rafmacalaba/datause-encoder-data huggingface dataset:
Kraay, Aart, Norman Loayza, Luis Serven, and Jaume Ventura, “Country Portfolios,” _Journal of the European Economic Association,_ 3 (2005), 914–945.

Kraay, Aart, and Jaume Ventura, “Current Account in Debtor and Creditor Countries,” _Quarterly Journal of Economics_ , 115 (2000), 1137–1166.

Lane, Philip, and Gian Maria Milesi-Ferretti, “A Global Perspective of External Positions,” in _G7 Current Account Imbalances: Sustainability and Adjustments,_ Richard Clarida, ed. (Cambridge, MA: National Bureau of Economic Research, 2006).

Lettau, Martin, and Sydney Ludvigson, “Consumption, Aggregate Wealth, and Expected Stock Returns,” _Journal of Finance,_ 56 (2001), 815–849.

Lynch, Anthony, “Portfolio Choice and Equity Characteristics: Characterizing the Hedging Demands Induced by Return Predictability”, _Journal of Financial Economics,_ 62 (2001), 67– 130.

Mendoza, Enrique, Vincinzo Quadrini, and Jose-Victor Rios-Rull, “Financial Integration, Financial Deepness, and Global Imbalances,” University of Pennsylvania, mimeo, 2007.

Merton, Robert, “Optimal Consumption and Portfolio Rules in a Continuous Time Model,” _Journal of Economic Theory_ , 3 (1971), 373–413.

Normandin, Michel, and Pascal St-Amour, “An Empirical Analysis of U.S. Aggregate Portfolio Allocations,” CIRPEE Working Paper No. 05-03, 2005.

Rogoff, Kenneth, “Purchasing Power Parity Puzzle,” _Journal of Economic Literature,_ 34 (1996), 647–668.

Sangvinatsos, Antonios, and Jessica Watcher, “Does the Failure of the Expectations Hypothesis Matter for Long-Term Investors?,” _Journal of Finance,_ 60 (2005), 179–230.

Tille, Cedric, and Eric Van Wincoop, “International Capital Flows,” NBER Working Paper No. 12856, 2007.

44
# **Figure 4: Distribution of age by employment category**

<!-- Start of picture text --> All Low and Middle Income Countries-Male<br>a15_24 5 03 21 22 49<br>a25_39 16 2 2 37 32 11<br>a40_49 18 3 1 35 36 8<br>a50_65 14 21 27 38 19<br>0 20 40 60 80 100<br>percent<br>All Low and Middle Income Countries-Female<br>a15_24 302 10 12 74<br>a25_39 7 02 16 19 56<br>a40_49 8 12 18 22 49<br>a50_65 7 11 17 19 56<br>0 20 40 60 80 100<br>percent<br>own_account employer<br>non_paid_employee wage_and_salary<br>agriculture not_employed<br><!-- End of picture text --> 31
person in the ESS 5 context and over the phone as part of the high frequency phone surveys, but there is a lag of nine months between these interviews, rendering the direct comparison of estimated vaccination rates difficult. Instead, we propose a more indirect test of this hypothesis through a comparison of vaccination rates according to administrative records to vaccination rates estimated in the in-person survey. We argue that if the phone survey mode effects were responsible for the observed misalignment between surveys and administrative records, the misalignment should disappear in in-person surveys. The prediction we test is the following.

**Prediction 3:** In-person surveying will display reported vaccine uptake close to administrative records.

where _Vi_ is the vaccination status of individual _i_ , _α_ is a constant, A is the COVID-19 vaccine coverage rate as reported in administrative data and _t_ the test statistic obtained from a standard t- Test. If our prediction is confirmed, comparing _t_ to the critical value of a Student t-distribution will no longer reveal a statistically significant difference between in-person survey estimates and the administrative data.

<u>Hypothesis 4: Panel conditioning/Survey participation effects</u> The phone surveys we conduct are longitudinal, meaning that the same households (and often the same respondents) are interviewed multiple times regarding their COVID-19 vaccination status, willingness to get vaccinated, and related information. It is possible that this leads to a behavioral response in which respondents become more likely to get vaccinated (or report to be vaccinated) as they are repeatedly interviewed over time. Such ‘panel conditioning’ effects have been hypothesized to affect survey data across a wide range of topics but require experimental data for reliable identification (Struminskaya and Bosnjak 2021). Our hypothesis reads as follows.

**Hypothesis 4:** Repeatedly interviewing respondents on the topic of COVID-19 vaccination makes them more likely to get vaccinated or to report to have been vaccinated.

> 10 As data collection for the in-person survey covered a period of multiple months, we conservatively take the administrative coverage rate from the date closest to the end of data collection for our survey. At this point in time, over 99% of data had been collected.

12
# **APPENDIX C: FCS FOOD GROUPS AND WEIGHTS**

**Table C: Food groups and their corresponding weights for Food Consumption Score (FCS)**

||**FOOD ITEMS (examples)**|**Food**<br>**groups**|**Weigh**<br>**t**|**Justification for weight**|
|---|---|---|---|---|
|1|Maize, rice, millet, wheat, bread,<br>sorghum, other cereals, cassava,<br>potatoes, sweet potatoes, and other<br>tubers|Main<br>staples|2|Energy dense, protein content lower<br>and poorer quality than legumes,<br>micronutrients (bound by phytates)|
|2|Legumes, beans, peas, peanuts, nuts|Pulses|3|Energy dense, high amounts of<br>protein but of lower quality than<br>meats, micronutrients (inhibited by<br>phytates),low fat|
|3|Vegetables, leaves|Vegetable<br>s|1|Low energy, low protein, no fat,<br>micronutrients|
|4|Fruits|Fruit|1|Low energy, low protein, no fat,<br>micronutrients|
|5|Beef, goat, poultry, pork, eggs, fish,<br>insects|Meat and<br>fish|4|Highest quality protein, easily<br>absorbable micronutrients (no<br>phytates), energy dense, fat. Even<br>when consumed in small quantities,<br>improvements to the quality of diet<br>are large|
|6|Milk, yogurt, and other dairy|Milk|4|Highest quality protein,<br>micronutrients, vitamin A, energy.<br>However, milk could be consumed<br>only in very small amounts and<br>should then be treated as condiment,<br>and therefore reclassification in such<br>cases is needed.|
|7|Sugar and sugar products, honey|Sugar|0.5|Empty calories. Usually consumed in<br>smallquantities.|
|8|Vegetable oil, fats and butter|Oil|0.5|Energy dense but usually no other<br>micronutrients. Usually consumed in<br>smallquantities.|
|9|Spices, tea, coffee, salt, fish powder,<br>small amounts of milk for tea|Condimen<br>ts|0|These foods are by definition eaten<br>in very small quantities and not<br>considered to have an important<br>impact on overall diet.|

Source: World Food Programme (2008) 40
geneous impact of DTAs on firms with different characteristics is confirmed by 2SLS estimations - see Table 9 columns 2-7.<sup>23</sup> The interaction terms are instrumented by simply interacting the firm indicators _I_ ( _kf > k_<sup> ̄</sup> ) with the IV discussed above. The bottom part of Table 9 shows the first stage results of the 2SLS approach. Our IVs (one for the RTA depth and the other for its interaction with the firm type dummy) are good predictors of the endogenous variables (i.e. significant first stage coefficients). Also, the joint F-stat statistics well above 10 support the absence of a weak instrument problem.

## **6.1 IV validity**

As discussed above, the validity of our instrumental variable is based on the absence of a direct effect of the depth of RTAs signed with third-country on _fij_ -specific exports. In this section we test the robustness of our baseline results to a deviation from the perfect validity of the exclusion restriction (Conley et al., 2012). A degree of deviation from the exclusion restriction can be obtained by regressing the exports of firms _Xfijt_ on our main _DTAijt_ and IV variable. The coefficient associated to the IV represents an approximation of the direct effect of the IV on the outcome variable (i.e. degree of deviation from the exclusion restriction) – van Kippersluis and Rietveld (2018). We obtain a small and not statistically significant direct effect of IV on firm exports - see parameter _ν_ in Table 10. We therefore plug such a degree of deviation from exclusion restriction in the plausible exogeneity test à la Conley et al. (2012), and obtain a lowerand upper-bound coefficients that do not cross the zero. Thus, we can safely argue that the depth of RTAs has an unambiguous positive causal effect on the export of firms in developing countries.<sup>24</sup>

# **7 The dynamic effect of deep RTAs: An event study approach**

We relied so far on a fixed effects approach delivering the average effect of deep RTAs on the exports of firms (i.e. pre- _versus_ post- change in RTA depth). However, deep RTAs may stimulate firms’ exports dynamically and for a limited amount of time. In this section we adopt an event-study approach to visualize the dynamic effect of deep RTAs. Namely, we follow Fajgelbaum et al. (2019), and compare the targeted varieties (i.e. firm-destination combinations

> 23Point estimates from _linear_ 2SLS estimations in Table 9 cannot directly compared with our baseline _non-linear_ PPML estimations.

> 24For computational reasons (i.e. maximum number of covariates allowed by `plausexog` STATA command) we had to reduce the number dummies (fixed effects) included in the estimations. First, we replaced destination-year fixed effects by destination-period fixed effects, with periods containing 6-year each. Second, we restricted the number of destinations by: (i) using top-50 destinations for the all the 31 exporting countries of our sample (panel a in Table 10), (ii) keep only destinations representing at least the 0.5% of total exports of all the 31 exporting countries of our sample.

17
**Table 4. Regression results for misinformation discernment scores.**

||(1)<br>Misinformation<br>Discernment|(2)<br>Misinformation<br>Judgements of<br>Misleading<br>Headline|(3)<br>Misinformation<br>Judgements of Non-<br>Misleading<br>Headline|
|---|---|---|---|
|**_Panel A: Placebo as base group_**||||
|Control|-0.067|-0.054|0.037|
||(0.059)|(0.061)|(0.058)|
||[0.828]|[0.828]|[0.828]|
|Comprehensive Active|0.285***|0.155***|-0.218***|
|Inoculation|(0.057)|(0.058)|(0.056)|
||[0.000]|[0.026]|[0.000]|
|Brief Active Inoculation|0.140**|0.311***|0.084|
||(0.056)|(0.059)|(0.059)|
||[0.035]|[0.000]|[0.258]|
|Passive Inoculation|-0.041|0.037|0.080|
||(0.056)|(0.057)|(0.054)|
||[0.975]|[0.975]|[0.607]|
|Controls|Yes|Yes|Yes|
|Observations|2,851|2,851|2,851|
|R-squared|0.090|0.040|0.069|
|**_Panel B: Passive Inoculation as base group_**||||
|Control|-0.027|-0.091|-0.042|
||(0.058)|(0.060)|(0.057)|
||[0.909]|[0.563]|[0.909]|
|Placebo|0.041|-0.037|-0.080|
||(0.056)|(0.057)|(0.054)|
||[0.975]|[0.975]|[0.607]|
|Comprehensive Active|0.325***|0.119**|-0.297***|
|Inoculation|(0.056)|(0.056)|(0.055)|
||[0.000]|[0.098]|[0.000]|
|Brief Active Inoculation|0.181***|0.274***|0.005|
||(0.055)|(0.057)|(0.058)|
||[0.006]|[0.000]|[0.938]|
|Controls|Yes|Yes|Yes|
|Observations|2,851|2,851|2,851|
|R-squared|0.090|0.040|0.069|

_Note._ OLS estimates of treatment effects. Control variables are age, education, gender, and vaccination status for COVID-19. Robust standard errors are shown in parenthesis, and Westfall-Young stepdown adjusted p-values in brackets (1,000 bootstrap replications). _*** p<_ 0.01 _, ** p<_ 0.05 _, * p<_ 0.1.

21
That is, the mean deviation of height from the reference population will be the same for treatment and control groups within each age-by-sex category, so we are simply comparing the difference in heights.

# **3.4.2 Negative externalities: Effects in households without latrines**

Existing evidence such as the interaction of open defecation with population density in its effect on children’s health (Spears, 2012a, 2013) suggests negative externalities, effects of one household’s open defecation on another’s children. But perhaps these prior studies cannot definitively rule out that the effect is purely due to a household’s use of its own latrine, or that associations between child height and community sanitation averages reflect omitted variables. Our experiment randomized the intervention at the community level (Miguel and Kremer, 2004). Therefore, an effect on the heights of children whose households did not use latrines, even at the endline after the program, would be evidence of spillovers of sanitation onto other local households.

Indeed, even after the program most children lived in households without latrines. Restricting the sample to this subset<sup>21</sup> (74.6 percent of the Ahmednagar sample) and estimating the simple difference-in-differences in panel B of table 3 finds that the program caused even children in this group to be 0.42 standard deviations taller (standard error = 0.19, _n_ = 2 _,_ 562). Although this point estimate appears slightly larger than the estimate for the full sample, in fact it is not statistically detectably different. When the full sample is used with a fully-interacted triple difference, the effect of the program on households with a latrine at endline is no different than the effect on households without a latrine at endline: the estimate of the triple difference (treatment _×_ after _×_ latrine at endline) is 0.001 with a standard error of 0.20 and a _t_ -statistic of 0.01.

Therefore, this community-level experiment offers evidence of spillover effects of open

> 21The subset is children who live in households who did not have a latrine at endline; this excludes children who live in households who did not have a latrine at baseline but who acquired one by endline.

21
# **2. Recent Zimbabwean labor market studies**

Until recently, we had little knowledge of which factors influence employment and wages in the dual labor market in Zimbabwe. Most recently, Mazumdar (1995) and Ann Velenchik (1996a, 1996b) apply a sample of relatively homogenous groups of manufacturing workers in analyzing whether labor markets are competitive, employer size effects, and rent sharing.<sup>2</sup> Furthermore, Mazumdar (1994) estimated the marginal returns to male's education for four African countries. Earnings functions were estimated with variables capturing experience, dummy for permanent and temporary positions, and whether the worker had been an apprentice. The estimated incremental coefficients of the log of earnings for primary, secondary and university education are reported to be 0.5, 0.66 and 1.86. These high returns reflect larger occupational differentials observed in Zimbabwe. The coefficients are around three times higher in Zimbabwe ancl Zambia than in Cameroon and Kenya. However, the estimates may be biased or overestimated if education is correlated with firm characteristics3, indicating that workers in larger firms may be sharing rents from a protected market. Earnings functions with other control variables (industry dummies, ownership firm dummies, size of establishment dummies) indicate that earnings increase with size of establishment. The elasticity of value added with respect to firm size is statistically significant and positive (0.1).

Additionally, Mazumdar (1995) classifies size groups of enterprises. The findings show that differences in earnings are partly explained by enterprise size in Cameroon, Ghana, Kenya, Zambia, and Zimbabwe. The size of enterprise dummies is found to be important

> 2 Other studies related to the labor market in Zimbabwe are on: rural labor markets (Vali (1995) and Adams (1991a, 1991b)); earnings and entries in micro enterprises and self employment (Steel and Webster (1992)); pay and employment reforms (Lindauer and Nunberg (1994)); job security regulations and policies (Fallon and Lucas (1991, 1993)), the macro wage curve (Verner; 1998), labor productivity in manufacturing (Pack (1993)), see Velenchik (1996a, b) and Addison (1996).

- 3 The return to education after introducing other control variables are not reported in the paper.

4
growth of skills demand.<sup>21</sup> Moreover, the model can easily be updated with new data to deliver timely analysis of emerging labor market dynamics.

Prospective job seekers and training institutions can use these forecasts to tailor their education and training curricula to reflect trends in the demand for specific skills. Similarly, policy makers can identify skills gaps and make demand-driven policy decisions. These gaps may reflect the types of skills demanded or differences in demand conditions across geographical locations. Real-time data can also provide an early warning of disruptions in the labor market. For example, slowing wage growth during 2015 may pose a sign of weakening trend in workforce skills demand.

# **5.5. Experimental Studies**

Firms often pilot new business strategies to test their effectiveness. Horton and Tambe (2015) found that experimentation in digital markets is often simple and inexpensive, since interactions between users are computer-mediated and interfaces are relatively easy to modify. Horton (2016) conducted an experiment in which oDesk<sup>22</sup> introduced algorithmic recommendations for employers about which workers to hire. He found that the employers with technical job vacancies that received recruiting recommendations had a 20 percent higher fill rate compared to the control group, and there was no evidence that the treatment crowded out the hiring of non-recommended candidates. Gee (2015) collaborated with LinkedIn on an experiment that tested whether access to additional information affects job application behavior and found that additional information increased completed job applications by 1.9 percent overall, and by as much as 6 percent among female applicants.

# **_Application of the Analysis to Babajob Data_**

Babajob is currently considering an experimental study designed to improve employment transitions and promote better matches between job seekers and employers.<sup>23</sup> Frictional unemployment is the time that workers spend between jobs, which can be extended if inadequate information constrains employers’ capability to select good candidates. This is particularly the case for subjective employee characteristics and skills that are not easily observed. Information on prospective employees is costly to acquire (Stigler 1962), and under certain circumstances imperfect information can create suboptimal equilibria (Spence, 1974; Fang and Moro, 2011). Facilitating the flow of information between workers and employers can

> 21 Forecasting changes in workforce skills demand and in the supply of workers would be of great interest to both employers and job seekers. However, due to absence of external data to validate against at the time of data analysis, such an analysis is not conducted.

> 22 oDesk, which is now renamed as Upwork, is a job portal for freelancers.

> 23 This study is being funded by the Strategic Research Program and the Jobs Umbrella Trust Fund.

23
# **_4.2 Social Cohesion_**

Compared to the use of public spaces, the impacts on social cohesion are mixed. The impacts on trust toward outgroups, perceptions toward outgroups, and willingness to interact with outgroups are summarized in Tables 3-5, respectively, both for the full sample as well as for selected subgroups.

First, Table 3 reports that the intervention did not have a statistically significant effect on the “trust toward outgroups index” (Column 1-2) and its components, i.e. trust toward different ethnic groups and the opposite sex (Columns 3-4 and 5-6, respectively). The only exception is the urban design and diversity intervention, which had a negative impact of 0.06 standard deviation on trust toward people from the opposite sex (Columns 5 and 6). The F-tests indicate that the treatment arms are not statistically different from each other.

Table 3. Treatment Effects on Trust Toward Outgroups

|VARIABLES|(1)<br>Index Trust<br>(z-sc|(2)<br>Outgroups<br>ore)|(3)<br>Trust Dif<br>Gr|(4)<br>ferent Ethnic<br>oup (=1)|(5)<br>Trust Op|(6)<br>posite Gender<br>(=1)|
|---|---|---|---|---|---|---|
|[T1] Urban Design|-0.07|-0.06|0.00|0.01|-0.04|-0.04|
||(0.064)|(0.063)|(0.032)|(0.032)|(0.032)|(0.032)|
|[T2] Diversity|-0.06|-0.06|0.02|0.02|-0.05|-0.04|
||(0.063)|(0.063)|(0.032)|(0.032)|(0.032)|(0.032)|
|[T3] Urban Design + Diversity|-0.03|-0.05|0.01|0.01|-0.06*|-0.06**|
||(0.061)|(0.060)|(0.031)|(0.031)|(0.031)|(0.031)|
|Observations|1,924|1,924|1,924|1,924|1,924|1,924|
|Control Mean|0.00|0.00|0.60|0.60|0.49|0.49|
|Control Mean (No z-score)|0.55|0.55|||||
|F-test T1=T2 (p-value)|0.90|0.91|0.54|0.59|0.90|0.89|
|F-test T1=T3 (p-value)|0.54|0.81|0.69|0.80|0.63|0.37|
|F-test T2=T3 (p-value)|0.63|0.91|0.82|0.77|0.72|0.45|
|Selected Controls||X||X||X|

Notes: Specifications in odd-numbered columns include experiment location fixed effects while even-numbered columns include all the covariates selected. Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1.

There is, on the other hand, significant heterogeneity in the impacts observed on different subgroups. T1 and T3 resulted in lower trust among the most vulnerable groups, including women and those with lower incomes, whereas T2 had no statistically significant impact. Columns 1-2 in Table 3.A shows that exposure to urban design elements is associated with a decrease of 0.18 standard deviation in the “trust toward outgroups index” among those with monthly incomes below the median and the difference is statistically different at 5 percent. Similarly, Columns 4-5 show that exposure to urban design 16