rafmacalaba/data-use-mentions-extended huggingface dataset:
in the time preference game), stake sizes that de Quidt, Haushofer, and Roth (2018) have found effectively eliminate demand effects.

Perhaps employed individuals feel a need to impress the enumerator, as their proximate employer, in a way cash recipients do not. This may lead to reporting better mental and physical health and investing greater effort in the cognitive tasks. However, we find that life satisfaction increases substantially for both groups, inconsistent with a differential desire to impress among the employed. We also observe patterns of treatment effects within our validated PHQ-9 module that are inconsistent with experimenter demand (Appendix Table

> 12The signaling value of the certificate may have been diminished if other employers learned about the nature of the certificate distribution. Our time in the field suggests this is unlikely: we randomized certificate distribution at the block level to limit spillovers, only five people in each block of<sup> ̃</sup> 200 adults was involved in the experiment, and job opportunities were scarce.

> 13The certificate read “I engaged with Pulse Bangladesh to do data collection”. It was written this way in order to be generic enough to apply to all the individuals in the experiment, all of whom were providing us data from the weekly surveys.

18
|**Plot level data**<br>Percent of output sold|Follow-up|Plot level data. Total quantity harvested and sold as share of total quantity<br>harvested in the long rainy season of 2010/11.|
|---|---|---|
|Maize yield (Kgs/acre) in 2009/10|Follow-up|Plot level data. Maize yield (KG/acre) for any type of maize seed during the long<br>rainy season of 2009/10.|
|Kg of Basal Fert. per acre|Follow-up|Plot level data. KG of basal fertilizer used per acre of area planted in the long<br>rainy season of 2010/11.|
|Kg of Fertilizer per acre|Follow-up|Plot level data. KG of top-dressing fertilizer used per acre of area planted in the<br>long rainy season of 2010/11.|
|Crop Failure|Follow-up|Plot level data. Dummy variable. Equals to 1 if yield is zero. in the long rainy<br>season of 2010/11.|
|Log Total Yield|Follow-up|Plot level data. Log of the total yield (KG/acre) in the long rainy season of<br>2010/11. Include zeros (crop failure) and uses planted area. Yields over 4000<br>KGs/acre were removed from the sample.|
|Standardized index of agriculture outcomes<br>**Link level data**|Follow-up|Plot level data. Standardized sum of 5 agricultural outcome variables, also<br>standardized before the sum: (i) area planted, (ii) kilograms of basal fertilier per<br>acre, (iii) kilograms of total fertilizer per acre, (iv) dummy that takes value of 1 if<br>yield is positive (1 - crop failure) and (v) log total yield.|
|Gives inputs to others|
estimation approach. Note, however, that only by randomizing the order of choices in the MPL and options within each binary choice researchers can test for any framing or anchoring effects in new samples.

A second limitation of our approach is that the correction of order bias can be applied to the study sample, or to large enough subgroups within the study sample, but likely not – to individual-level data. The implication is that MPL data carefully implemented and – analyzed is appropriate for measuring sample average WTP, but not individual-specific WTP. It should also be noted that inattention, incomprehension, and framing effects can lead to choice error in other elicitation methods too. Given the prevalence of these errors in WTP estimation, the method we propose in this paper offers a way forward: an estimation approach that correctly accounts for individual error and a measurement approach that reveals bias.

34
<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. 1988 revision<br>(prelir.)|

Notes: When a source for an estimate is directly cited (e.g.. UNPVSR 4/87), the estimate is taken as is from that publication. When an estimate Is "based on" a source, the estimate is not reported in but derived using data in that publication. When a source is given for a fertility or mortality estimate prior to 1985-90, the source for t e corresponding 1985-90 estimate is a projection usirg the earlier estimate. Citations for the main documents referred to here (usually by abbreviations) arein the list of references.

Nature of population estimate: F a de facto; J = de jure.

No census, or no fertility or mortality estimates and no projections made (the latter for small countries).

a Census was for entire peninsula. b 55 people.

CBR Crude birth rate. CDR Crude death rate.

Celade Centro Latino Americano de Demograffa (1987). DNS Demographic and Health Survey.

INED Institut National d'&tudes D6mographiques (Momier 1987).

TFR Total fertility rate.

UNPVSR United Nations, Population and Vital Statistics Report. USBOC United States, Bureau of the Census (1985).
and Brazil) and a an increase in some other</mark> (Türkiye).

<mark>For Brazil specifically, the differences are substantial. Using 2020 employment data, our simulations project poverty increases ranging between 3 and 5 percentage points depending on the poverty line used. When using employment elasticities to project the employment level of 2020, the expected poverty increases are below 2 percentage point (between 25% and 40% of the poverty increases using actual employment data). The large differences in Brazil, however, are in line with the earlier findings that the employment-GDP elasticities were underestimating the magnitude of</mark> 22
buying government agency. We leverage a detailed dataset of 1.2 million procurement contracts awarded in France and Spain between 2009 and 2019. In our dataset, we observe the winning establishment and the buying government agency, as well as their geographic location and contract characteristics.

**Classifying agencies into government-types.** A crucial part of our data work consists of correctly identifying and classifying more than 30,000 government agencies into “national” or “subnational” categories. National governments refer to agencies that belong to the national government (i.e., central or federal administration) but are located anywhere within the country. In contrast, subnational agencies belong to a government that exclusively operates locally (i.e., an individual region or territory within the country). Let’s consider again the case of Catalonia. In our data, we observe offices of the Spanish Ministry of Finance ( _Delegaci ́on Especial de la Agencia Estatal de Adminisistraci ́on Tributaria en Catalu ̃na_ ) and the Catalan Tax Authority ( _Departament d’Economia i Hisenda_ ). Both of these agencies have offices in Barcelona and we will observe them buying the same good from the same firm at a given point in time. Our classification procedure

> 5 Alternatively, one can exploit a slightly different type of variation by comparing how much a given firm sells same product locally vs. non-locally across government types. In the paper, we show that the two types of variation lead to similar results.

2
</sup> Our micro data for Moldova have fully supported in this respect the macro data we showed for the CIS-7 and provided by CIS-Stat. From the probit regressions on stayers and movers we concluded that disposable income is not what is attracting workers into individual farming. By default, we have to conclude that other factors have been at work.

Possibly, self-employment during the recession phase can be understood as a combination of structural reforms (privatization) and as an escape from unemployment and poverty. The Schumpeterian entrepreneur was rare among the self-employed. We did not find evidence that selfemployment constituted a springboard to SMEs creation. It is rather the dismantling of collective agriculture and the lack of growth in agricultural and non-agricultural enterprises that is still pushing people into individual farming contributing to explaining these trends.

Past research on Kazakhstan has shown two aspects that could further support these conclusions.<sup>24</sup> One is that poverty among the self-employed was found to be much higher than poverty among the employees at the end of the recessional period in 1996 suggesting that selfemployment could hardly be considered an attractive status for the employees. And the second is that when employment in the formal sector started to expand in 2002, self-employment rather than unemployment has constituted the recruitment ground for enterprises. Many of the selfemployed were evidently in the waiting for a better opportunity to join the employees and remained constrained for many years in the subsistence self-employment 'trap'. While in Kazakhstan some signs of revival in the industrial manufacturing sector are visible, this phenomenon is still not happening in the CIS-7.

> 22 We do not use the wage as explanatory variable because the variable is not provided in the sample but also because disposable income is a much better indicator to compare non-wage with wage workers.

> 23 Transition probabilities were also calculated for the entire samples, including non panel observations. Results were very close in size and the described trends almost identical indicating that the households panel is not a significantly different sample from the entire longitudinal sample, at least in
At the same time, the rapid proliferation of publicly available “big data” from satellite and crowdsourcing applications has made geographically comprehensive data freely available, while survey enumeration area geocoordinates or administrative shapefiles required to link surveys to geospatial data are also becoming increasingly available. These developments have sparked a burgeoning literature – ably reviewed in Burke (2021), McBride et al. (2021), and World Bank (2021) – on the use of satellite imagery for economic measurement. Most early work has focused on the ability of geospatial data to predict agricultural yields and crops (Erciulescu et al., 2019, Lobell et al., 2020), household asset wealth and poverty (Jean et al., 2016, Yeh et al., 2020 Steele et al., 2017, Engstrom et al., 2021), and population (Wardrop et al.., 2020, Engstrom et al., 2020).

This is the first paper to our knowledge that explores whether combining survey data with geospatial indicators can be used for the purposes of improving estimates of labor market outcomes, such as female and male labor force participation and unemployment, through small area estimation. Demonstrating that this methodology works is important, as many developing countries do not have the requisite data traditionally used for small area estimation, such as up-todate censuses or detailed administrative data. We show that the incorporation of geospatial data – which is available across the globe – is a feasible alternative when preferred sources of auxiliary data are not available.

This paper also contributes to the ongoing discussion over the proper methods for combining survey and geospatial data for the purpose of prediction. Some analysts take an explicitly Bayesian approach (Steele et al., 2017, Pokhriyal and Jacques, 2017, Erciulescu et al., 2019), while others employ an empirical best predictor model (Battese Harter and Fuller, 1988 Masaki et al., 2020).

We undertake this exercise in the context of Mexico, which has publicly available data that makes our approach feasible.
|Furukawa<br>(2014)*|Student|Uganda|Education|Solar lamps increased reported study time, but nonetheless lowered<br>student test scores, perhaps due to inappropriate usage.|
|---|---|---|---|---|
|Aevarsdottir et<br>al (2017)+|Household|Tanzania|Consumption/<br>Jobs|Solar lamps, reduce expenditure on lighting and phone charging, and lead<br>to more employment and greater household consumption and well-being.|
|Aklin et al (2017)|Household|India|Welfare|Access to off-grid solar power decreases black market kerosene<br>expenditures, without systematically affecting other outcomes.|
|Ratledge et al<br>(2022)|Village|Uganda|Welfare|Grid access improves village-level asset wealth in rural Uganda by up to<br>0.15 standard deviations.|

Source: Compiled by the authors.

Note: Findings column is based on the main finding of the paper. * represents peer-reviewed papers. Plus identifies papers following the instrumental variable approach.

As a part of a field experiment in rural India, randomly chosen households were offered to set up a solar microgrid if at least 10 households within the area subscribed at a price that corresponds to about 2 percent of the monthly baseline household expenditure. The electrification rates for the households offered solar power under the experiment increased around 30 percent faster than other households. However, the study was not able to identify any consistent impacts of electrification, such as the use of lighting for study or power for the creation of new businesses (Aklin et al, 2017).

# **4.2 Reliability of Supply**

Based on World Bank Enterprise Surveys conducted between 2010 and 2020, 8.4 percent of the firms in the world chose electricity as the biggest obstacle affecting their businesses. The main reason for this is the unreliability of power supply, with outages
, one would expect this to lead to an immediate increase in demand for personal loans, but not for the non-credit product which merely signals a continued commitment to the lender that preserves the option of obtaining a loan in the future but has no immediate payoff to the borrower.

We find evidence that forbearance offers granted by one’s lender strengthen demand for future engagement with the lender. While there are no meaningful differences in demand for personal loans between treatment conditions, borrowers who received a moratorium offer from the lender in the main experiment have 10 percentage points (27 percent) higher demand for the branded non-credit product than borrowers who received debt forbearance offers framed as an initiative of the regulator. Because there is no scope for ex post renegotiation in the case of the non-credit product, the results suggest that borrowers wish to deepen their relationship with the lender that granted forbearance for reasons other than making the inference that it is easy to renegotiate financial contracts with the lender. These findings from a fully incentivized product adoption experiment indicate that, in our setting, forbearance offers appear to create neither moral hazard in short-term loan repayment nor moral hazard in the longer-term selection into banking relationships.

Our results have several implications for the design of debt relief policies. Regulators and private financial institutions have often been reluctant to grant temporary repayment deferrals because of the widespread assumption that such policies can damage credit discipline. Indeed, in a survey representative of the Indian population, conducted as part of our study supports this assumption —66 percent of respondents state that they expect debt moratoria to have a negative effect on timely 4
_Production (percent)_|13.3|38.8|7.1|3.3|10.7|73.2|26.8|100|

**Notes:** Yield is measured as MCal per hectare. The changes from 1961-63 to 2019-21 are calculated as logarithmic changes. Some numbers may not add up due to rounding. Acronyms denote regions according to the FAO classification: LAC (Latin America and the Caribbean), SEAO (South and East Asia, and Oceania), EECA (Eastern Europe and Central Asia), MENA (Middle East and North Africa), SSA (Sub-Saharan Africa). EMDE (Emerging Markets and Developing Economies) denotes the sum of the five preceding regions. AE (Advanced Economies) includes North America, Northern Europe, Western Europe, and Southern Europe. All refers to all regions.

**Source** : Authors’ calculations based on FAO data.

_— 20 —_
> 900,000 45<br>800,000 40<br>700,000 35<br>600,000 30<br>500,000 25<br>400,000 20<br>300,000 15<br>200,000 10<br>100,000 5<br>0 0<br>2001 2004 2007 2001 2004 2007<br>Total<br>International<br>Total International Intercontinental Domestic<br>Intercontinental excluding flights between NA and SSA<br>Number of seats<br>Number of city pairs-<br><!-- End of picture text -->

## Source: Bofinger 2009.

Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data Note: As reported to international reservation systems Like many other African countries, Mali continues to face significant safety and security issues in air transport. Mali failed the FAA/IASA Audit, and none of its carriers have passed the IATA/IOSA audit.

18
)|(0.0051)|
|||||[0.0058]|[0.0075]|[0.0050]|
|Intercept|7.59|6.78|-0.81|7.59|6.73|-0.86|
||(0.11)***|(0.12)***|(0.17)***|(0.12)***|(0.13)***|(0.18)***|
||[0.36]***|[0.42]***|[0.32]**|[0.40]***|[0.48]***|[0.33]**|
|No. Observations|3955|4085|8040|3955|4085|8040|
|||**Estima**<br>|**tes of Mobility**|**from Quadrati**|**c CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|7.59|6.78||**_IGRC_**|0.27|0.33|
|**_ES6 _**|9.21|8.76|||||
|**_ES9_**|10.02|9.75|||||
|**_ES16_**|11.91|12.06|||||

Notes: (1) The data used are CFPS 2016 with children aged
3 and Oates (1988) found for the sample of the US states that only population and urbanization variables robustly positively affected the degree of fiscal decentralization at the state level. Patsouratis (1990) studied fiscal decentralization in a sample of 11 EU countries and reported robust positive effects of population size and per capita income variables. Inter-American Development Bank (1997) looked at fiscal decentralization in a small sample of Latin American countries and found that larger country size, larger population, formally federal structure, and better development of democratic institutions were conducive to fiscal decentralization. Arzaghi and Henderson (2005) studied the determinants of fiscal decentralization in 41 countries (looking only at government consumption as opposed to total government expenditure) and found that fiscal decentralization was associated with higher per capita income, smaller size of the main metropolitan area, formally federal structure, and higher levels of democracy. For a more detailed review of the empirical literature, see Cerniglia (2003) and Letelier (2005).

Freinkman and Yossifov (1999) first studied the determinants of fiscal decentralization in Russian regions and found for the period of 1994-96 that historically more developed regions and regions with lower inflation tended to be more decentralized. This paper extends the analysis of Freinkman and Yossifov (1999) to cover an expanded time interval and, more importantly, to check a much more developed set of decentralization hypotheses. At the same time, the paper intends to apply to Russia the framework developed by Wallis and Oates (1988) for the analysis of decentralization in the United States.

Our initial statistical analysis revealed that the conventional hypothesis about the determinants of fiscal decentralization fails with the Russian regional data. Two coefficients in our base model (income and ethnolinguistic fractionalization) are significant but of the strictly opposite signs relative to the priors. Another core variable (democratization) remains statistically insignificant. Only urbanization and regional size variables have the hypothesized signs and are statistically significant in most specifications. This finding encouraged further analysis that points
the date the law was passed. As will be shown below, this explains in great part the disagreement between the studies.

This paper revisits the identification strategy used in both studies and performs a thorough check of the RD estimates of the impact of the SIMPLES program on firms’ formalization rates. I focus on the RD estimates because the identification strategy of both RD and DID relies on a jump in formalization rates after the program started. Notice that an invalidation of the first-stage results would call into question any treatment effect estimate of the program on outcomes related to firms’ performance obtained through an IV method.

In my analysis I use the same data set as in both studies, the Brazilian Survey of the Urban Informal Sector (ECINF) collected in October 1997 by the Brazilian Bureau of Statistics (IBGE), but in a format that is closest to Monteiro and Assunção (2012), that is, I do not restrict the sample as in Fajnzylber et al. (2011), and I look at the sample of all micro firms without distinguishing between micro-entrepreneurs and small firms.<sup>2</sup> It is important to emphasize that this paper should not be seen as a replication exercise. The objectives of this paper are (1) to identify reasons for the studies coming to different conclusions, even though they used the same data set, and (2) to test the robustness of the observed jump in formalization rates detected by Fajnzylber et al. (2012).

Even though ECINF has more than one variable that could be used to capture formalization rates (see Fajnzylber et al. 2011), I will focus on the results obtained with the variable, ‘license’. This variable tells us whether a firm had a municipal or state license to operate. This variable was used in both studies to measure the impact of SIMPLES on formalization rates and to estimate the impact of formalization on firms’ performance through two-stage least squares by Fajnzylber et al. (2011).<sup>
I obtain qualitatively similar FE results with aid variables that are constructed by scaling up sectoral CRS disbursements to ensure that their sum matches the aggregate DAC2a disbursements (equation (5) but applied to CRS disbursements rather than commitments). The main change is that for some aid variables, the estimated coefficients are closer to zero and/or estimated less precisely, which is consistent with greater measurement error in the aid data that are constructed using this short-cut method.<sup>21</sup> Table 4 illustrates that a failure to properly address the presence of off-budget aid may yield misleading conclusions. After on- and off-budget aid are separated and their effects are assessed against appropriate benchmarks, the FE results suggest that there is little if any fungibility. This conclusion is robust to a large number of specification changes. I replace the PV of debt with a non-PV measure of long-term external public and publicly guaranteed debt expressed as a percentage of GDP (from World Bank, 2006b). I also add to the model, in turn, two different measures of the PV of debt relief constructed by Depetris Chauvin and Kraay (2005).<sup>22</sup> Because debt relief is often linked to higher social expenditure, one might expect it to have a larger positive effect on public education and health expenditure than the effect achieved by a reduction in debt or debt service that arises through means other than debt relief. If this effect is indeed larger, then we would expect a positive effect of debt relief even after controlling for the level of debt and debt service. However. I do not find evidence of this effect. Even without controlling for debt and debt service, I find no effect of the PV of debt relief. I further include GDP per capita in log form rather than in thousands of dollars. I add 16