rafmacalaba/data-use-mentions-extended huggingface dataset:
-3-

The overall macroeconomic situation, which sets the broad incentive structure for the operations of banks, has affected the financial sector. As a result of predictable depreciation of the currency, banks borrow at cheaper rates abroad and lend it at high rates domestically, expecting to earn more in net interest income than they lose from currency depreciation. As a result of the moral hazard created by extensive government guarantees and the lack of effective prudential regulation and supervision, the banks take on substantial risks in their interest rate exposures due to maturity mismatches, in unhedged foreign currency positions, and in potential loan losses. Such risks and the existence of large confidence-sensitive claims by nonresidents make the banking sector vulnerable to business cycles and shifts in market sentiment. Although the state banks have much less exposure in market risks, their operations are distorted by their heavy involvement in quasi-fiscal activities and the failure of the government to reimburse the banks for the subsequent "duty losses". Their losses are a major source of liquidity risk to the banking system.

The rest of the paper is organized in three main parts. First, we analyze capital flows data and the determinants of capital flows and their impact on the real sectors of the economy. Seconcl, we consider macroeconomic management issues relating to capital flows. Thirdly, we review the interactions of capital flows with the financial sector. Section II presents the basic capital inflows and outflows data and, discusses the main characteristics of these flows and provides a decomposition of the capital account. In section III the setting for capital account liberalization and the initial conditions with regard to the key macroeconomic aggregates are presented, followed by some econometric evidence on the determinants of capital flows. Section IV discusses the effects of capital flows on consumption and investment. Section V turns to macroeconomic management issues, including the exchange rate, monetary and fiscal policies, that relate to capital flows and point out macroeconomic vulnerabilities. Section VI turns to the financial sector and analyzes banking intermediation of capital flows and issues surrounding the process. Section VII
# Table (E12) Impacts of the Program on Individual Child Outcomes

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Strengths and Diffculties Questionnaire (Z-Score)|-0.16|(0.18)|[1.00]|0.00|(1.00)|905|
|Child Happiness (Scale 1-7)|-0.16|(0.31)|[1.00]|5.73|(1.54)|951|
|Child Alertness (Scale 1-7)|0.04|(0.25)|[1.00]|5.72|(1.64)|951|
|Child Sleep Duration (Hours)|-0.13|(0.24)|[1.00]|9.79|(1.49)|939|
|Child Bed Time (Military)|2.02|(1.70)|[1.00]|17.26|(8.65)|948|
|Child Wake Up Time (Military)|-0.06|(0.33)|[1.00]|8.20|(1.61)|943|

**_Notes_** :

- The table shows the regression results on pre-specified child outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Strengths and Difficulties Questionnaire (SDQ) is a screening questionnaire administered to a randomly selected child aged
To identify protected areas, we used the World Database of Protected Areas (UNEP-WCMC and <u>IUCN 2019). Our definition of protected areas encompasses all six categories of the International</u> Union for Conservation of Nature (IUCN 2016).<sup>17</sup> We acknowledge that not all activities financed with bank loans are or could be prohibited in protected areas. For instance, protected areas cataloged under IUCN VI (protected areas with sustainable use of natural resources) are designated for low-level non-industrial use of natural resources compatible with nature conservation. Nonetheless, we decided to account for these areas as protected. Because of their conserved ecosystems and habitats, they could potentially “transition” to higher stringency levels of protection in the future.

To detect areas that could become protected in the coming years, we leverage the Systematic Conservation Planning (SPC), an initiative of the Brazilian government launched in the early 2000s and led by the Ministry of Environment.<sup>18</sup> The SPC collects and processes spatial information about species abundance, biodiversity hotspots, costs, and conservation opportunities. This information is complemented and validated through a series of participatory workshops with specialists and representatives from different sectors. This process occurs every five years and results in mapping areas of priority actions for biodiversity conservation in all major biomes and coastal and marine zones.<sup>19</sup> Actions are classified as extremely high, very high, and high.

We use spatial methods to determine priority areas that do not overlap with existing protected areas and their location in the Brazilian political-administrative divisions. Our methodology to undertake this identification closely follows Fonseca and Venticinque (2018). We first estimate biodiversity conservation gaps in Brazil as the most stringent priority areas currently not protected. In other words, these are areas with extremely high priority actions for biodiversity conservation that exclude existing protected areas. In addition, priority areas tagged as very high and high that do not overlap with protected areas are also identified. The
infrastructure, literacy, distance to nodal cities, access to finance, population, and then several measures of distortions, but again, disaggregated by age.<sup>12</sup> Panel B shows that for local infrastructure, as with the overall results, only the young cohort of plants shows significant results, and these are mixed. Overall district output, and firm size measured by employment are higher for young plants with better local infrastructure, but size measured by output, TFP and labor productivity are actually higher in places with worse, a result shared with older firms. The possibility that poor local infrastructure and other distortions may serve to protect local markets, while access to cheaper inputs may lead to higher markups and hence higher measured productivity is discussed below.

The most striking result from the disaggregation by district-level education (panel C) is that in high literacy areas, mature firms now show significant responses for output and plant size, and older firms also have a significant response in output comparable to that of young firms as well as rising TFP. That is, the null or declining impact of proximity to the highway network on older firms is being driven by those plants in low literacy areas.

This proxy might literally be measuring the availability of skilled workers. Alternatively, if literacy is proxying for capabilities/managerial practices, plants closer to the technological frontier are more likely to innovate out of increased competition while laggards will be less able to compete and will decline a la Aghion et al. Somewhat puzzling is that young plants appear to do slightly better across all indicators for _lower_ skill districts although as the true plant-level panel estimates below will show, this does not imply better performance by lower capability firms – the reverse is true. It may be that entering firms have better managerial capital but seek low skilled labor. This is the only cut of the data that reveals statistically significant responses for mature and older firms (although with lower point estimates) and hence we focus on the young plants going forward.

Several variables
enrollment sites.

Recruitment for the PRO-Jeunes program was carried out in two stages: (i) interested youth first registered, expressing their interest in the program; (ii) after screening all the applications, the project team invited eligible applicants to come to an “enrollment meeting.” According to data from the first two cohorts from the program, a significant share of eligible applicants who had expressed their interest in stage 1 did not attend the subsequent enrollment meeting in stage 2. The intervention evaluated in this study targeted the next cohort, and took place between the two stages, aiming to increase take-up from registration to enrollment.

Eligible applicants were randomly selected to receive an SMS reminding them that the program was free.<sup>1</sup> Youth were already informed that the program was free at pre-registration and the message served as a reminder. Randomization was stratified by gender and geographic location. The SMS message read as follows: “Come participate in an information session about PRO-Jeunes, a program offering free training, support for self-employment and employment opportunities.” In the first treatment, the SMS was randomly sent to the youth only. In the second, treatment, the SMS was sent to the youth and their listed contact person. At pre-registration, youth were asked to list a person to contact in case they could not be reached. It was specified that youth who did not live by themselves should provide the name of a person they lived with.

Our study uses administrative data collected in 2019 on a sample of 2,926 eligible applicants to the PRO-Jeunes program who had completed the pre-registration stage, during which they provided identification and contact information for their contact person, as described in Table 2. The data include variables such as date of birth, phone number, gender and geographic location for youth, and the relationship to the youth and phone number for contacts. Contact gender was inferred from the relationship variable. Listed re1A second type of SMS emphasizing the long-term career benefits of the program was also tested
Policy Research Working Paper 10173

# **Abstract**

Multiple price lists are a convenient tool to elicit willingness to pay in surveys and experiments, but choice patterns such as “multiple switching” and “never switching” indicate high error rates. Existing measurement approaches often do not provide accurate standard errors and cannot correct for bias due to framing and order effects. This paper proposes to combine a randomization approach with a random-effects latent utility model to detect bias and account for error. Data from a choice experiment in South Africa shows that significant order effects exist which, if uncorrected, would lead to distorted conclusions about subjects’ preferences. Templates are provided to create a multiple price list survey instrument in SurveyCTO and analyze the resulting data using the proposed methods.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at asautmann@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_wholesale price may go up following the elimination of DOTs from the market._

# **(4.2) The Role of DOTs in Alleviating Credit Limits of Wholesalers**

In this section, we analyze a model in which the absence of DOTs following the reform led not only to an increase in the cost of borrowing by wholesale dealers, but also imposition of quantitative credit limits by alternative financiers. For simplicity, we assume that wholesalers faced no credit rationing prior to the reform, owing to long-term relationships with DOTs. The results derived below hold if the DOT provided credit is not enough to eliminate the credit constraint for some wholesale traders, as long as the trade credit relives the credit constraints substantially compared to the alternative sources such as banks.

As noted earlier, evidence from a survey conducted by the authors in 2013 indicates that about 30 percent of transactions between the DOTs and the wholesalers are on credit (32 percent of quantity). The reform constrained the use of the information capital accumulated by the DOTs (and brokers) over more than six decades. Unlike the DOTs, refiners had little or no information about the credit-worthiness of a wholesaler (dealer) and were therefore reluctant to provide oil on credit.<sup>13</sup> This forced the dealers to finance their purchases from other sources, possibly resulting in binding credit limits which restricted their ability to purchase oil from refiners. The resulting disruption in oil supply to downstream retailers would then raise the price at which the wholesalers would be selling to retailers. In order to derive implications for pass-through rates, we now work out an extension of the preceding model.

Prior to the reform, we suppose the analysis of the earlier section applies, with the wholesale price determined according to (8). After the reform, wholesale dealers have to borrow at an interest rate of (1+ _ib_ ) and are subject to a credit limit of _B_ (aggregating across all the dealers). For simplicity and tractability, we do not model
Large countries show the greatest absolute sales losses due to power outages, whereas smaller countries lose a larger fraction of GDP. This is illustrated by Figure 6 which disaggregates the global results into absolute numbers and absolute numbers as a share of GDP and shows the 15 most affected countries according to both measures. Sales lost as percentage of GDP in Figure 6 are significantly lower than share of sales lost as reported by the average firm in Figure 5. The set of countries affected most severely is quite different between the two measures. This is caused by several factors. First, the share of sales lost by the average firm is ignorant of firm size. If small firms experience more losses but larger firms can better cope with outages, the share of sales lost by the average firm will be higher than the total sales lost as percentage of GDP. Second, sales and GDP are quite different measures, as sales are ignorant of the value added a firm provides and thus its contribution to GDP. Third, the Enterprise Surveys primarily focus on the manufacturing and services sectors and thus covers a fraction of GDP that varies between countries. Finally, the fraction of firms answering the question about their sales and thus the degree to which a countries total economic activity is captured can differ among sample countries.

Figure 6: Total (left panel) and relative (right panel, as % of GDP) sales lost due to power outages for sample countries most affected <!-- Start of picture text --> Indonesia Sudan<br>India Malawi<br>Brazil Burundi<br>Egypt Kyrgyz Republic<br>South Africa Mozambique<br>Turkey Egypt<br>Pakistan Indonesia<br>Sudan Uganda<br>China Ethiopia<br>Mexico Zimbabwe<br>Poland Yemen<br>Hungary Pakistan<br>Ethiopia South Africa<br>Vietnam Djibouti<br>Nigeria Lebanon<br>10 20 30 0 1 2 3 4 5<br>Total sales losses (in billions, 2018 USD
datasets do not include village-level identifiers and, thus, district is the smallest policy-relevant geographical boundary possible. Since decentralization, districts have also often split over time. In 1993, there were 290 districts; by 2014, there were 511 districts. To ensure equal comparisons across time, we harmonize district boundaries as they existed in 1993. We then match our constructed panel with preschool access at the consistent district boundaries circa 1993.

PODES is not available annually. We employ three strategies to account for the missing years. First, we simply restrict our constructed panel to PODES years. Second, we infer preschool data from in-between PODES years using the closest upper year available. For instance, year 1992 is sandwiched between PODES 1990 and 1993, so we infer preschool data from the 1993 round.<sup>10</sup> Third, we predict preschool density for the missing years using linear projection with the closest two data points available. For year 1992, we fit a linear projection using preschool density data in PODES 1990 and 1993. In the subsequent section on empirical strategy, we discuss the results from each strategy.

Table 1 provides summary statistics from our constructed panel. We have 227,579 femaleyear observations, with an average age of 31.5 and a 30 percent probability of having a preschoolaged child in any year. They have a 52 percent work participation rate and 51 percent live in urban areas. They are subjected to an average of 0.15-0.16 public and 4.45-4.60 private preschool density in their district of residence. There are 10,340 distinct females, who are surveyed on average in 3.54 rounds representing an average of 22 years of observations and 7.5 years coinciding with PODES years.<sup>11</sup> The average age of first marriage and first birth are 20.23 and 22.13, respectively. On average, each mother has 2.74 children and 7.75 years of education, or the equivalent of halfway
sup>_<u>li</u>_</sup> 1+<sup>1+</sup> _ν_<sup>_ν_</sup> , _co_<sup>_i_</sup> _l_<sup>=</sup><sup>_σ_Φ</sup> _c_<sup>_i_</sup> _−_ Φ<sup>_<u>li</u>_</sup> 1+<sup>1+</sup> _ν_<sup>_ν_</sup> .

The details of the solution method are presented in Appendix A.

# _4.2. Baseline Calibration and the Steady State_

The value of the discount factor is set for quarterly data: _β_ = 0 _._ 98. The share of tradable consumption is a third of total consumption: _γ_ =<sup><u>1</u></sup> 3<sup>.FollowingChugh(2006),weset</sup><sup>_χ_equal0</sup><sup>_._05,</sup> the elasticities of substitution _ωF_ = 10. We set the endogenous discount factor coefficient very small _μ_ = 0 _._ 001, so that it does not have a significant impact on the model’s dynamics. We also set the persistence of money supply to 0.9, and the persistence of endowment to 0.8. We set _κ_ equal to 0.75, implying that the average period between price adjustments is four quarters, as in Bernanke et al. (1999). The correlations of P, C and O’s endowments are consistent with the data.<sup>11</sup> The steady state of the three-economy system is summarized in Table 2 and they seem quite reasonable. Overall, the share of the non-tradable good value is about two-thirds of total consumption in all the economies. Country P’s net foreign
Wbile WHO's definition of health as "a state of complete physical, mental, and social wellbeing, and not merely the absence of disease or infirmity (WHO, 1988)" is attractive, it is subjective and hard to assess.<sup>8</sup> Moreover, since death occurs rarely, and only once for each person, it is difficult to study at the household level. So while we might be interested in the totality of health over an individual's life course, empirical studies tend to focus on infant (or child) mortality or life expectancy as proxies for health status and rely on aggregate (district, province and country) comparisons.

_Country level: Aggregate spending._ Cross national studies have come to a fair consensus on two points. First, socio-economic characteristics explain nearly all of the variation in mortality rates across countries. A recent cross-national econometric study of child (under-5) mortality shows that average GDP per capita, a measure of the distribution of income, the level of female education, a dummy variable for countries predominantly Muslim, an index of ethnolinguistic diversity, and a set of five dummy variables for regions explain virtually all the variation in child mortality (Filmer and Pritchett, 1997). GDP per capita alone "explains" 80 percent of the variation in mortality and adding the other variables raises this to 95 percent. Preston (1980), in an influential paper based on data from between 1940 and the 1970 emphasized the low explanatory power of socioeconomic variables. However, more recent data and results are unanimous about the high explanatory power of socioeconomic basics like average income and female education, including Preston (1986) using 'The huge effort to create measures of Disability Adjusted Life Years (DALYs) has led to some additional information on morbidity (Murray, 1994). However, the correlation between DALYs lost and life expectancy or infant mortality is 0.93 across the eight regions for which DALYS have been calculated (World Bank, 1993).

10
survey data with GPS coordinates for most surveys and are available for several rounds per country. The DHS is a repeated cross-section of enumeration areas (EA), with approximately 20–30 households enumerated per EA. Five rounds of survey data are covered in Nigeria (1990, 2003, 2008, 2013, 2018), four in Cameroon (1991, 2004, 2011, 2018), and one in Chad (2014). Niger did not conduct DHSs for the period of interest.

We use DHS data for which we have access to the occupation of the individuals as well as a proxy for their location. In order to construct the shares of employment per sector, we use respondents’ answers to questions about their current occupation. We first compute the shares of nonworking individuals and then group all working individuals into three sectors: agriculture, manufacturing, and services. We aggregate individual responses within each EA and then generate an unbalanced panel of districts that had at least one EA during a survey round. The DHS–provided GPS coordinates for EA locations are not perfectly reliable because of the common random displacement applied to GPS coordinates for anonymity.<sup>9</sup> We aggregate EAs per geographic location.

**Use of infrastructure** In addition of access to infrastructure, we consider how uses of roads, electricity and ICT infrastructure impact the structure of employment. We include variables from the DHS surveys that cover use of electricity as reported by households, ownership of cars and motorcycles, ownership of land phone and mobile phone, and use of internet. We aggregate the answers at the subnational level of interest as percentage of households that have access to electricity, own a car, a motorcycle, a land phone, a mobile phone, or use internet. The analysis of use of infrastructure complements the analysis of infrastructure investments. Not all variables are available for the whole period so we restrict our analysis to the period 2008-2018.

**District characteristics** We use additional data to control for district
Irrespective of the growth, poverty projections show sizeable differences depending on the baseline year used: 2003, 2007 and 2009. In particular, estimates based on the latest HIES 2009 deliver consistently higher levels of poverty projections. This finding is not surprising however, as none of the projection approaches is able to match actual pre-conflict poverty dynamics (Table 10). _Accordingly, to make best use of pre-conflict data, preferred poverty projections should be based on HIES 2009 baseline poverty estimates_ .

_Table 10: Comparison between baseline poverty estimates and projections in 2009_

||**Poverty**<br>**line**<sup>**32**</sup>|**Poverty estimates**<br>**based on grouped data**<br>**from HIES 2009**|**Distribution neutral**<br>**poverty projections**<br>**baseline 2003**|**Distribution neutral**<br>**poverty projections**<br>**baseline 2007**|
|---|---|---|---|---|
|**Constant pc GDP**|$2.15|2.4%|0.9%|2.7%|
|**Current pc GDP & CPI**|$2.15|2.4%|0.7%|2.8%|
|**Constant pc GDP**|$3.65|16.0%|10.2%|16.1%|
|**Current pc GDP & CPI**|$3.65|16.0%|9.5%|16.3%|

_Source_ : World Bank staff calculations, based on WDI and CBS data The analysis further reveals the substantial sensitivity of results to the choice of the NA aggregate growth to be used in the