rafmacalaba/data-use-mentions-extended huggingface dataset:
banks have.

The empirical framework is altered as follows: _Log(Exportsijt)_ = _α_ + _βFinancial developmentit_ + _γRegional trade-partner bank?ijt_ + _Xijtδ_ + _εijt_ (3) _Log(Exportsijt)_ = _τ_ + _φFinancial developmentit_ + _χShare(Regional trade-partner bankijt)_ + _Xijtψ_ + _ωijt_ (4) where _Trade-partner regional bank?ijt_ is a dummy variable that equals one if Turkish subregion _i_ has a bank from countries close to trade partner _j_ , and _Share(Trade-partner regional bankijt)_ is the share of branches of the bank from countries close to the trade partner. We consider countries that are close to the trade partner to be those within the same region as the trade partner.<sup>6</sup> The other variables in Equations 3 and 4 are defined as above.

Finally, we want to distinguish the effect of the information channels before and during the crisis. A dummy variable that captures the crisis period will be interacted with the variables for the financing and information channels in the regressions. The dummy _Crisis?t (Yes=1)_ takes the value one for the years 2008 to 2010, the last year in our sample. As such it captures the latest financial crisis, taking into account the delay in contagion from the US.

The analysis will use data on 26 Turkish sub-regions and 180 partner countries between 2002 and 2010. It primarily relies on two data sources. First, NUTS 2 sub-regional economic data come from Celbis, Nijkamp, and Poot (2014).<sup>7</sup> The NUTS 2 sub-regional economic data are collected by Celbis, Nijkamp, and Poot (2014), and provide information on the main variables used in the analysis. The sub-regional exports ( _Exportsijt_ ) is the total value of bilateral exports of sub-region _i_ to country _j_ at time _t_ .<sup>8</sup> Sub-regional gross value added ( _Regional GVAit_ ) is obtained from Turkstat and the trade-partner country gross value-added is from the UN Statistics Division
0.010)|0.010<br>(0.011)|
|Observations|3,550|3,457|3,550|3,457|3,550|3,457|3,550|3,457|3,550|3,457|
|R-squared|0.012|0.039|0.010|0.038|0.013|0.039|0.013|0.041|0.009|0.038|
|Number of households|717|717|717|717|717|717|717|717|717|717|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|

_Note_ : Dependent variable is the standardized CSI. Data used are the six visits of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 35
whether there is a persistent effect of the savings default. Results for dependent variables are in levels of Malawi kwacha (MK) to facilitate the interpretation of the economic importance of treatments.

Next, we look at spending in several categories, one and two weeks after the transfer. Recall that expenditure data were collected before prizes were announced (survey round 1). Specifications incorporate the pre-treatment value of outcomes and fixed effects as in equation (1). For outcomes measured with survey data, we use OLS to estimate separately using data one week after the transfer (Panel A) and two weeks after the transfer (Panel B). We collected two-week follow up data only for the subset of respondents who received transfers immediately or with one day delay, so the sample size in Panel B is smaller than in Panel A, and differences between the point estimates of coefficients in the two panels reflect both the difference in the sample and any change in the impact over time.

Finally, we study the effect of payment delay on expenditures. The dependent variable is expenditures one week post-transfer since using the two-week follow up data would preclude using the eight day delay treatment group. The specification we run is with village and week-of-first-survey fixed effects and standard errors computed as in equation (2). Here, _β_ 1 is the marginal effect of a one day delay compared to an immediate payment, while _β_ 2 is the marginal effect of an eight day delay instead of immediate payment, averaged across cash and direct deposit treatments.

# **5 Savings default results**

## **5.1 Administrative outcomes**

Estimates of equation (1) test whether the savings default had any impact on immediate cash-on-hand. Since we argue that the transaction costs associated with receiving the full transfer in (or converting it to) cash were equal, the mode of payment should affect outcomes only through psychological channels. Differences in bank activity for cash compared to direct deposit recipients is sufficient (though not necessary) evidence that savings defaults
**Table 4:  Daily Average PM10 Exposure by Age and Gender***

||Typical Hou<br>|sehold**<br>|236 Mo<br>Househ<br>|nitored<br>olds***<br>|
|---|---|---|---|---|
|Age|Female|Male|Female|Male|
|0-1|216|214|209|195|
|1-5|212|212|199|192|
|6-8|173|172|156|163|
|9-19|207|174|196|194|
|20-60|227|116|221|118|
|60+|220|161|264|188|

* Outdoor PM10 = 50

**  PM10 concentrations:  cooking area 260; living area 210

***  Averages for 236 separate calculations using monitored PM10 levels

Differences in the two sets of results stem from variations in average pollution levels, household age-sex compositions, and time allocations by household members in the full set of monitored households. We present the results for the typical household in order to display exposure variations when inside/outside pollution concentrations and individuals’ time allocations are held constant. In any case, the two sets of results are quite similar. The most striking finding is the high exposure -- around 200 -- for infants and children, regardless of gender. Exposures for student-age individuals (619) are somewhat lower (although still quite high), and also similar for both sexes. The real genderbased divergence occurs among adults, with women’s exposures nearly twice those for men in the age group 20-60, and about 40% higher for older women (over 60).

Table 4 indicates that only adult males aged 20-60 have daily PM10 exposures low enough to approach the Indian standard (100 ug/m<sup>3</sup> ). All other household members have significantly higher exposure levels
by a handful of small NGOs, operating mainly in urban centers (Krekó and Scharle, 2020).<sup>4</sup>

# **3 Data**

The analysis is based on an individual-level linked employer-employee administrative panel database, covering a randomly selected half of the population of Hungary in 2003, who are then followed up until 2017. The database consists of linked data sets at the monthly frequency of the pension, tax and health care authorities and contains detailed individuallevel information on employment and earnings history, use of the health care system, pension and other social benefits, and firm-level indicators. Importantly, it also contains information on the type and amount of different disability benefits and old-age pensions received. Two important limitations of the data are that the employment status of DI recipients cannot be observed until April 2007 and we do not observe the health condition based on which the disability benefit is received. Based on the 2011 census (Appendix Table A1), the majority of DI recipients suffer from long-lasting diseases. Among those recipients who have an impairment, mobility impairment is the most prevalent form of disability.

When estimating the effects of the reform, we analyze the following monthly indicators of labor market and DI status. DI status is a binary variable that takes value one if the individual is DI recipient in a given month and zero otherwise.<sup>5</sup> The binary variable for employment status equals one if the individual is employed on the 15<sup>_th_</sup> of the given month and zero otherwise. Employment includes self-employment but excludes public work. Importantly, employment was always allowed while receiving DI benefits, with restrictions on the maximum possible earnings.<sup>6</sup> We analyze public work as a separate outcome.<sup>7</sup> Based 4In contrast, the Netherlands provided access to a wide range of active labor market programs (Drøpping, Hvinden and van Oorschot, 2000) and introduced a temporary program to cushion
that for countries where labor is relatively cheaper, exports would be more labor intensive given these countries’ comparative advantage being in more labor intensive products.

The agenda for further research is rich, starting from the need to explore the causal determinants of the patterns identified in the data. This paper has not gone further than basic correlations. More systematic analyses of the determinants of the labor value added and jobs in exports - possibly trying to exploit shocks exogenous to countries – would be welcome. Going forward it will also be important to reconcile the various existing data, such as GTAP, TiVA and Eora, from which the labor value added in exports can be extracted. This would allow filling the existing data gap (for example this data set has still only 60% of the country coverage) as well as improving the quality of the data.

34
theory that reduced opportunities can lead to religious revival (Binzel and Carvalho 2017), unrest (Campante and Chor 2012, Urzúa 2012, Flechtner 2017) and terrorism (Brockmeyer et al. 2020).

Third, aspirations are inherently social and undoubtedly circumstantial. Theory makes a strong case for it, especially for the poor who may come to believe that they can achieve little (Appadurai 2004; Ray 2006; Dalton, Ghosal, and Mani 2016). Others—such as parents, peers, teachers, leaders, etc.— exert tremendous influence on aspirations by acting as role models or promoting social norms. Empirical studies show that role models (Beaman et al. 2012; Macours and Vakis 2014, 2016; Riley 2019; Breda et al. 2020) as well as neighbors or peers (Bernard et al. 2014; Gagete-Miranda 2020; Galiani, Gertler, and Undurraga 2021) influence one’s aspirations and outcomes. The place of residence, living standards and life experiences can weigh heavily on what one gets to know and perceives as possible. For example, empirical evidence shows more directly that adverse circumstantial factors such as conflicts and natural disasters can crush hope and aspirations (Kosec and Mo 2017; Moya and Carter 2019). This is consistent with the nonlinear evolution of educational aspirations of adolescents over time, suggesting that their aspirations evolve with their circumstances (Favara, Chang, and Sánchez 2018).

Fourth, while the bulk of empirical studies points to consistent insights, measurement issues can challenge our understanding of the issue across studies. All empirical measures of aspirations are based on self-reported questions from surveys. However, aspiration measures often differ in the underlying concept they capture but they are rarely the subject of validation or even discussed. Most authors have a common understanding of aspirations as multidimensional and motivating, but the questions used to capture them in surveys differ in their formulation, possibly leading to different results and policy conclusions (see annex 3 for the list of the measures of aspirations used by the empirical studies
children 12–24 months old, but minimal effects on older cohorts. The effects are also smaller than those estimated after crop failure and civil conflict in Rwanda, where Akresh et al. (2011) found impacts between -0.5 and -1.0 SD. Like Guatemala, these latter two settings are ones in which undernutrition is also highly prevalent, leading us to hypothesize that children in areas with more severe undernutrition may be more sensitive to large shocks, as they already begin at levels near stunting and likely have few physical reserves.

# _4.4 Sensitivity analyses and individual-level attrition_

In this section, we consider the extent to which results shown in Tables 2 and 3 are robust to different specifications, including alternative definitions for migrant households and alternative age cohorts of children. Additionally, we provide an assessment of the potential effects of attrition for the cohort panel analyses.

# _4.4.1 Alternative definitions of migration_

The definition of a migrant household was motivated by the patterns of migration and by the period of vulnerability in child growth to focus on children likely to have been influenced by the economic crisis. In this section, we examine two alternative definitions: 1) the household had a migrant to either the United States or Mexico in three of the four years when the child was under three years old; and 2) the household had a migrant to the United States in as few as one of the four years when the child was under three years old. Both alternatives likely capture reduced exposure, in the first case because the economic shock emanated from the United States (and there was evidence of migration shifting to Mexico) and in the second case because of a shorter potential exposure, which may be important as it often requires about a year to pay back migration-related travel debt. For both, however, there is some overlap with the primary definition.<sup>xxiv</sup>
and table 3). Note, however, that the share of adults per household among the poor decreased in some cases, notably in Panama and Colombia.

Second, growth in labor income could be the main driver in the observed changes in poverty. This could be due to greater employment, or due to an increase in earnings. As shown in figure 5 and table 4, the share of occupied adults per household increased for most of the countries in our sample. In some cases this was related to important increases in female employment. For example, in Bangladesh, the female employment-to-population rate increased from 53 to 56 percent between 2000 and 2008, equivalent to a 6 percent increase. In Costa Rica, both labor force participation and employment of women increased by about 23 percent between 2000 and 2008.<sup>5</sup> However, there is also evidence that labor incomes per adult increased at the bottom of

> 5 World Development Indicators 2012.

5
refugee population is associated with a 3.7% increase in the luminosity index within 10 km of the camp, the equivalent effect on consumption is approximately (0.015 × 0.037) *100 = 5.5%. Therefore, the paper provides rather strong evidence that vicinity to the camp increases economic activity and household consumption for local residents. These findings are also consistent with those outlined above for the Kagera region of Tanzania.

# Congolese refugees in Uganda and Rwanda

The Democratic Republic of Congo (DRC, ex-Zaire) has been the center of the first (1996-1997) and second (1998-2003) Congo wars that saw several countries and several armed groups involved and the largest death toll for a single war since World War II. Up to 5.4 m people may have perished as a consequence of these wars, although the actual number of casualties remains a disputed issue. In addition to generating death and mass displacement within Congo, hundreds of thousands of Congolese fled to neighboring countries including Uganda and Rwanda. Two studies looked at the impact of these refugees on host communities in these two countries.

Kreibaum (2015) looked at Uganda and finds that, overall, the refugee presence increases monthly consumption of the host population. Increasing the number of refugees per 1,000 inhabitants by 10 increases consumption on average by 3 percent, which is about equivalent to one day’s income for the local population. Interestingly, this does not match the perception of the local population which feels that the presence of refugees decreases well-being. Taylor et al. (2016) look at Congolese refugees in Rwanda and find a positive effect of aid on the local economy. This is the only paper in our knowledge that uses a general equilibrium model supported by microeconomic survey data to assess the impact of cash and food aid assistance to refugees in camps on the local economy identified as host communities living in a 10 km radius from the camps. They find that each adult refugee receiving cash assistance increases the annual real income of
12 months. Source: EHCVM and World Bank estimates.

These descriptive statistics show that Sahelian households’ mechanisms for managing and coping with the effects of seasonality may be limited. All other things equal, this could leave households exposed to large seasonal swings in welfare. We now turn to the main results to assess whether this is the case.

# Section 4. Main results

In this section we present the main results, showing how welfare differs between the lean and non-lean seasons in the EHCVM sample. We first present the effects of seasonality on overall consumption and poverty. We then disaggregate these changes in consumption, showing how the data are consistent with 11
comparable framework. Building the database on which the following analysis is based was a huge job, and is itself an important contribution of this research.<sup>11</sup>

# **3.3 Estimation Strategy**

The econometric analysis explores the following three propositions for spatial or horizontal equity in service delivery.

_Proposition 1:_ Woreda block grant allocations are pro-poor, implying that β>0 in the following specification: ∆Gi, = α+βPi +γR+ δt + εj (1) where ∆Gi, is the deviation of the per capita block grant allocation from the mean (in percent), Pi is woreda poverty level based on the Ethiopia poverty map, and R and t are region and year dummies, respectively. If β>0, then the higher the woreda poverty level, the higher its allocation, and the proposition holds.

_Proposition 2:_ There is a negative association between woreda headcount poverty rate (Pi) and service delivery in that woreda (Θi), i.e., η<0 in the following equation: Θi=ηPi (2) If η<0, then the poorest woredas are also those where service delivery is lowest.

_Proposition 3:_ Following propositions 1 and 2, a block grant system that allocates proportionally more resources to the poorest woredas will also improve spatial equity in service delivery outcomes. We estimate this using the same approach as Khan et al. (2014), which showed that per capita block grant allocations were directly linked to service delivery outcomes using the following equation: ln _Oi_ =  +  ln _Ei_ +ηln _Ki_ +  _i_ (3) where _Oi_ is various outcome variables for woreda _i_ : for education, net primary enrollment rate; and for health, contraceptive use, antenatal care utilization, and births by skilled birth attendant; E is yearly expenditure per capita in the relevant sector; and _K_ is capital expenditure per capita. We additionally control for year, percentage of rural population
Probit panel-data models are used to generate a suitable instrument for each of the eight CCMs, separately. The general specification for the models is: (4) i 1C C i 2 i 3D i 4D i2 5 i + 6C C i 7C C C i 8 i 9 C i 10 i i ii Pr(yy = 1) = αα + ββ CC + ββ DDC OOhCC + ββ OO + ββ OO + ββ HHCCHH ββ CCh + ββ + ββ TTCCTTCC + ββ DDCCTTD DDyy + ββ DDCCTTDDf CCDDOO + εε where i = 1 indicates that at time _t_ the country implemented the CCM. Parameter indicates the existence of idiosyncratic characteristics at the country level, that prompts the use of the ii random effects models to avoid biasing the estimation. yy αα

**We include the same set of control variables when modeling the probability of a country having implemented a CCM** . The first two controls are the number of daily cases and daily deaths, for which we naturally expect 1 2 > 0. By nature, pandemics tend to spread in the population with an “S” shape which, in turn, depends on the time elapsed since the beginning of the contagion period (see Gatto et al, 2020; Scally 2020). We use the time in days elapsed since ββ , ββ the outbreak (i.e., the date when the first case was officially reported in each country), which we label as _DSO_ , and its squared value to capture the non-linear diffusion of the disease. Our prior is that 3 > 0 and 4 < 0. **Response measures to the pandemics reveal, to a large extent, the stance of the** ββ ββ

**Response measures to the pandemics reveal, to a large extent, the stance of the health system in each country** . Kandel et al (2020) evaluate health security capacities in 182 countries, using 18 indicators that reflect the ability of each country to prevent, detect, and respond to the pandemic
) rural parcel prices are US$ 438 and US$ 185. Thus, for the median rural property, registering a transaction would require a monetary outlay equivalent to 22.6% of property value, compared to urban properties in Kigali where the registration fee is only 0.64% of median property value. While it is not the only factor, making fee structures less biased against rural areas could possibly go a long way towards ensuring sustainability of Rwanda’s land registry.

# **4.2 Willingness to pay for registration and its determinants**

To explore willingness to pay for registering subsequent rural transfers, the survey asked those who had acquired but not registered land, to state the amount they would be willing to pay for registration. While 20% were not willing to pay at all, the mean for those with positive amounts is RwF 4,500; about 20% of the official fee and the median is RwF 1,000, equivalent to the fee charged for first-time registration of rural parcels.<sup>18</sup> We explore determinants of households’ willingness to pay (WTP) by estimating a cross-section regression of the form:

> 15 Information had to be obtained from household surveys, as RNRA does not maintain records in ways that allows easy identification of parties to past transactions. While it is true that, contrary to a deeds system, registration of the current owner is sufficient to document ownership, having a historic record could still be very valuable help reach decisions in case of dispute and the current practice of discarding it may not be prudent. 16 The lower level of informality for inheritance is due to a higher share of such transfers having been registered during LTR. ** 17 Although responses to questions about reasons for non-registration may have to be taken with a grain of salt if households lack information, this is consistent with the fact that about 13% of those who have not yet registered their transactions cited high registration fees as a major reason. 18 The
sample, we are considering only developing countries, and they enter the panel if they are independent at time t � 1. The data set employed in our analysis is an updated version of that used by Acemoglu et al. (2005) for the democracy indicators (except for economic freedom) and all the control variables. The migration data come from Defoort (2008).

# Democracy

Data on democracy are taken from the Freedom House data set, from the POLITY IV data set, and from the Economic Freedom of the World project (Simon Fraser

> 13For the level equation the following moment conditions are to be satis...ed:

> 14This test is not reported in the tables, but it is available upon request.

> 15See Roodman (2009)

> 16The xtabond2 command, implemented in Stata, gives a warning when instruments exceed the number of groups.

10