rafmacalaba/data-use-mentions-extended huggingface dataset:
This approach gives consistent estimates of the CHB index if the gravity equation is correctly specified, i.e. if the country-specific fixed effects are consistent estimates of their theoretical counterparts. Fally (2015) shows that this automatically holds true when the Poisson pseudo-maximum-likelihood (PPML) estimator proposed by Silva and Tenreyro (2006) is employed and income and expenditure are consistent with bilateral trade flows (i.e., _Yi,t_<sup>_k,s_= �</sup> _j_<sup>_X_</sup> _ij,t_<sup>_k,s_;</sup><sup>_E_</sup> _j,t_<sup>_k,s_= �</sup> _i_<sup>_X_</sup> _ij,t_<sup>_k,s_).The peculiar properties of the estimator implies that</sup> the actual income and expenditure values equal the predicted ones, which should normally be used as both are endogenous in the general equilibrium gravity model. We thus employ the PPML estimator, which has the added advantages of controlling for heteroskedasticity in the data and statistically dealing with zero trade flows. The gravity equation in eq (6) is estimated separately for each market _s_ and sector _k_ , although covariances of the estimated coefficients are taken into account when testing significance of the difference in across public and private gravity.

To bring this empirical artillery to the data, we need information on bilateral trade that involves the public sector as a buyer, on a large enough sample of countries. Other studies that investigate trade barriers in government procurement employ data from inter-country input-output tables (Riker, 2013; Messerlin and Mirodout, 2012) as these can split public expenditures from national accounts across type of goods and services purchased and country of origin. We thus follow this route and employ data from the TiVA initiative of the OECD. Similarly to other ICIO database (e.g., Timmer, 2012), the TiVA database harmonizes national IO
CLEMENS & TIONGSON applicant does not affect the probability that children are working, the probability that anyone in the household reads to the child in the evenings, or the years of education that the respondent wishes the child to someday attain.

We explore heterogeneous reduced-form impacts by pre-treatment subgroups in Appendix section A1.

# 6.4. Selection bias and nonexperimental reduced-form results

Could the preceding effects have been well-identified without the quasi-experiment we use? It is plausible that households with migrants differ in many ways from households without migrants, ways that might not be the result of migration. If all such differences were observable, quasi-experimental methods like RDD would have less value. Here we test for differences in unobservable determinants of outcomes between the survey sample and the national population.

We follow LaLonde (1986), Smith and Todd (2005), McKenzie et al. (2010), and others in constructing analogous nonexperimental tests of migration treatment effects by using the nationally representative data in Table 10 to construct a synthetic control group. That is, we create a new dataset that retains only treated households from the survey sample and stacks them with _all_ households in the nationally representative sample.<sup>23</sup> We then estimate treatement effects with ordinary least squares (OLS) and propensity score matching (PSM), for comparison to the RDD estimates.

Table 8 shows this exercise for selected RDD regressions. The first column simply reproduces the ITT result from RDD analysis in an earlier table. The second column shows the coefficient on the treatment variable in an OLS regression including numerous controls. If we could not carry out a quasi-experimental analysis, it might be tempting to think that these controls capture much of the important economic difference between

> 23This plausibly assumes that the fraction of Filipino households that have had a member in Korea is very small. The FIES-LFS data contain an indicator of whether or not household members are currently abroad, but do not contain information on specific destinations nor on past migration experience.

20
(few lenders offer them), there is also a risk of poor negotiation outcomes. In both cases, the risk is higher when the outcome is subject to limited scrutiny. Finally, RBLs may utilize transaction structures that are not reflected in standard debt statistics or may confer undisclosed seniority or payment advantages to their lenders, which ultimately leads to a mispricing of the country’s risk by other creditors.

# c. Cost of financing

We compared the interest rates on RBLs to the rates on regular loans. We identified 19 RBLs for which we had the necessary data on interest rate and maturity needed for evaluation. We compared each of these loans to an individual control group with similar features (other than collateral) constructed based on the WB Debt Reporting System, which contains over 25,000 loans. We filtered the data to include only those loans extended by private sector and bilateral creditors to the public sector. The loan features used for matching were for borrower countries that have similar risk ratings (splitting countries between those classified by the WB/IMF debt sustainability analysis as low or medium risk vs high risk and in debt distress vs not scored), same currency, same interest type (floating vs fixed), similar maturity, similar prevailing global lending rates at time of signing (Libor within +/100 bps range). Each control group contains at least 5 loans, some as many 500 loans. We compared loan terms based on their weighted interest rate, a metric used by WB to denote average interest rate for complex loans. Figure 5 below denotes key results.

19
_Epistemology, RCT methodology, and research transparency._ Researchers have begun to examine large sets of studies to understand the “political economy” of designing, conducting, and publishing experiments (e.g., Andrews and Oster (2019) on external validity bias; Gechter and Meager (2022) on the role of pre-existing infrastructure for site selection; H ̈offler (2017) on how data publication influences citation rates; Anderson and Magruder (2017) on how to reduce the number of false discoveries; or Christensen and Miguel (2018) on the adherence to transparent research practices).

_Summary statistics._ RCT data can help both researchers and the broader public to better understand underrepresented populations or get an overview of the body of experimental evidence. Especially in lowincome contexts, RCT data delivers a detailed picture of populations that are typically not well-represented in available data – neither government data, such as (formal) labor market statistics, nor private data, such as bank records. RCT data can be used to extract stylized facts, conduct exploratory research or power calculations, and more. For example, the non-profit AidGrade (2019) compiled a database of standardized effect sizes and standard errors to facilitate simple forms of comparative analysis (e.g. Vivalt, 2015, 2019). The metadata schema is designed to benefit all these different applications by

- enabling filtering datasets on criteria such as unit of randomization or intervention assignment strategy;

- collecting information specific to RCTs such as interventions, treatment arms, or treatment compliance;

- facilitating the combination of multiple RCT datasets by documenting features such as available covariates, time period covered in the data, or inclusion/exclusion criteria;

- recording external resources such as registry entries, ethics review protocols, and academic publications, and documenting information such as whether a pre-analysis plan exists or who funded or partnered in the implementation of the RCT.

# **3 Creating the Metadata Schema**

We followed the process outlined in ISO (2021) in creating the metadata schema.
the probability of having problems during their pregnancy. Unless they are in pain, they do not seek care. Optimism bias seems to decrease with age, but women with children are less likely to attend ANC or to deliver in a health institution.

Lack of transportation is an important barrier to reaching care. Women without access to transportation are less likely to reach maternal healthcare services. Conversations with women revealed that, without access to transportation, they often have to walk long hours or rely on motorbikes or tap-taps (public buses or pick-up trucks). While common in urban areas, motorbikes or tap-taps are rather scarce in rural areas. Moreover, given the poor conditions of rural roads women often fear for their own safety and that of their baby. As is the case with seeking care, the high transportation costs may prevent them from going to healthcare centers despite intending to. Structural barriers are real, and women’s concerns about their impact are often rational and warranted.

Finally, when receiving care, the way hospitals and medical staff make women feel, and the perceptions around the quality of care, matter as much as the care itself. Qualitative findings reveal that women often perceive the care they receive from nurses and doctors as disrespectful. Women are usually not welcomed or treated as they would like. Moreover, women dislike the model of care provided in hospitals, finding that they do not respect local customs such as choosing the birthing positions or being surrounded by family members for labor. Quantitative data shows that women living in clusters where a large share of women have attended ANC consultations are more likely to do the same, suggesting that peer effects or role models may trigger knowledge sharing about their experiences in a positive manner. It is important for hospitals and medical staff to prioritize good communication and respectful treatment of patients to ensure that women feel comfortable seeking the care they need.

Surprisingly, interviews revealed that husbands may play a key role in encouraging their wives to attend institutional care. Not only are they often convinced of
incidence of violence and women’s economic standing and access to opportunities (Eswaran and Malhotra 2011, Tauchen, Witte and Long 1991). Women’s ownership of assets and their relative share of wealth can increase their bargaining power within the household and hence can act as a deterrent to physical violence against them (Agarwal and Panda 2007; Panda and Agarwal 2005; Oduro, Deere, and Catanzarite 2015). Similarly, cash transfers provided to women and intended to reduce poverty and food insecurity have been shown to decrease physical and sexual violence (Hidrobo, Peterman and Heise 2016). Alternatively, higher bargaining power may attract more violence by threatening the existing male dominance, as suggested by the backlash theory of violence (Koenig et al. 2003, Rocca et al. 2009). The evidence suggests that the association of financial autonomy or higher bargaining power within household with protective role of violence is influenced by context specific factors (Vyas and Watts 2009).

In addition to who makes decisions and has bargaining power, whether decisions are made independently or jointly may also influence IPV. Indeed, empirical papers from demography, development studies and public health have established a relationship between women’s responses on joint decision making patterns and IPV. Coleman and Straus (1986) provide the first evidence of this relationship using data on US couples. While the authors expected that families making decisions jointly would have the highest degree of conflict, they found the opposite. Kim and Emery (2003) replicate their study in the Korean population and encountered similar results. Hindin and Adair (2002) use the Philippines Cebu Longitudinal Health and Nutrition Survey (CLHNS) and find that only 6% of women reported IPV when all household decisions were made jointly compared to 25% when no decisions were made jointly. Using the 2000 Haiti DHS, Gage (2005) finds that women who had the final say on major household purchases were almost 2.7 times more likely to experience emotional violence, and 1.7 times more likely to experience
year dummies; _α, β, γ, δ, η_ = Parameters to be estimated; _ε_ = Error term; _i_ = individuals; _c_ = countries and _y_ = years. We estimate the life satisfaction equation cross-section on a pooled sample of world citizens as described below: A wide range of reduced specifications will be considered as well as alternative estimations of the standard error. We use the robust Huber-White sandwich estimator and regional clusters for a robust estimation of the standard error. As shown below, the dependent variable is categorical and all estimations are made with an order logit model.

As a measure of subjective well-being ( _H_ ), we use _Life satisfaction_ . The question asked is: “ _All things considered, how satisfied are you with your life as a whole these days?_ ” Answers include a ten steps ladder where ‘1’ stands for “ _Dissatisfied_ ” and ‘10’ stands for “ _Satisfied_ ”. This is a common question used in happiness research and validation studies conducted by psychologists and social scientists show that answers to such questions are reliable (Lepper 1998, Sandvik et al. 1993, Fordyce 1988, Inglehart 1990, Saris and Scherpenzel 1996).

_Income_ ( _X_ ) is measured as self-positioning in a ten-steps income scale where the income brackets have been measured in local currency in each country.<sup>10</sup> This is not self-declared income but the positioning of individuals into income brackets. In some sense, this is a more accurate indicator than self-reported income which is known to be underreported in household surveys worldwide. That is because people are not asked to tell how much they earn but simply to say to which income bracket they belong to.

For cross-country comparability purposes, the income variable has been further transformed into mid-class values, real terms, USD and Purchasing Power Parity (PPP). In the World and European Values surveys, each country uses a ten steps income scale where each step is reported in local currency. For each country/year, we first calculated
higher education, driven largely by the private sector. Progression from secondary to tertiary education is available through two routes: enrollments in universities/colleges administered by the Tanzania Commission of Universities (TCU) and in nonuniversity technical education institutions regulated by the National Council of Technical Education (NACTE). The total number of students enrolled in degree programs in universities almost doubled from 81,782 in 2008/09 to 162,510 in 2012/13.<sup>15</sup> Enrollments in technical education institutions under NACTE more than tripled, from 36,586 in 2007/2008 to 112,447 in

> 14 3.7 percent (UIS 2012 data)

> 15 TCU-administered universities and colleges also enroll students (about 20 percent of total enrollments in 2012/2013) in certificate and diploma programs.

7
to their husbands or even to another household member.

Second, the near universal program take-up results in a 55-percentage-point increase in women’s employment, after accounting for incomplete take-up, delegation, and limited crowding out of self-employment. There is no labor response from husbands (mainly limited to instances of delegation) or other household members. After the short-term employment opportunity ended, women either revert to remaining unemployed or to searching for employment.

Third, based on detailed time-use data, we document that beneficiary women accommodate the additional outside earning opportunity by assigning some of their household chores to other household members and entrusting the care of their children to other women, including female neighbors.

Fourth, we estimate the average participant’s net income gains during the program and find that it was very close to the gross wage weekly transfer (with an estimated 16 percent of foregone income). These net income gains correspond to a 30 percent increase in household labor income. In line with the temporary nature of the program, women save most of their 3
will increase with the fall in protection. The dualism of the market structure will be further reduced with the abolition of the complex trading rights system,<sup>50</sup> local content requirements, and foreign balancing rules.

China’s WTO accession commitments do not require the duty drawback removal,<sup>51</sup> yet given the fall in statutory tariffs and the negative aspects associated with the drawback system, it becomes important to assess ways of unifying the country’s tariff structure. One way to accomplish this will be to remove duty drawbacks after 2007. An alternative way will be to remove tariffs on manufactured imports thus making duty drawbacks irrelevant. Yet another alternative is to abolish duty drawbacks and halve tariffs on manufactures.

We use the empirical multi-region, multi-sector general equilibrium model in section 3 to provide answers to some of these questions. The results however should be interpreted with caution. They are not predictions but are estimates reflecting the model’s assumptions and the key forces driving the outcomes.

# _4.2.1 Data and simulation design_

The starting point for the numerical simulations is the modified GTAP version 5 database in Ianchovichina and Martin (2004). This database is aggregated to 20 regions, 25 sectors and 5 factor inputs and is changed to reflect the presence of duty drawbacks.<sup>52</sup> The database also reflects growth in the world economy to 2007<sup>53</sup> and the implementation of

> 50 China has agreed to terminate limitations on trading rights upon WTO accession, and in the case of designated trading over a 5 year period.

> 51 WTO explicitly allows duty drawbacks/exemptions for use in the production of export up to the value of the liable duty.

> 52 Ianchovichina (2004) discusses in detail the implementation of the empirical model in GEMPACK (Harrison and Pearson, 1996) and the changes to the GTAP data necessary to incorporate duty drawbacks. 53 The growth projections are based on World Bank projections
around 5 years window around the custody cutoff and whom did not have children after the reform. Wild bootstrap (WB) p-value using one thousand replications. *p<0.10, **p<0.05, ***p<0.01.

# _5.2. A set of legislation that targets several areas of gender-based discriminations reduces cultural norms’ stickiness inching towards desired outcomes_

As for the second control, it captures the set of legislative advancements aimed at reducing the legal gender discrimination in these countries. For this, data from the World Bank’s Women, Business, and the Law Index (WBL) (1971 – 2021) scores based on the average of a country’s scores each year for 8 topics pertaining to mobility, workplace, pay, marriage, parenthood, entrepreneurship, assets, and pension are used. Admittedly, while the passing of laws does not guarantee desired outcomes, namely that they will be enforced, a set of legislative reforms that target several areas of gender discrimination may help create a more favorable environment as they are actionable in the short run compared to social norms and traditions, which take longer to change (Kahan, 2000; Alesina et al., 2013; Hyland et al., 2020). Moreover, there are documented cases of unintended results of a single gender 22
47|PT Males<br>1.5|
|'<br>sta|UE|19|3|1|1|23|FT Males<br>0.6|
|ales<br>or|PT|9|1|0|0|11|PT Females<br>1.2|
|M<br>lab|FT|16|2|1|1|19|FT Females<br>0.5|
|All fe|males|83|10|2|4|100||

_Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey.

_Note:_ Abbreviations: FT= employed full-time; PT= employed part-time; U= unemployed; NP= nonparticipant. The numbers next to the labor market labels are the proportion of husbands and wives in each labor market state for each country. Per panel, the proportion of husbands and wives should sum up to 100. Because of approximation, for some panels, the sum is equal to 101.

**Table 2, (right panel) shows that in terms of hourly wages, there is a wage gender gap of 27 and 24 percent for part-time and full-time jobs, respectively.** Additionally, irrespective of gender, parttime workers earn in hourly terms more than twice the salary of full-time workers.

# **4. Estimation and Identification**

**The identification is provided by groups of parameters; first, we selected some moments from the RRS, and we assumed nine functional forms to identify 35 model parameters.** As mentioned, the model aims to explain spouses’ behavior in terms of their labor market status (full-time, part-time, unemployment, and nonparticipation), as well as their time allocation (leisure, work, and home production). Presence of children in the household is used as a shifter for spouses’ nonparticipation and intensive margin decisions. All the gender-specific parameters rely on gender differences found in participation
|Control of Corruption (lagged)|Control<br>of<br>Corruption<br>indicator<br>from<br>Worldwide Governance Indicators. Higher<br>values of the variable imply lower corruption.<br>The variable is lagged by 2 years from the<br>fiscal year covered by the Enterprise Survey in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years), where the weights used are the<br>proportion of months covered in the calendar<br>year.<br>Source: Worldwide Governance Indicators,<br>World Bank.|
|---|---|
|Profit Tax Rate|The amount of taxes on profits paid by the<br>business expressed as a percentage of total<br>commercial profits of the business as per the<br>Doing Business methodology, World Bank. To<br>make the data comparable across countries,<br>several<br>assumptions<br>are<br>made<br>about<br>businesses. The main assumptions are that they<br>are limited liability companies, they operate in<br>the country's most populous city, they are<br>domestically owned, they perform general<br>industrial or commercial activities, and they<br>have certain levels of start-up capital,<br>employees, and turnover.<br>The values of profit tax are for the last fiscal<br>year that is covered by the ES. In cases where<br>the fiscal year spanned more than one<br>calendar year, the figure used is the weighted<br>average over the calendar years covered,<br>where the weights used are the proportion of<br
. In addition, climate conditions vary significantly across region in the country. The northern and eastern mountainous regions are relatively cool in Albania. By contrast, the coastal and southern areas are hotter (Figure 5). In 2003, temperature was high, exceeding 30°C almost all over the country. Summer 2004 was relatively cool. Correspondingly, more precipitation fell over most of the country in 2004 (Figure 6). Summer 2002 was driest among the sample years.

<!-- Start of picture text --> Figure 5. Monthly average maximum temperature by district<br>June 2002 June 2003 June 2004<br>Monthly average Monthly average Monthly average<br>maximum maximum maximum<br>temperature temperature temperature<br>23.9 - 25.0 25.5 22.2 - 25.0<br>25.1 - 27.0 25.6 - 27.0 25.1 - 27.0<br>27.1 - 29.0 27.1 - 29.0 27.1 - 29.0<br>29.1 - 31.0 29.1 - 31.0 29.1 - 29.5<br>31.1 - 33.0 31.1 - 35.3 29.6 - 29.9<br>. . .<br>Source: Author’s drawing based on weather data from IEWE, Albania.<br><!-- End of picture text -->