rafmacalaba/data-use-mentions-extended huggingface dataset:
. Annual median growth under the upper bound SGE simulations is 1.2 percentage points higher than the median historical growth used in the baseline.

Figures 16 presents the comparison between the debt ratio in 2016 and 2030, for the baseline and BRI scenarios. In Figure 16, the solid gray line is the 45-degree line. Any point above this line implies that the government will face a larger debt ratio at the end of 2030 relative to 2016. Any point below the solid gray line implies a reduction in debt ratio at the end of 2030. The dashed gray lines represent the debt ratio that is equal to 50 percent, as threshold effects for increased debt vulnerabilities. The analysis is limited to only 30 countries, which presents BRI debt financing (as for the WIND database) and the countries considered in the SGE model.

> 44 de Soyres et al. (2019) provide four different growth estimates for two scenarios. The two scenarios are referred as the “lower-bound” and the “upper-bound”. The “upper-bound” scenario allows for changes in transportation mode due to the new infrastructure, while the “lower-bound” scenario assumes that switching mode of transportation is difficult -allowing for modal changes lower than 5 percent with respect to the pre-BRI modes of transport. The four different growth estimates are derived by considering the: (i) additional BRI infrastructure; (ii) additional BRI infrastructure and tariff reduction; (iii) additional BRI infrastructure and additional reforms that lead to reduced boarded delays; (iv) additional BRI infrastructure, tariff reduction, and additional reforms that lead to reduced boarded delays.

26
0.052|0.044|0.047|
||(0.0837)|(0.0836)|(0.083)|(0.083)|(0.083)|(0.083)|(0.083)|(0.084)|
|Const|-0.5738***|-0.2077|-0.567***|-0.288|-0.567***|-0.303|-0.571***|-0.238|
||(0.1259)|(0.2491)|(0.128)|(0.271)|(0.127)|(0.267)|(0.127)|(0.254)|
|N. Obs|11,127|11,127|11,127|11,127|11,127|11,127|11,127|11,127|
|R2|0.0889|0.0925|0.092|0.095|0.094|0.097|0.090|0.094|
|CY FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|IQis|||Control of|Corruption|Rule o|f Law|Government|Effectiveness|

*** p<0.01; ** p<0.05; * p<0.1

30
targeting all travelers in only specic areas of low-malaria regions that are likely to receive many high-risk travelers.<sup>5</sup> Within each type, I compare a strategy that incorporates daily information on origins and destinations of travelers from mobile phone data with strategies that only use information on incidence in the previous year that could be implemented by the government in the absence of mobile phone data. The most cost-eective

> 5This paper does not focus on the type of targeting, but examples can include information campaigns targeted to travelers via mobile phone and strategically setting up testing sites.

3
4096|4096|4096|4096|4096|
|P-R2|0.983|0.962|0.959|0.961|0.927|0.967|
|MODEL**CB**|0.129|0.165|0.125|0.072|0.069|0.002|
||(0.145)|(0.296)|(0.349)|(0.483)|(0.729)|(0.988)|
|MODEL**DR**|-0.223**|-0.046|-0.524***|0.097|0.238|-0.225|
||(0.031)|(0.788)|(0.005)|(0.470)|(0.242)|(0.193)|
|Obs|4096|4096|4096|4096|4096|4096|
|P-R2|0.983|0.963|0.959|0.961|0.930|0.968|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp+ imp|Yes|Yes|Yes|Yes|Yes|Yes|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex Table A2. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter and importer are applied. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A4.

Table 4 shows the results when considering each
Observations|9,583|9,583|9,583|
|R-squared|0.636|0.635|0.636|
|1996 Controls|Yes|Yes|Yes|
|MunicipalityFE|Yes|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log per capita income for 2011-1996. Distance to major cities defined as the population-weighted average of the linear distance from given community to the 10 largest cities in South Africa (as explained in section 7.2). Distance to main road is the linear distance from given community to the nearest main road as defined by Statistics South Africa in 2001. All regressions include 1996 controls and municipality fixed effects. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
# **4** Data Analysis

## **4.1 Diagnostic Tests**

Prior beliefs regarding some of the tested covariates were summarized from the relevant literature as the first step of the analysis. This provided a theoretical point of reference from which to evaluate the posterior coefficients.<sup>14</sup> Models were run with delivery challenges measured at the cluster, category, and subcategory levels, systematically increasing the granularity of analysis and allowing for the disaggregation of substantive effects for each delivery challenge. Over 50 model averaging runs were conducted, testing the three levels of delivery challenges on project outcomes, performance, and supervision, including basic covariates and fixed effects, and testing a variety of alternate model priors.

In addition to basic controls such as project cost, duration, the presence of various specialists, setting, and Task Team Leader (TTL) quality, several indicators of aid effectiveness from Girod and Tobin (2016) were included to ensure that all relevant context- and project-based variables were accounted for. Before conducting the model averaging runs, data were tested for collinearity and missingness issues in order to ensure that variable selection was not unduly affected by structural faults inherent in the samples tested. Inclusion of collinear indicators can introduce an unintended stochastic element that may even lead to one of the collinear variables being chosen at random. Similarly, listwise deletion of observations can severely impact predictive power. Such tests were conducted on diagnostic subsets of control variables used as well as all subsets used in model averaging runs. Furthermore, models were run using both original and imputed data to guard against potential idiosyncrasies introduced by systematic missingness.<sup>15</sup> Model averaging runs were conducted on the delivery challenges as well as five additional subsets: the first two tested delivery challenge clusters alongside the control variables and fixed effects, respectively. Subsets three and four tested the delivery challenges at the category level against the same controls and fixed effects. Finally, the last specification tested subcategories of delivery challenges alongside
## Overview of the paper

The paper examines methodological approaches to improving socioeconomic survey data on rural women’s employment amid rural economic transitions, presenting stylized facts from recent World Bank Living Standards and Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) conducted in Ethiopia, Malawi, Nigeria and Uganda, as well as recent country pilot studies conducted by the ILO to test survey question design under the 19<sup>th</sup> ICLS recommendations. These surveys are of particular interest in the context of rural employment, given their detailed modules on agriculture along with other sectors, as well as a broad range of individual and household socioeconomic characteristics that can be used to better understand employment trends.

The paper covers five main priority areas (from both research and policy perspectives) in improving the measurement of rural women’s employment in surveys:

- (a) Accurate counting of rural women’s employment, since their labor contributions are often underreported, and particularly given the revised definitions of work and employment under the 19<sup>th</sup> ICLS;

- (b) Survey questions to better understand labor underutilization;

- (c) Measuring unpaid work burdens and the role of time use surveys;

- (d) Survey respondent choice in intra-household decision-making questions on earnings and farm/business management roles; and

- (e) Capturing additional features of women’s work amid rural transition — including migration, skills development, access to productivity-enhancing technologies — along with barriers to economic mobility (or being able to seek better economic opportunities).

The aim of the discussion is not to recommend that all issues be tackled together, particularly given cost and other considerations, including country context — but to highlight important measurement issues for further research and policy design, the implementation of which could be determined based on local priorities. Variations also exist across surveys in how employment questions are asked and modules are structured, including within the LSMS. This paper compares different survey approaches to understand best practices in presenting a more comprehensive
4.5.1: <u>Restricting the Sample to Relatively Poor Households. The limited impacts of aid on food</u> consumption outcomes may be due to a number of reasons. The first is that aid really does have limited impacts on food consumption outcomes. The second is that the sample does not provide sufficient power to estimate modest impacts of aid on food consumption outcomes. For value of food consumed and calories per capita in particular, we would expect that the impacts of aid would be relatively higher for those with low levels of food consumption outcomes, since aid would then form a larger percentage of the food sourced. Targeting errors can then exacerbate the difficulty in estimating the impacts of aid on food consumption, since we would expect more muted impacts if aid is received by relatively wealthy households. But we cannot introduce yet additional instrumented variables. Instead, we limit the sample to those households with wealth index levels below the median (.195). The results for the four food consumption outcomes are reported in the top panel of Table 7 for the two-shock specification.<sup>23</sup> As can be seen, we are more likely to observe impacts of aid on food consumption outcomes versus results from the full sample, particularly for those households that have experienced a sequential shock. For the both the IHPS and FIAS samples, there are significant negative impacts of a sequential shock on the value of food consumed per capita, but also significant positive impacts of receiving aid under a sequential shock. For FIAS, calories per capita are also negatively affected by a sequential shock but aid under a sequential shock has a positive impact. For IHPS, households who experienced a currentperiod only shock but also received aid reduced food purchases. We do lose significance on the food consumption score for the IHPS sample, however.

4.5.2: Restricting the Sample to Households Interviewed during September-March. Another reason we might not be able to detect changes in food consumption outcomes from receipt of food
social gatherings’, ‘cultural and religious events’.

- Third, the deterioration of social capital in the US over the most recent decades, as claimed by Putnam (2000) and confirmed by others (Costa and Kahn 2003; Robinson and Jackson 2001), is associated with the decline of individuals’ happiness in the same country, as reported by Layard et al. (2009), Easterlin and Angelescu (2009), and Stevenson and Wolfers (2008). This linkage seems to be significant and relevant, even after controlling for individuals’ income and comparison income (Bartolini et al. 2008; see also Bjørnskov 2008).

> 2Similar conclusions are found by Winkelman (2009), who has used panel data for Germany, and by Leung et al. (2011), who have used non-panel data for Canada, while Ram (2010) has found more fragile results by using country data instead of individual data.

4
of changes in the stock of public capital on economic performance; Aschauer, 1997. The main focus of that research has been to determine the importance of public infrastructure on the private sector's productive capacity, to its costs of production and/or to its level of profitability. By and large, this research concludes that public capital exerts an important positive influence on the performance of the private sector economy. But these findings - - which are static in nature -- have left open the question of the dynamic, or long run, effects of public capital on the economy."<sup>3</sup> More recent attempts to capture the dynamic

> 1 The existence of such influences is discussed in the 1998 World Bank's Country Economic Memorandum on Mexico: Enhancing Factor Productivity Growth. It draws attention to the failure of Mexico's productivity growth to recover as quickly as in other countries after inflation was stabilized and structural economic reforms had been implemented.

> A note of caution is in order here: The disaggregated capital stock series used for this comparison are based on an earlier constant price series, with base-1970, produced by the Banco de Mexico. These series were recently re-based (to base year 1980), which resulted in significant revisions in some individual series (notably Communications), while the series for the Electricity sector was discontinued.

- 13 The few empirical studies that have focused on the Mexican economy (e.g., Shah, 1992, Feltenstein and Ha, 1995, and Nazmi and Ramirez, 1997) are of this type. They investigate the relationship between
6) Child birth complication index<br>12|_−_0.619|_−_0.002|0.975|_−_0.619|2.649|856|Yes|
|(7) Child illness index<br>1|_−_0.091|0.028|0.924|_−_1.435|3.424|4993|Yes|

Maternal death is defined as death relating to either pregnancy complications or childbirth. The Pentavalent Vaccination targets diphtheria, tetanus, whooping cough, hepatitis B, and haemophilus influenza type B. †: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5); _♦_ : measured across all health episode types (ante and post natal care, child birth, under-2 vaccinations, and illness and injury); w , 1 , 6 , and 12 stand for measures referring to the last week, one month, six months and twelve months respectively.

**Table B.4:** Satisfaction

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|---|---|
|(1) Satisfaction with family health §|4|3.469|0.657|1|4|5052|Yes|
|(2) Satisfaction with public health workers §|3|3.291|0.791|1|4|4994|Yes|
|(3) Satisfaction with care<br>1 _♦_§|4|3.658|0.670|1|4|2535|Yes|
|(4) Would return to clinic_♦_†|1|0.969|0.168|0|1|2527|Yes|

†: dummy variable; §: likert scale
Figure 21: Percent of households consuming electricity by decile <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Per capita market income decile<br>0–100 kWh 101–200 kWh 201+ kWh Not connected to grid/not consuming<br>Source: Authors’ calculations based on CSES 2019/20 and fiscal data.<br>Note: A kilowatt hour (kWh) is a measure of how much energy is used per hour.<br><!-- End of picture text -->

# **Limitations and interpretations**

The CEQ analysis provides useful insights about the effects of Cambodia’s tax and benefits system, but we must acknowledge some limitations of the CEQ methodology and data, and their implications on interpreting the CEQ results. First, the CEQ looks at only part of the fiscal system and does not fully capture some taxes paid by households or benefits received from public goods. For instance, public infrastructure could be progressive through its impacts on employment and economic activity. Second, while the data are nationally representative, they are not designed to fully represent high-income households, a phenomenon standard with household surveys in all countries. While missing top incomes does not affect our ability to measure poverty, it has important consequences for inequality measures. If many richer households are not captured in the survey, “true” inequality will be higher. This might attenuate distributional implications and redistributive effects of various instruments since the higher burden of taxes on non-represented rich households is not included. This is not a flaw in the survey data, which are not designed to accurately measure the very top of the income distribution, but it does have implications for fiscal incidence analysis. Another limitation is the challenge of assigning direct taxes to individuals when the data does not
starting in June 2009 (Galhardi 2009) and some of the amounts were still paid out in later years: out of the total US$63 million, US$53 million were disbursed in 2009, US$1.5 million in 2010 and US$8.5 million in 2011 ( _Secretaría de Economía_ 2012).

# **3. Data and identification strategy**

# **3.1. Data sources**

Data on employment come from IMSS. The data are at the industry level (as per the IMSS industry classification) and include the total number of employees registered with IMSS in each month from January 2000 to November 2013. I use the data for years 2004 through 2013 to estimate the effects of the wage subsidy program and the data for 2000 to 2003 for a falsification exercise.

I also use monthly data on US imports (total aggregated from world) for the period of January 2004 to December 2013, as a control variable. This data come from the US International Trade Commission’s Interactive Tariff and Trade DataWeb and were hand matched to the IMSS industry data using converted Harmonized Tariff Schedule (HTS) codes, at the 4 or 6‐digit level, depending on the industry. Due to differences in the industry classifications, not all industries could be matched, so that US import data are missing for a quarter of the IMSS industries in the study sample.

> 4 Calculations based on data from IMSS for December 2008. IMSS reports number of employers, which may either be plants or firms.

5
the meeting. Column 4 of table 4 shows that women in the information session are significantly more likely to have attended a village meeting in the last month than women in both the control group and the information only group. Note that this is only suggestive evidence as the previous month the question refers to takes place after the intervention.<sup>12</sup> Column 1 of table 4 shows that expenditures in the past month were not significantly higher in the information plus cash group, though in column 3 we

> 12Additionally, at the midline survey, when the question refers to a month during which the intervention is taking place, there is no statistical difference in attendance across treatment groups. However, issues of attrition during the midline survey mean that this finding is not completely reliable.

12
# 4.2 Randomization Balance Tests

As shown via balance tests in Appendix Table A2, the subsample of households who were subjected to HAP monitoring (n=204) are representative of the overall sample (n=504<sup>8</sup> ). We test balance across treatment and control households using 39 variables for the subsample that had HAP monitoring. A total of 23 variables are from the baseline survey and 16 are from the endline survey. Though randomization occurred prior to the baseline, we add endline variables related to temperature, humidity, cooking/baking area floor area, other cooking area characteristics and cooking characteristics from 2016 that were not collected in 2013, because these variables may affect 2016 HAP measurements and are invariant to whether a household is in the treatment group.

We find that all 2016 variables are balanced across treatment and control subsamples, suggesting that HAP monitoring will yield an unbiased estimate of the effect of Mirt on HAP. From the 2013 baseline survey, only one variable – existence of metal roofs on main houses - is not balanced at the 5% significance level, which is a rate that is just as likely to be due to chance given the 5% significance threshold; though a majority of both treated and control households have metal rather than thatched roofs, treated houses are more likely to have metal roofs. All balance test results are presented in the Appendix.

# 4.3 Treatment Effects of the Mirt ICS on Household Air Pollution

Table 1 presents the basic findings from the HAP monitoring. As shown in Table 1, consistent with the literature (Ojo et al., 2015; Chen et al., 2016; MacKracken and Smith, 1998), the mean and maximum values are extremely high. The average of the mean concentrations across the sample is 1,231  g/m<sup>3</sup> . This figure is about 50 times the WHO (2015; 2006) 24-hour guideline of 25  g/m<sup>3
(0.012)|(0.048)|(0.078)|(0.046)|(0.009)|
|p: public = private|0.002|0.011|0.028|0.030|0.769|
|Mean outcome|0.309|8.848|159.291|9.840|0.120|
|# observations|6607|6598|6589|6574|6575|
|# clusters|84|84|84|84|84|

Coefficients are from regressing each outcome on a vector of treatment assignments, randomization block fixed effects, and prespecified baseline covariates (measured skills, self-reported skills, education, age, gender, employment, discount rate, risk aversion). Heteroskedasticity-robust standard errors shown in parentheses, clustering by treatment date. Mean outcome is for the control group. Skill belief accurate is the share of the six assessments where the candidate’s perceived tercile matches their actual tercile. Targeted search is an indicator equal to one if the candidate reports mainly applying for jobs that most value the skill in which the candidate scored highest. Above-median self-esteem is an indicator equal to one if the candidate’s response on a shortened version of the Rosenberg (1965) self-esteem scale is above the sample median. All outcomes use a 7-day recall/forecast period unless marked with<sup>a</sup> (30-day recall/forecast period) or<sup>b</sup> (since treatment). Outcomes marked with<sup>c</sup> use the inverse hyperbolic sine transformation for the treatment effects but the control group means are reported in levels. All monetary figures are reported in South Africa Rands. 1 Rand _≈_ USD 0.167 in purchasing power parity terms. The sample sizes differ across columns due to item non-response, mostly from respondents reporting that they don’t know the answer.

19
> ). Based on this vector and Eq. (2), we can simulate outcomes _Zi_<sup>_sk_</sup> for the students in each wave (drawing the school effects _ηs_<sup>_E_from the Standard Normal</sup> distribution). The first simulated sample uses equal assignment shares, the second is generated under an adaptive design, where the assignment shares for wave 2 are obtained from estimating our model above from the simulated wave 1 data. We can then compare the estimation results under these two sampling strategies to calculate the predicted gains from the adaptive vs. the non-adaptive design for the given parameter vector. This is reminiscent of conducting power calculations for an assumed effect size.

Panel A of Table 7 shows the result of such an exercise, using as the parameter vector the mean of the posterior distributions of _β_<sup>_E_</sup> and _κ_<sup>_E_</sup> after wave 2, as reported in Table 3. Using the wave-2 estimates from the experiment serves to show how well the ex ante simulation does in predicting these estimates, and how ex ante simulation results compare with the ex post simulation above. The predicted gains from using adaptive sampling in terms of posterior regret are very similar to our previous exercise based on the actual IVR data. The average posterior expected regret from arm T1B is 0.02% with adaptive sampling but 0.08% with the “standard RCT” on average. The average posterior probability optimal for both sampling strategies is also similar to what we obtained in Table 6.

39