study randomized treatment assignment repeatedly until no _t_ -test comparing treatment to control for any covariate was larger than 2.0. Variables in Panel B became available only after assignment to treatment. The last rows in panels A and B report the _p_ -value on an _F_ -test of joint significance for all variables in the panel from a regression where the dependent variable is a treatment dummy (Column 4) or spillover dummy (column 5). Column 4 excludes the spillover group from the analysis, while column 5 excludes the treatment group from the analysis.
financial depth in our sample of countries: where _FINc_ denotes the level of financial development in country _c_ . We experiment with four indicators of financial depth: (i) Private Credit by Deposit Money Banks; (ii) Private Credit by Deposit Money Banks and Other Financial Institutions; (iii) Deposit Money Bank Assets; (iv) Stock Market Capitalization, each expressed as a share of GDP. The source for financial depth data is Beck, Demirg ̈u ̧c-Kunt, and Levine (2010).

13
revenues are, in fact, used to support the broader public good. Survey respondents in the DRC, as noted above, expressed strong in-principle support for taxation, but 40 percent of respondents said that in practice they had refused or would refuse to pay taxes. This is a reflection of the strikingly low level of trust in the way the revenues are used, with 70 percent of respondents believing that the government is very likely to misuse tax revenue, and another 15 percent finding it somewhat likely.<sup>137</sup> While survey questions on this specific point are not widely available across countries, public concerns about use of government resources appear widespread.

The core challenge facing governments is thus to demonstrate to taxpayers that tax revenues are in fact being used to fund national development. Doing so is likely to require two things: (1) effective strategies for highlighting the connections between revenue and services for taxpayers; and (2) actual improvements in service delivery outcomes as revenue collection increases. While

> 132 Aiko, R., & Logan, C. (2014). Africa's willing taxpayers thwarted by opaque tax systems, corruption. _Afrobarometer Policy Paper 7_ . Afrobarometer.

> 133 Paler, L., Prichard, W., Sanchez de la Sierra, R., & Samii, C. (2017). Survey on Total Tax Burden in the DRC, Final Report. Kinshasa: Department for International Development.

> 134 Ortega, D., Ronconi, L., & Sanguinetti, P. (2016). Reciprocity and willingness to pay taxes: Evidence from a survey experiment in Latin America. _Economia_ , 16 (2):55-87.

> 135 Ali, M., Fjeldstad, O-H., & Sjursen, I. (2013). To pay or not to pay? Citizens' attitudes towards taxation in Kenya, Tanzania, Uganda and South Africa. _World Development_ , 64:828-842.

> 136 OECD. (2013). What drives tax morale
<!-- Start of picture text --> Bulgaria 2020<br>Headcount poverty (AROP) by children age<br>40 38.1 36.6<br>30.3 31.0 30.7 31.9 32.0 29.9<br>30<br>24,4 25.9 26.4 25.6<br>22.9<br>214<br>20<br>16.2<br>10<br>0<br>Children aged 0-2 Children aged 3-5 Children aged 6-9 Children aged 10-13 Children aged 14-17<br>TM Market Income plus pensions TM Disposable Income mi Consumable Income<br>Source: Author estimations based on EU-SILC 2021 and HBS 2021 using CEQ methodology<br><!-- End of picture text -->
First, our results highlight a need to move beyond dyadic models of household bargaining and decision-making that are based on nuclear family structures. In most household surveys and collective models of the household, a woman’s decision-making ability within the household is bench-marked against that of her husband. As a result, programs and policies that are informed by these data and studies rely on strategies that seek to exclusively engage husbands as a means to improve women’s bargaining power and outcomes.<sup>56</sup> In many lowand middle-income settings, however, extended families are common and comprise a broader range of kinship structures, such as joint, multi-generational, and polygamous relationships within the household (Kramer, 2020). Variations in kinship strength and relative bargaining power across extended household members make it necessary to understand the influence of, and potentially even target, household members other than the woman’s husband on her wellbeing. More broadly, policy impacts on women’s well-being can vary by household structure in particular when extended families are prevalent (Heath et al., 2020).

> 56Examples of such strategies include couples’ counseling, “husbands’ clubs” or the creation of partnerfriendly spaces within the health sector (father-friendly clinics, couples-oriented child visits), among others (Bank, 2019; Davis et al., 2016; Hawkins et al., 2008; Sicouri et al., 2018; Tiedje and Darling-Fisher, 2003).

25
Table 1. Impacts of PEP on COVID-19 Resilience

||NPI<br>Adherence<br>Index<br>(1)|Medical<br>Access<br>Index<br>(2)|Protection against<br>Contracting<br>Covid-19 Index<br>(3)|Economic<br>Resilience<br>Index<br>(4)|
|---|---|---|---|---|
|_Panel A. ITT Direct Impacts_|||||
|1[_RAMVijk_ = 1]|0.191|0.282|0.253|0.422|
||(0.047)|(0.013)|(0.046)|(0.048)|
|R-squared|0.067|0.215|0.061|0.136|
|_Panel B. ITT coeffcient interact with_|_wave_||||
|_β_1 =1[RAMV_ijk_ = 1]_×_1[wave]|0.080<br>(0.094)|-0.102<br>(0.026)|–<br>–|0.043<br>(0.099)|
|_β_2 =1[RAMV_ijk_ = 1]|0.144|0.333|–|0.397|
||(0.074)|(0.020)|–|(0.080)|
|Wave Diff. Effect=_β_1+_β_2|0.224|0.232|–|0.440|
||(0.060)|(0.017)|–|(0.059)|
|R-squared|0.067|0.220|–|0.136|
|Observations(Both waves)|2,318|2,618|2,640|2,337|
occurred—collapsing the data by municipality and year. By our definition, a new school is introduced in a municipality if a new unique school identification number appears in the school panel.<sup>12</sup> Information on municipality codes allows us to locate every school in Brazil to the corresponding municipality. The school census provides information on primary school enrolment, the availability of nursery classrooms, and the number of pre-school classrooms, which we use as control.

_Childbearing data_ Data on birth outcomes come from the microdata of Brazilian vital statistics, which cover approximately 45 million births occurring between 1997 and 2009. Vital statistics data are based on birth certificates issued by health institutions or midwifes attending homebirths and are collected through the states’ health secretariats. The vital statistics microdata are publicly available through the System of Information on Life Births (SINASC) of the Brazilian public health system (DATASUS). These data provide information on the age and municipality of residence of the mother, as well as gestational length of the pregnancies, and the mother-reported race of the child.

For each year, we collapse these data to create a summary measure of births by municipality and mother’s age at conception.<sup>13</sup> We calculate age at date of conception using information on gestational length recorded in the birth certificates to provide a municipal panel of births by mother’s age at conception.<sup>14</sup> Brazilian vital statistics data show close to universal coverage of all occurring births; information from the 2010 population census shows that more than 99% of all births occurring between 2000 and 2010 were registered and entered into the vital statistics data

> 12 We restrict the school count to schools that are active in a given year and report a positive number of enrolled students to limit measurement error in the school panel.

> 13 This works in the Brazilian context, as the school year coincides with the calendar year.

> 14 Using date of conception rather than date of birth of the child