rafmacalaba/data-use-mentions-extended huggingface dataset:
regression model to estimate intergenerational correlation between parents and children can be written as: where<sup>is the parental year of schooling in family i, andis the intergenerational regression</sup> coefficient. Because individual component in equation (1) is orthogonal to the family component, one can express the family component as: where denotes family factors that are orthogonal to parental education. It follows from equation (5) that: where is the intergenerational correlation in education. The above equation shows clearly that sibling correlation is a broader measure of the impact of family background than the squared intergenerational correlation. Also, the intergenerational correlation parameter ( ) is different from intergenerational regression coefficient ( ).

# **Estimating Equations**

To estimate the sibling correlations, we extend the regression model in equation (1) and specify the following mixed effects model: where is a vector of control variables. To estimate the intergenerational correlation in education, we augment equation (4) to estimate the following regression specification where the education variables of both generations are standardized: Equations (6) and (7) can be estimated as soon as vector is specified. Following Bjorklund et al. (2010) and Mazumder (2008, 2011), we take a sequential approach in introducing variables to vector. All regressions in this paper include age and/or gender dummy, the latter is added whenever relevant. In addition, we introduce two sets of explanatory variables sequentially.<sup>19</sup> The first set

> 19 The NFHS 2006 dataset has more detailed information about some of the family background variables such as mother‘s age at first marriage, mother‘s health, domestic violence faced by mothers as well as birth order of

9
differences in all the pre-treatment administrative variables are jointly insignificant (the lowest p- value is 0.22).

However, the treatment groups perform worse than the control group in two key dimensions, namely dropout (36% for the pooled treatment vs. 34%), and school size (268 vs. 296, respectively), suggesting that the treatment schools are relatively more vulnerable than the control schools. In particular, the 2-percentage points gap in dropout rates is an issue because it creates a significant downward bias in the impact of the program (particularly taking into account that we are analyzing a very low-cost intervention).

To correct for this bias, as will be discussed in the next section, we will include school-fixed effects in our estimations (exploiting the availability of repeated cross-sections from the administrative data).

Table 2. Baseline average characteristics of the students in control and treatment groups.

|_Variable_|_Control_|_ENTRE_|_ENTRE 1_|_ENTRE 2_|_ENTRE 3_|
|---|---|---|---|---|---|
|Age|1291|1294|1292|1295*|1294|
|1 = Male|.<br>0.50|.<br>0.51|.<br>0.51|.<br>0.52|.<br>0.51|
|1 = Repeater|0.01|0.01|0.01|0.01|0.01|
|Grade in math (previous year)|7.25|7.25|7.25|7.25|7.25|
|Grade in language 1 (previous year)|7.42|7.41|7.39|7.44|7.41|
|<br>Grade in language 2 (previous year)|7.45|7.43|7.42|7.45|
3, 6, and 12 months after each LPA was signed, while security and social cohesion indicators were measured at the closest point in time before each LPA was signed, as well as approximately 6 months after each LPA was signed (due to the lower frequency of data collection for these indicators).

> 68 The study relied on IOM DTM’s Master Lists from Round 21 (May 2015) to Round 121 (May 2021), available at: http://iraqdtm.iom.int/. Note that the DTM database defines ‘returnee’ as an Iraq who became displaced after January 2014, and subsequently returned to their location of origin.

69 The study relied on ILA Round 1 (July – October 2016), Round 3 (March – May 2018) and Round 4 (May – June 2019), as well as the Return Index Round 7 (November – December 2019) to Round 12 (March – April 2021), all available at: http://iraqdtm.iom.int/ Note that the indicators included in the ILA dataset vary with each yearly assessment. For this reason, ILA 2 was excluded from this study because the indicators included in ILA 2 were not comparable to ILA 1 or ILA 3, and no suitable proxy indicators could be identified in the dataset. Note also that the dates of each round refer to the period in which data was collected.

> 70 The _Master Lists_ contain data on IDP and returnee movements (from the onset of the ISIL crisis in 2014) and are updated every two months. The _Integrated Location Assessment_ and the _Return Index_ each provide location-level data on demographic composition, state of infrastructure, services, security, social cohesion, utilizing different but complementary indicators. While these datasets offer valuable insights into population movements and return conditions, it is important to keep in mind that DTM data aims to provide general trends on population figures, rather than precise figures. This methodology introduces some limitations to the dataset, and therefore to this
**Table 2: Imports in Select African Countries, by Product Category, 2011** _<u>% of all imports</u>_

|_Product category_|_Total_|_Kenya_|_Nigeria_|_South Africa_|_Uganda_|_Zambia_|
|---|---|---|---|---|---|---|
|_Chemicals and allied industries_|14|18|15|11|12|11|
|Foodstuffs|9|10|15|5|7|7|
|_Machinery and electrical_|27|25|32|30|30|18|
|Mineral products and metals|9|12|17|9|4|5|
|Miscellaneous|4|4|0|9|5|4|
|Plastics and rubbers|6|6|3|9|7|2|
|Service|2|4|0|2|1|0|
|Stone and glass|7|2|7|4|10|16|
|Textiles|5|4|0|16|4|2|
|_Transportation_|13|6|10|5|15|30|
|Wood and woodenproducts|4|8|2|0|5|5|

_Source:_ Authors based on firm surveys.

This might explain a certain selection bias in favor of equipment, but also a higher level of professionalism (in South Africa compared to the other four countries) and therefore might depict the situation as better than it is in reality. Moreover, the category of service products is small overall (two percent) and nonexistent in Nigeria and Zambia.

Average dwell time varies significantly across categories and countries (Table 3). For example, stone and glass products remain in port terminals for about 12 days. They are cleared more rapidly in South Africa (4.5 days) than in Zambia (14 days), which may
23

|Country.<br>L<br>economy,or<br>territory|ostaubil<br>P<br>Date|shed<br>censu<br>opulatio<br>(1000)*|s<br>n Source for population<br>(mid-1985)|Source<br>for fertility<br>(1985-90,<br>unlessindicated)|Source for mortality<br>(1985-90, unless indicated)|
|---|---|---|---|---|---|
|Slo Tomeand|Aug 81|97 F|UNPVSR1/88 (official<br>est.)|1980-85, based onUUPVSR|1980-85,<br>based onUNPVSR|
|Principe||||1984Special<br>Supplesent;|1984 Special Supplement;|
|||||1985-90,<br>Bank projection|1985-90,<br>Bank projection|
|Saudi Arabia|Sep 74|<br>7013 F|U.N. 1988 revision<br>(prelim.)|U.N. 1988i<br>revision<br>(prelim.)|Based on U.N. 1984<br>assessment|
|Senegal|Apr 76|<br>5069 F|Official<br>est., Situation<br>Ecornomiqie<br>du Senegal,<br>1985|1986 DHS|Estimated<br>from country life<br>table prepared<br>by D. Ewbank,<br>University<br>of Pemsylvania|
|ychelles|Aug 77|62 F|UNPVSR<br>4/87|1980-8". based on UNPVSR<br>1984 Special Supplement;<br>1985-90, Bank projection|Based on UNPVSR<br>1984<br>Speciat Supplement|
|erra Leone|Dec 85|3516 F|Bankprojection<br>from census,<
Salvadoran migrants reside are valid instruments (i.e., we cannot reject exogeneity of the other instrument conditional on one instrument being exogenous).

We find that our main results are robust to controlling for the potential endogeneity of remittances. Namely, while remittances have a positive effect on the likelihood that a household has a deposit account, neither the fact that a household receives remittances nor the actual per capita amount received has a statistically robust impact on the likelihood of applying for and receiving a loan.

# **V. Conclusion**

As the importance of remittances for developing countries has grown, a sizeable literature has flourished examining the impact of remittances on various aspects of countries’ development. An issue which has received little attention is the effect of remittances on the financial system. This issue is important given the evidence that financial development matters for growth and poverty alleviation and financial inclusion has many beneficial effects for households.

This study examined the impact of remittances on financial inclusion using data from a four-wave rural household survey for El Salvador. In particular, we analyzed the impact of remittances on the likelihood that households use financial services such as deposit accounts and loans. Overall, we find that remittances have a positive impact on financial inclusion by promoting the use of deposit accounts. These results hold controlling for unobserved household characteristics and using instrumental variables regressions to correct for the potential endogeneity of remittances. On the other hand, remittances do not have a significant effect on credit from formal financing institutions. Hence, we conjecture that by relaxing credit constraints, remittances might reduce the need for external financing by financial institutions, while at the same time increasing the demand for savings instruments.

There are a number of potential avenues for future research. First, it would be interesting to analyze the extent to which remittance recipients that have accounts, actively use these accounts to save and manage their daily transactions. Second, it would be important to go deeper into the reasons why those that receive remittances do not seem to have a higher demand for
prevailing official block tariff schedule.<sup>23</sup> The solid line in Figure 4 shows monthly predicted margins (and confidence intervals) from a regression of _actual_ log monthly consumption on month since connection dummies, which also controls for preferences _θ_<sup>ˆ</sup> _i_ and for calendar month _×_ year fixed effects to absorb aggregate shocks.<sup>24</sup> Water usage data cover the first 40 months of the utility’s operation in the commune prior to our experiment and includes all (and only) the 1462 households in the experimental sample. The dashed curve in Figure 4 is derived from exactly the same regression procedure but with the dependent variable now being model-generated log monthly consumption; i.e., an unbalanced panel of household specific time-series beginning at initial connection. With the exception of the first and last few months of connection, our model’s out-of-sample fit is quite good. Most encouraging is that model and data seem to agree on how long it takes, on average, for consumption to reach its steady state level starting from the initial month of connection; a static model of demand, by contrast, would trivially, but counter-factually, predict that steady state consumption is achieved at _t_ = 1.

# **5 Optimal pricing of a new utility service**

We are now ready to investigate the optimal pricing of a new utility service. Returning to the model of Section 3, the _ex-ante_ expected indirect utility, i.e., prior to consumption of the service, may be defined as _Ex-post_ utility, i.e., once consumption has reached its long-run steady state level, is similarly given by In defining these utilities, we abstract from transition dynamics. Thus, equation (15) assumes that _C_<sup> ̄</sup> 0 = 0 and remains so over time, whereas equation (16) assumes that _C_<sup> ̄</sup> _t−_ 1 reaches _C_
only area where reserved pradhans are put on par with non-reserved ones relates to the ability to solve local disputes.

Consistently for males and females, descriptive data suggest that reservation affects perceived quality of service delivery either negatively (for water, schools, health, and sanitation although, with some 53%, a majority of respondents saw things improve) or not at all, i.e. it does not lead to improvement (for roads where about 37% perceived an improvement and credit where 29% judged their situation better than in the previous period). Problems related to officials‟ absence from their duties are a notable exception; with some 25% in total who perceived things to have improved, the share of those who viewed improvements was significantly higher in reserved than in unreserved villages.

Gender differences are also striking regarding respondents‟ perceived ability to hold officials to account: While some 50% of men find it easy to hold officials to account, have them take care of local problems,

> 14 To simplify presentation, we use only currently reserved villages for the first set of data and villages that have been ever (or never) reserved for the rest. More disaggregated data are not substantively different and available on request. To facilitate interpretation, state level effects are controlled for throughout.

13
in Barro (1990) and Barro and Sala-i-Martin (1992). The empirical section of the paper uses data from Latin American countries to test some of the implications of the model and to provide estimates of the relative size of the informal sector throughout these countries.

# **THE RATIONALITY** OF **BEING INFORMAL**

Economic units choose to be partially or completely informal by weighing the costs and benefits a legal status entails and considering their particular institutional and resource constraints. In this sense, the choice to be informal is a rational one, a fact which does not imply that some firms are not forced by their constraints to be either formal or informal.

The costs and benefits of legality are now analyzed in detail. The presentation adopts de Soto's analytical framework as well as some of his reported evidence. De Soto studied the informal sector in Peru. Other researchers have applied his methodology to a variety of different countries. Chickering and Salahdine (1991) present evidence from selected underdeveloped Asian countries. Tokman (1992) offers evidence from Latin America and the Caribbean. These empirical findings also contribute to the following presentation.

# **The Costs of Formality**

The costs of formality can be divided into costs to access the formal sector and costs to remain in the formal sector.

**Costs to access the formal sector.** De Soto and his research group conducted various experiments to quantify the access costs. In one of them, they set up a small clothing factory in the outskirts of Lima and tried to register it legally. Four university

-3-
,755|<br>7.21|715|10,817|<br>6.61|
|Metal products|23,949|741,602|<br>3.23|2,129|52,617|<br>4.05|
|Electrical Machinery|9,557|243,997|<br>3.92|839|27,369|<br>3.06|
|Machinery andTransport|||||||
|Equipment|19,423|1,086,945|1.79|1,434|79,087|<br>1.81|
|Other|2,494|225,228|1.11|228|15,099|<br>1.51|
|Total|157,488|4,897,816|<br>3.22|14,401|<br>328,343|4.39|

a) indirect taxes and subsidies are deducted (see table 2.3).

b) includes 3,831.7 million pesos (excl.indirect taxes and subsidies ) for petroleum refining, which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico.

- Note: The breakdown between food products and beverages for the US on a national accounts basis was assumed to be proportionately the same as on a US Census basis (1975 figures derived from Annual Survey of Manufactures).

- Source: Mexican value added in national currencies from Resumen General (see table 2.3 which does not exclude bank costs). US value added in national currencies from National Income and Products Accounts of the United States: 1929-76 Statistical Tables (1981c) after adjustment for inventories indirect taxes and subsidies and net intrest (see table 2.6). PPPs from table 3.14.
0.012)|(0.014)|(0.027)|
|Risk taking (Z score)|<br>0.042***|<br>0.039*|<br>0.044***|<br>0.037**|<br>0.040***|<br>0.065**|<br>0.028**|<br>0.037*|<br>0.025*|<br>0.020|<br>0.029*|<br>0.004|
||(0.013)|(0.020)|(0.013)|(0.015)|(0.015)|(0.027)|(0.012)|(0.019)|(0.014)|(0.015)|(0.017)|(0.022)|
|Observations|3911|3911|3911|3911|3911|3911|4514|4514|4514|4514|4514|4514|
|R-squared|0.229||||||0.211||||||

Notes: These results derive from the full model with country fixed effects and an additional control for metropolitan area of residence. Statistical significance is denoted as *** p<0.01, ** p<0.05, * p<0.1 ; robust standard errors are in parenthesis. The estimations do not include Serbia which does not have data on risk taking or hostile attribution bias. Data sources: STEP data for 8 countries, 2012-13 40
are typically large, roughly 50 meters long on average, with engine power averaging 1,036 kW. Distant water fishing vessels fish on average 221 hours per month (297 for those fishing positive hours). The range of activity across vessels and months is large, with monthly fishing hours ranging from 1 to 751. 12This matching criterion is based on MOA regulations. First, the MOA mandates the use of the MMSI module in the AIS equipment of Chinese distant water fishing vessels (Ministry of Agriculture and Rural Affairs, 2021b). Second, the MOA requires vessels to report their call sign in their distant water fishing permits (Bureau of Fisheries, 2020), while reporting IMO is not mandatory (Ministry of Agriculture and Rural Affairs, 2020).

> 13Implicit in this procedure is that if a vessel-year does not appear in the GFW data, we do not add rows for that vessel-year to our panel data. The issue of when to add zeros only affects our estimates in Section 5.2.1, when the dependent variable is an indicator for positive fishing hours.

> 14Pacific saury vessels have only one ceiling of subsidized engine power: 2,200 kilowatts for vessels with gross tonnage above 1,400 (Table B1). All Pacific saury vessels have gross tonnage above 1,400, and there is no information on what the ceiling of subsidized engine power would be for Pacific saury vessels with gross tonnage below 1,400.

11
<mark>could have been confidently anticipated (Abadie, 2021). Because when applying the SCM there is no restriction on time variation in the effect of the treatment, de facto treating pre-treatment similar to post-treatment periods does not bias the estimators but allows one to see the difference of the effect of anticipation (if and when it sets in) and the effects of the actual treatment. We apply the backdated matching approach and confirm at least some anticipation seems to be at play in some instances, though even in these cases it is the actual occurrence of default that drives the lion share of the effects measured.</mark><sup>19</sup>

# **IV.** **<mark>Findings</mark>**

## **<mark>Output</mark>**

<mark>Based on a sample of 131 defaults since 1900, we find that, on average, sovereign default causes an economy to fall behind by a cumulative 8.4 percent gap relative to the synthetic control within the first three years of the default. This is mainly driven by growth-rates in the first two years of defaults being 3.6 and 2.4 percentage points below the counterfactual. And while GDP growth slows down relative to the control prior to the default – which likely includes effects from default-anticipation on investor- and business confidence, inter alia – the marked decline in output only begins with the default year. On average, GDP per capita growth returns in the third year – though it remains below the trend observed prior to default, on which the synthetic control continues forward. After four years, sovereigns reach their pre-default output level. However, over the decade following default the average annual growth rate is 1.7% below that of the control. So, there appears to remain significant scarring that precludes reconvergence to the prior trend. Note that despite the information-based weight, these results are not driven primarily by the weighting. As is shown
education and literacy status of the household head does not hold consistent results across country. In Ethiopia, literacy of the household head reduces measurement bias (in acreage terms), but years of education has the opposite effect. In fact, the education of the household head significantly increases the relative bias in Ethiopia (but is not statistically significant in Nigeria or Tanzania). One can speculate that, controlling for literacy, education may be associated with increasing opportunity cost of time or decreasing involvement in agriculture, which could make the respondent less attentive or knowledgeable regarding the measurement or reporting of plot area.

Finally, one key decision point when collecting self-reported area data is whether to allow respondents to use non-standard units, or to force them (or enumerators) to convert responses from traditional to standard units at the moment of the interview. In Table 11, we compare deviations between self-reported and GPS data separately splitting the sample between observations where respondents used traditional and standard units in the Ethiopia and Nigeria sample. When land area is collected using non-standard units, as opposed to forcing respondents or enumerators to perform a conversion to standard units at the time of the interview, data from self-report appears to approximate the preferred GPS measures much better. This finding supports the idea that it is best to allow non-standard units to be used at interview time, while organizing complementary collection of adequate conversion factors to translate all the data in a common metric at the data processing stage.

# **VI. Conclusions**

Several important findings emerge forcefully from this analysis, which translate into clear implications for future survey design and implementation. The first result is that our experimental data confirm what we already knew about the presence of large, systematic measurement error in farmers’ self-reported estimates of land area, and on its direction, correlates and determinants (which include land area itself, introducing potentially large biases at the data analysis stage). While not a novel finding, this is a useful reminder of the urgency to find alternative measures that are both accurate and
, fine-scale traffic data from Beijing, Yang et al. (forthcoming) analyze the relationship between traffic density and speed. They estimate the optimal time-varying and location-specific congestion charges to be between CNY 0.05 and CNY 0.39 per kilometer; they conclude that the 15 Several dozen high-occupancy toll (HOT) lanes with variable or dynamic tolls are operating or planned in the United States. For example, the express lanes on Interstate 66 near Washington, DC, charge singleoccupancy vehicles a fee that fluctuates according to traffic conditions.

16 Singapore’s electronic road pricing system features about 80 entry points that record passing vehicles around the city. The charges are not based on distance traveled, and they vary only infrequently. The system is being upgraded to a GPS-based system with the ability to incorporate time-varying and location-specific charges.

> 17 Drivers may respond to the charges by driving around the priced area. Such behaviors may lead to more traffic and emissions outside the area, as Gibson & Carnovale (2015) suggest.

> 18 Daniel & Bekka (2000), Bigazzi & Figliozzi (2013), and Fu & Gu (2017) study highway tolls and their environmental impacts. Using data from 98 Chinese cities, and both RD and DID methods, Fu & Gu (2017) show that eliminating highway tolls increases air pollution by 20 percent and decreases visibility by 1 kilometer.

19
14|
||SD|0.50|0.45|0.52|0.51|

<u>Panel 3</u>

|||Uk<br>|raine<br>|Vie<br>|tnam<br>|
|---|---|---|---|---|---|
|||Men|Women|Men|Women|
|Openness|Mean|3.02|3.10|2.90|2.70|
||SD|0.50|0.59|0.60|0.62|
|Conscientiousness|Mean|2.93|3.09|2.86|2.79|
||SD|0.45|0.54|0.50|0.49|
|Extraversion|Mean|2.49|2.76|2.69|2.74|
||SD|0.53|0.64|0.48|0.53|
|Agreeableness|Mean|2.81|2.98|3.02|3.01|
||SD|0.54|0.64|0.52|0.52|
|Emotional|Mean|2.77|2.40|3.07|2.74|
|stability|SD|0.53|0.71|0.52|0.54|
|Grit|Mean|2.79|2.78|2.80|2.75|
||SD|0.56|0.67|0.54|0.49|
|Decision-making|Mean|3.02|3.20|2.91|2.87|
||SD|0.48|0.61|0.61|0.60|

_Note_ . Noncognitive skills are self-reported