rafmacalaba/data-use-mentions-extended huggingface dataset:
For literacy and numeracy outcomes, we aggregated all levels of literacy and numeracy skills and constructed a normalized score measure. This measure consists of normalizing literacy and numeracy scores using the mean and standard deviation for the control in _baseline_ . This approach allows comparing our results with other studies targeting primary school age children with edtech strategies such as Banerjee et al. (2007), Muralidharan et al. (2019), among others. Our main analysis also reports the proportion of children providing ‘no correct’ answer in letter recognition, a measure of basic literacy foundations commonly used in the education literature.

To understand what competency levels the interventions may have improved, we assessed the target child and sibling using _Early Grade Reading Assessment_ (EGRA, literacy) and Early Grade Mathematics Assessment (EGMA, numeracy) in Hausa. We used each level of competency to construct an aggregate of learning measures. All levels are reported in Table 1.<sup>10</sup>

# **5.1.2 Mechanism measures**

As described in Section 4, the videos aimed to help parents visualize a future that many of them had not considered before for their children. This involved increasing the aspirations of treated parents by expanding the mental set of choices they had for their children if they were educated. To measure how much our interventions changed the way parents perceived the future of their children, we collected data on parental aspirations and expectations for the target child. For aspirations, we asked parents about what they desired their children to become or to do. For instance, we use questions such as _What is the highest level of education you would like your child to complete?_ and _At what age would you like your child to get married?_ . To measure expectations, we also asked parents about the we use chances that certain outcomes would happen in the child’s future. For example, questions such as _how likely do you think it is that your child will finish secondary school?_ and _how
Mexico's output in national currency terms vis-&-vis Brazil. Substantive Results for Output and Productivity. Brazil/USA and Mexico/USA The most striking feature of our quantitative results (table 8.3) is the relatively high levels of productivity (output per person engaged) in the manufacturing sectors of the two Latin American countries. Though well below the US level, they are not far from those which comparable studies have revealed for Western European countries for some earlier years (for example Paige and Bombach for 1950, and Smith, Hitchens and Davies for 1967/8, see table 7.3). A few additional remarks should be added to this surprising conclusion. Firstly, the Latin American standing in terms of output per man hour, which could not be measured accurately, is probably lower than the productivity ratio in terms of output per person engaged, because working hours appear to be higher than in the USA. Secondly, in comparison with the USA there is probably a greater amount of informal manufacturing activity outside the scope of the census in Latin America where productivity is lower. Thirdly, Latin American performance per head of population is much lower than their productivity standing, because manufacturing employment is relatively much smaller than it is in the USA.

# Substantive Results for Output and Productivity Mexico/Brazil

Table 8.4 is a binary comparison of Mexico/Brazil which is statistically somewhat better grounded than table 8.3, because of the availability of census data on all inputs and a bigger industry sample for the two Latin American countries than for the USA. Table 8.4 shows that Brazil had a better performance than Mexico both in terms of labour productivity and output per head of population. From the results shown in table 8.3 one can also make the inferential comparisons for Mexico/Brazil shown on the right hand side of table 8.4.
|6.39|6.16|1248|7.51|7.37|1462|8.43|8.26|
|Students/Staff|1839|3.39|3.21|1487|3.91|3.78|1248|4.64|4.50|1462|5.39|5.24|
|Class Size|1839|7.04|7.00|1487|11.43|11.00|1248|15.49|15.50|1462|21.88|21.00|
|Share Ukrainian|1839|0.91|1.00|1487|0.90|1.00|1248|0.87|1.00|1462|0.89|1.00|
|Share of Males|1839|43.72|42.86|1487|43.46|44.44|1248|43.21|42.86|1462|42.08|42.31|
|Unemployment Rate in Rayon|1839|3.24|2.90|1487|3.03|2.70|1248|2.80|2.50|1462|2.66|2.30|
|Average Wage in Rayon|1839|1405.11|1336.00|1487|1404.92|1328.00|1248|1417.27|1338.00|1462|1430.95|1338.63|

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As an additional way of seeing whether these impacts are concentrated in a few firms or more widespread, we estimate quantile treatment effects. Figure 1 plots the impacts and compares them to the ITT impact shown in Table 2. Quantile treatment effects for the level of monthly sales are well below the ITT for all but the top decile, showing that the large magnitude of the average improvement is indeed driven by the top of the distribution. However, we would expect training to be result in a constant percentage increase in sales, than the same level increase in sales regardless of initial firm size. This is the case, with the quantile impacts on log sales relatively constant across all quantiles and similar to the estimated ITT average impact. Likewise, we see the quantile treatment impacts on business practices are positive and significant and of similar magnitude across most of the distribution.

# **3.4 Do these impacts last?**

Columns 4-7 of Table 2 show that none of these impacts persist at 6 months. The estimated impact on business practices has fallen to 0.8 percentage points, which is not statistically different from zero, and is statistically different from the 2-month impact. The estimated impacts on sales and profits are all negative in sign, and not statistically different from zero, and likewise are statistically different from the 2-month impacts. This difference is not a result of changing sample composition: Appendix Table B3 shows the results are similar if we restrict analysis to the balanced panel of firms.

We investigated treatment effect heterogeneity to examine whether training had sustained impacts for some subgroup of the sample, even if the overall impact disappeared. We explored two approaches to examining heterogeneity in Appendix D. The first is to examine treatment interactions with firm and owner characteristics. We find the initial impacts of training appear to be higher for those owners with more personal initiative, but even this subgroup does not have lasting impacts. Second, we use the generic machine learning approach of Chernozhukov et al. (2020) to test whether there
The second section of the paper considers the potential for biases from limited-dependent variables, missing data and heteroscedasticity, and their implications for the design of Monte Carlo experiments. The third section presents estimates of the gravity model from a range of potential estimators, partly to see whether there are important differences in results, and partly to provide estimates of the parameters needed for Monte Carlo analyses to identify the vulnerability of different estimators to bias. The Monte Carlo framework is developed in the fourth section and its results are discussed in the fifth section. A short sixth section provides some suggestions for applied research, prior to the final conclusions.

# **II. Sources and Solutions for Estimation Bias**

Before designing Monte Carlo simulations that allow testing the suitability of alternative estimators, it is important to understand the sources of potential bias. SST (2006) identified two: (i) _limited-dependent variable bias_ associated with zero trade values, and (ii) the combination of _nonlinearity and heteroscedasticity_ in the model. An additional source of potential bias considered here arises from missing trade data treated as zeros.

_Limited-dependent variable bias_ results from winsorization of the distribution of a latent variable at a threshold value, with all values beyond the threshold mapped to this value (Tobin 1958). This results in errors from Ordinary Least Squares (OLS) estimators having non-zero means (Heckman 1979), violating the fundamental assumptions of OLS and leading to biased parameter estimates. The combination of _nonlinearity and heteroscedasticity_ may cause bias in nonlinear models because of correlations between explanatory variables and error terms. In limited-dependent models heteroscedasticity causes bias, even in linear models, as discussed below.

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Neyman allocation rule is derived from an optimization problem consisting in minimizing the variance of a target variable subject to a pre-determined fixed sample size. The full derivation of the modified Neyman allocation rule used for the allocation of PSUs in the HIES 2016/17 is presented in Annex 2.

> 17 The finite sample correction factor must be taken into account when the sample size corresponds to a large percentage of the size of the population of interest. This condition rarely holds in practice.

> 18 The performance of the sample is directly affected by the decision to fix _ex-ante_ the cluster size in each PSU to 20 households. This follows the decision of the BBS to follow field data collection protocols closely aligned with those used in the three previous rounds of the HIES collected in 2000, 2005, and 2010. This paper does not assess the performance of the sample under the selection of different cluster sizes.

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to implement the national PMT. Initial discussions between the government, development partners and humanitarian actors revealed a division in terms of concepts, language and philosophy. While the humanitarian programs use vulnerability, the SSN uses monetary poverty for eligibility assessment and the targeting models between the two differ. However, the technical work summarized in this note allowed initial work to begin and momentum and discussion to ensue. Genuine concerns over data sharing issues were able to be overcome through the sharing not of underlying data but of pseudo-PMT models which captured the commonalities of targeting approaches without running afoul of data privacy.

This section outlines the MoLSA eligibility criteria and compares it to the current humanitarian approach. It then proposes a PPMT tool to compare these two methods given the lack of common data set.

# 3.1 MoLSA Eligibility

According to the new 2014 Social Protection Law, only households below the poverty line are eligible for social assistance support. The poverty line in 2017 was IQD 110,881 per person per month (Sharma & WaiPoi, 2019). By this line, national poverty was 20 percent in 2017-18 (Sharma & Wai-Poi, 2019),<sup>6</sup> slightly above the 18.9 percent measured directly in 2012 (World Bank, 2014) but a decline from 22.5 percent in 2014, and 41.2 percent in affected governorates, which was estimated indirectly by simulation methods (Krishnan & Olivieri, 2016). Thus, the current 1.36 million SPN beneficiaries (IMF, 2021) are less than the estimated number of poor in Iraq, highlighting the legal (but not fiscal) space for expanding the program. A full revision of the poverty line and poverty rate will be done after the implementation of the third round of the Iraq Household Socioeconomic Survey (IHSES).

In practice, MoLSA does not observe potential household beneficiaries’ poverty status, as this would require administering an intensive, time-consuming and expensive household expenditure survey which is not practical to be conducted for all potentially eligible households
afraid of disagreeing with their husbands or other males in the households, and women’s agreement with statements about women’s empowerment.<sup>8</sup> We estimate model (1) using as

> 6We also run these models for the sample of men and the results show no statistically significant changes in job search using the internet, employment or unemployment.

> 7 We do not analyze impacts on wage levels due to the very low number of women reporting wages in the sample.

> 8The decision-making power index is the average of indicator variables where 1 means women have

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There are two main insights from an analysis of the haircut data. First, there are strong recurring features over time. Over the entire 200-year span, average haircuts and their variation are surprisingly similar. The level of creditor losses averaged between 40% and 50% – with no visible time trend or outlier spells. Every decade since 1815 featured a few sovereign restructurings. The only major exception is the period between WW2 and the 1970s. This is the Bretton Woods era with closed capital accounts and very limited private cross-border lending, so that barely any new defaults or restructurings on privately held sovereign debt occurred. Since the 1980s, we have seen a sharp increase in the number of sovereign restructurings, which also owes to the fact that the number of independent countries is much higher today. The standard deviation of haircuts is large throughout the sample, at about 30%. Some deals imply low haircuts of less than 20% while others reach 80% or more. Thus, the historical haircut statistics resemble those of more recent decades, despite the fundamental changes in institutions and markets since the 19th century.<sup>27</sup> Table 1: Sovereign haircuts with foreign private creditors (1815-2016)

||Cases|Mean|Median|SD|Min|Max|
|---|---|---|---|---|---|---|
|**Haircuts across time**(by default-restructuring event)|||||||
|Full sample (1815-2016)|313|44|39|30|-14|100|
|_Historical_sample (defaults pre-1970,<br>only bond restructurings occurred, no bank debt)|138|51|48|32|-14|100|
|_Modern_sample (defaults post-1970,<br>incl. 152 bank debt defaults and 23 bond defaults)|175|39|34|28|-10|97|
|... subsample of
the extensive margin of regional integration remains sparse (Kosar and Fajgelbaum, 2014; Emran and Shilpi, 2019). In the absence of data on the universe of trucking transactions, we developed an empirical methodology to define this margin by using observable route characteristics to predict the expected number of trips for all potential routes and using a cut-off consistent with the maximum distance traveled in our data set. Simulation results for the post-lockdown transport cost shock highlight the need for considering the extensive margin. This margin shrank significantly, affecting the remoter states disproportionately.

Finally, the supply chain disruptions due to the COVID-19 pandemic have been a major cause of inflationary pressure around the world in recent months.<sup>7</sup> There is also considerable press coverage of the disproportionate burden of inflation falling on the poor. The evidence on the extensive margin of trade shows that the burden of the rise in transport costs also falls disproportionately on the poorer and remoter states in India. Given that enforcement of COVID-19 restrictions in India was more frequent in major cities which also experienced more frequent outbreaks, this result suggests spillovers from these restrictions in major cities to relatively poorer areas through the transport and trade networks. Any economic recovery policy in India should take account of these potential spillover effects.

The rest of the paper is organized as follows. We start with a discussion of transport platform data in section 2. Section 3 presents the evidence on the size and determinants of transport cost followed by its behavior during the pandemic and post-lockdown periods. Section 4 is devoted to the estimation of trade elasticity. Section 5 discusses the counterfactual results. Section 6 concludes the paper.

> 7 Though the incidence of the pandemic was much milder in India before March 2021, the national lockdown created serious disruptions in the Indian economy during this time. The transport of essential goods was exempt from mobility restrictions during lockdown to minimize supply chain disruptions. Yet, supply disruptions ensued because of the way the lockdown was implemented. As opposed to the developed world where supply chain disruptions
mark>[1] Female is a dummy variable where 1 = female-led business and 0 if male-led business.</mark>

[2] School Closure due to COVID-19 is a dummy variable where 1 = schools closed in a country at the time of survey wave.

[3] OLS pooled regressions include Region of the world Fixed Effects and Time Fixed Effects and full set of controls including Firm Controls (Sector + Size, Age and Role in Business); Mobility Controls (Movement around retail & recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and Types of Dependents).

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Table 2: Summary statistics

|Variables|Mean|Median|Min.|Max.|Std Dev.|
|---|---|---|---|---|---|
|_Job postings:_||||||
|Monthly postings|656|71|0|103,542|3,022|
|Monthly postings low skilled|300|40|0|48,651|1,268|
|Monthly postings high skilled|357|29|0|55,248|1,800|
|_Exposure Measures:_||||||
|Output tariff exposure|0.43|0.21|0|9.41|0.73|
|Input tariff exposure|1.67|0.84|0|37.6|2.40|
|Export tariff exposure|0.41|0.24|0|16.2|0.78|
|_Other:_||||||
|Agricultural subsidies|16|3.72|0|143|25.1|

_Notes_ : This table displays summary statistics for all of the key variables used in the analysis. The dataset includes a total of 625 commuting zones, across 24 months (Jan 2017 - Dec 2018), totalling 15,000 observations. Summary statistics are taken across all CZ-time observations. Agricultural subsidy data is only available for 610 CZs. All tariff exposure measures can be interpreted in units of $1,000 per worker average exposure, and agricultural subsidies in units of $1,000,000.

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936|
|R-squared|0.097|0.095|0.135|0.094|0.097|0.068|
|Control mean|0.926|0.661|0.774|0.797|0.748|0.387|
|Diff M2-M3|0.0133|0.0107|-0.0141|0.0325|0.0429|-0.0410|
|p-val|0.542|0.782|0.665|0.328|0.231|0.294|

Notes: The table shows the results of regressing the variables regarding knowledge of the parental leave program on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,242 individuals in experiment 2 (304 men and 938 women). Source: Post-intervention survey.

# _Gender norms_

Table 7 shows the effects of the intervention on gender norm attitudes for individuals in experiment 2. Among fathers, the message about couple planning (M3) helped reduce traditional gender norms compared to the information and role model messages. It increased the level of agreement with the statement `It is 18
<br>Heavy Industries High-Skill Labor-Intensive Industries Heavy Industries High-Skill Labor-Intensive Industries Traditional Light Industries<br>Traditional Light Industries Food and Related Industries Food and Related Industries Manufacturing<br>E. 12 Largest Manufacturing Industries in 2003 as F. Four-Way Breakdown of Manufacturing as Shares<br>Percentage Shares in Manufacturing Value Added, in Employment and Enterprise Numbers, 1997 and<br>1970 to 2003 2002 (Establishment Census)<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10%<br>10%<br>0%<br>0% 1996 1997 1998 1999 2000 2001 2002 2003 1997 2002 1997 2002<br>Rubber and Plastic ProductsOther Non-metallic Mineral Products Machinery and EquipmentChemicals and Chemical Products Workers | Enterprises<br>Wearing ApparelTextilesRefined Petroleum ProductsMotor Vehicles Office, Accounting and Computing MachineryFuniture; Manufacturing n.e.c.Radio, TV and Communication Equipment and ApparatusFood Products and Beverages Heavy IndustriesTraditional Light Industries High-Skill Labor-Intensive industriesFood and Related Industries<br>1970 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text -->

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in the econometric analysis.

<u>Table 1: Equilibrium Exchange Rate: Alternative Concepts</u>

||**Definition of equilibrium exchange rate**|
|---|---|
|**Estimation-based appr**|**oach**|
|Black market exchange<br>rate premium|Black market exchange rate.|
|Law of One Price<br>(LOP)|Rate that equalizes prices of goods across countries, when converted in<br>the same currency.|
|Purchasing Power<br>Parity (PPP)|Rate that equalizes the real exchange rate to its level in a base year.|
|**Model-based approac**|**h**|
|Behavioral Equilibrium<br>Exchange Rate (BEER)<br>- Clark and MacDonald<br>(1997,1999)|<br>The BEER is based on reduced-form estimation of a long-run relationship<br>between the real exchange rate and economic fundamentals. Actual or<br>medium-term fundamental values are then used to obtain predicted values<br>of the real exchange rate,and an HP filter is applied,to obtain the BEER.|
|Fundamental<br>Equilibrium Exchange<br>Rate (FEER) -<br>Williamson (1983, 1994)|The FEER is the exchange rate consistent with simultaneous internal (zero<br>output gap) and external (sustainable current account) balance. It requires<br>estimation of an econometric model for the trade sector (trade elasticities)<br>and selection of the (sustainable) current account norm that is either<br>estimated empirically (MB approach) or derived as the level that stabilizes<br>the net foreign asset position (ES approach).|

> 8 Égert (2003) provides a comprehensive survey of the literature.

> 9 Several papers have dealt with this by adopting a cross-country panel framework to incorporate a wider range
|_IPV experience_<br>**World Value Survey Vari**|Ever experienced any form of physical or sexual<br>violence by past or current partner. Coded 1 if a<br>woman has ever experienced any violence and 0<br>for no violence and missing for women selected<br>and interviewed for the domestic violence module.<br>**ables**|D105a-k (has ever been<br>slapped; punched with fist or<br>hit by something harmful;<br>strangled or burnt;<br>threatened with knife/gun or<br>other weapon; CS physical<br>violence by partner; had arm<br>twisted or hair pulled;<br>physically forced into<br>unwanted sex; forced into<br>other unwanted sexual acts;<br>physically forced to perform<br>sexual acts respondent<br>didn't want to.|
|---|---|---|
|_Men have more right to a_<br>_job_|<br>1 if a woman or man agrees that when jobs are<br>scarce, a man has more right to a job than a woman,<br>and 0 if theydisagree or “neither” agree or disagree.|C001_01|
|_University is more_<br>_important for a boy_|1 if a woman or man agrees or strongly agrees that<br>university education is more important for a boy than<br>for agirl,and 0 if theydisagree or stronglydisagree.|D060|
|_A woman has to have_<br>_children to be fulfilled_|1 if a woman or man thinks that for a woman to be<br>fulfilled, it is necessary that she has children, and 0 if<br>theythink that it is not necessary.|D019|
|_Both spouses should_<br>_contribute to household_<br>_income_<br
population and the dramatic rise in the proportion of the population above age 65 pose a serious threat to the European growth model.**<sup>16</sup> The median age in EU11, which is currently comparable with the rest of Europe, is expected to increase 6.8 years on average by 2050 (Figure 18). In the rest of the EU the increase is expected to be 5.8 years.<sup>17</sup> The old-age dependency ratio in the EU11 will deteriorate with the share of the elderly in the population reaching 33 percent in 2060 from 16 percent in 2010. Currently, there are about 5 persons of working

> 16 The demographic prospects of economies in Central and Eastern Europe were discussed extensively in World Bank (2007). See Annex 2 for more detail.

> 17 These figures are based on the median scenario of the UN World Population Projections.

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