rafmacalaba/datause-encoder-data huggingface dataset:
employ Bayesian techniques for their estimation, which requires multiple drawing from posterior distributions and consequently, more computing time. Second, MI studies often impute missing data within the same survey where usually less than half of the data are missing, rather than imputing consumption for a whole new survey (census) round. Finally, MI studies, unsurprisingly, do not use economic theory for model selection (as in Dang _et al._ , 2017).<sup>24</sup> A recent champion of the application of MI methods to poverty is the SWIFT method (Survey of Well-being via Instant and Frequent Tracking), which is developed by Yoshida _et al._ (2015). Besides sharing the features of MI methods discussed above, perhaps SWIFT’s most important contribution is its proposal to rapidly collect data on a key and reduced set of variables that can be used for faster and less expensive poverty imputation.<sup>25</sup> However, it can be useful to note some potential limitations of SWIFT. First, the (distributions of the) parameters �� , ��, and �� are likely to change more over longer time periods. Consequently, these parameters would need to be updated more frequently for better predictions, which would effectively require the implementation of the full household consumption survey rather than just a reduced set of variables. Second, a full household survey (with or without consumption data) may likely offer more precision with reproducing the whole income distribution

> 24 Douidich _et al._ (2016) offer an early application of MI methods to poverty imputation. See also Davey, Shanahan, and Schafer (2001) and Jenkins _et al._ (2011) for studies that apply MI techniques to economic issues. More discussion on the differences between the two literatures and references to earlier imputation studies are provided in Dang _et al_ . (2017).

> 25 Due to various post-survey data quality control procedures such as data entry, cleaning and checking, household survey data are usually unavailable for use from until half a year to longer with most household surveys. But this situation may improve with the increased use of tablet-based (or hand-held devices-based) data collection. Another notable feature of SWIFT is its statistical approach to model selection (see, e.g., James _et al._ (2013)), and building estimating models (say, by using stepwise regression to screen out variables with low p-values, rather than selecting these variables based on economic theory on household consumption).

24
**Table 3: Sensitivity Analysis of the Impact of Parent’s Education on Children’s Education [AET (2005)]**

|**Sl**|**Y**||**Correlation**|**between the err**|**or terms in the t**|**riangular model**||
|---|---|---|---|---|---|---|---|
|**ampe**|**ear**|||||||
|||0<br>|0.05<br>|0.10<br>|0.15<br>|0.20<br>|0.25<br>|
|||**0.16**|**0.14**|**0.11**|**0.08**|0.04|0.001|
||1988|(5.89)***|(4.55)***|(3.51)***|(2.46)**|(1.40)|(0.32)|
|Daughters||||||||
||2002|**0.12**|**0.10**|**0.08**|**0.05**|0.02|‐0.002|
|||(5.21)***|(4.61)***|(3.45)***|(2.28)**|(1.09)|(‐0.13)|
||1988|**0.09**|0.05|0.02|‐0.02|‐0.05|‐0.08|
|Sons||(3.44)***|(1.49)|(0.51)|(‐0.48)|(‐1.48)|(‐2.49)|
||2002|**0.14**|**0.12**|**0.10**|**0.08**|**0.05**|0.02|
|Notes||(8.33)***|(7.61)***|(6.07)***|(4.51)***|(2.93)***|(1.31)|

1. t statistics in parentheses;

2. Adult children’s education dummy: 1=more than primary school for the 1988 sample; 3. Adult children’s education dummy: 1=more than middle school for the 2002 sample; 4. Parent’s education dummy, 1= if at least one parent’s education is more than primary school; 5. “*” significant at 10 percent; “**” significant a 5 percent; “***” significant at 1 percent; 6. Children>=1967; 7. The estimation uses STATA routine of AET (2005); 8. The samples are: 2057 (1988, daughters), 1884 (1988, sons), 2264 (2002, daughters), 3616 (2002, sons).

53
respond to contemporaneous shocks.

**Figure 2: Disbursement rates over the life of a loan**

A. Average disbursement rate B. Average disbursement rate, by creditor type <!-- Start of picture text --> Share of loan disbursed Share of loan disbursed<br>0.16 0.18<br>0.14 0.16 Multilateral loans<br>0.12 0.14 Bilateral loans<br>0.12<br>0.10<br>0.10<br>0.08<br>0.08<br>0.06<br>0.06<br>0.04 0.04<br>0.02 0.02<br>0.00 0.00<br>t t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10 t t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10<br><!-- End of picture text --> _Source:_ Authors’ calculations.

_Note:_ Based on average disbursement rates for 73,994 loans (32,965 for multilateral and 41,029 for bilateral) in year of loan and subsequent years.

Since the predicted disbursements are in current US$, to get our instrumental variable relative to GDP, we convert it to local currency using exchange rates from the World Bank’s World Development Indicators, then deflate using the GDP deflator and again divide it by lagged real GDP. Before running regressions, we remove the 1<sup>st</sup> and 99<sup>th</sup> percentiles of each variable. After constructing our variables, we have observations between 1980 and 2019 for our regressions. Table 1 provides summary statistics of the main variables of interest.

**Table 1: Summary statistics**

||Mean|Median|Standard<br>deviation|Minimum|Maximum|Interquartile<br>range|Skewness|Kurtosis|Number of<br>observations|
|---|---|---|---|---|---|---|---|---|---|
|Change in private||||||||||
|investment to|0.74|0.63|4.11|-34.72|62.77|2.78|1.44|38.02|3737|
|GDP||||||||||
|Change in public||||||||||
|investment to|0.27|0.19|2.43|-42.52|29.81|1.41|-0.85|50.31|3737|
|GDP||||||||||
|Predicted loan||||||||||
|disbursements to|0.07|0.00|2.30|-33.18|46.20|0.39|6.40|213.70|3774|
|GDP||||||||||
|Predicted multi.||||||||||
|loan<br>disbursements to|0.05|0.00|0.45|-7.68|6.54|0.22|1.41|58.36|3756|
|GDP||||||||||

_Source:_ Authors’ calculations.

_Note:_ Multi. = multilateral. All variables are as a ratio to GDP. “Change” refers to the annual change as described in equation (3.1).

9
and self-administered instruments through phone messages.

Finally, existing evidence documents heterogeneity in data quality obtained from different survey modes by some respondent characteristics (Bardasi et al., 2011; Beegle et al., 2012; Gigliotti and Dietsch, 2014; LeFevre et al., 2020).<sup>8</sup> For example, Bardasi et al. (2011) document that responses by proxy of employment rather than self-report have differential effects on employment rates by gender in Tanzania. Similarly, Beegle et al. (2012) experimentally test alternative survey designs in face-to-face surveys that measure household consumption. The authors find that under-reporting is particularly apparent for illiterate households and for urban respondents. We contribute to this literature by experimentally testing survey mode – instead of survey design – and variation in response rate by respondents’ characteristics that are standard in the literature – gender and age – and by others that are novel and relevant due to the policy implications of the findings – phone availability and access of electronic-transfer technologies.

The remainder of the paper is organized as follows: Section 2 describes the research design, including details on the recruitment process, and experimental design. Section 3 presents the data collected and our implementation process. In section 4, we describe our main empirical approach. Section 5 summarizes the main results obtained from specifications presented in section 4, and section 6 describes our robustness checks. Finally, in section 7 we briefly conclude.

# **2 Research Design**

We embedded a survey-mode experiment in a midline survey of the impact evaluation of a positive-parenting and stress-management intervention for caregivers in El Salvador (Amaral et al., 2021). The midline survey sought to assess if caregivers who received the intervention were incorporating its concepts. We took the opportunity of the survey to conduct the survey-mode experiment and generate evidence on the most convenient method (CATI vs. self-completion) to collect data on sensitive topics. We used the results of this

> 8See De Weerdt et al. (2020) for a literature review on how survey designs can influence socioeconomic data collection in developing countries and, as a consequence, how this also affects the data and results of subsequent analyses.

7
# **Supplementary Appendix**

## **Supplementary Appendix S1: Additional Tables**

## **Table S1.1: Distribution of the total number of days worked in the survey**

|Number of days<br>worked|Frequency|Percentage|Cumulated|
|---|---|---|---|
|1|3|4.6|4.6|
|2|1|1.5|6.1|
|4|2|3|9.1|
|5|1|1.5|10.6|
|6|2|3|13.6|
|7|1|1.5|15.2|
|8|4|6.1|21.2|
|10|1|1.5|22.7|
|11|3|4.6|27.3|
|12|2|3|30.3|
|13|1|1.5|31.8|
|14|1|1.5|33.3|
|19|1|1.5|34.9|
|21|1|1.5|36.4|
|22|2|3|39.4|
|23|9|13.6|53|
|24|7|10.6|63.6|
|25|8|12.1|75.8|
|26|3|4.6|80.3|
|27|3|4.6|84.9|
|28|1|1.5|86.4|
|29|2|3|89.4|
|37|1|1.5|90.9|
|39|2|3|93.9|
|40|1|1.5|95.5|
|41|1|1.5|97|
|43|2|3|100|
|Total|66|100||

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : The table shows the number of work days as enumerator during the whole survey period.

33
Similar results are evident in the relationship between measles vaccination, household area, and child mortality, a primary indicator of socioeconomic status among rural Bangladeshi families (Figure 5). While the presence of a child replacement effect would argue in favor of a much weaker effect of vaccination upbn child survival among children from the<sup>poorest</sup> households (area of <200 sq ft), no such association is<sup>evident.</sup> The results indicate that children of all socioeconomic strata benefit from measles vaccination in terms of lower mortality risks. Although not as pronounced as in the case of sex, children in the poorest households benefit somewhat more from vaccination in terms of absolute reductions in mortality.

In conclusion, the evidence considered provides no direct or indirect support for the existence of a child replacement effect, which would diminish the contribution of measles vaccination to improved child survival. The available evidence instead indicates that the effect of measles vaccination is sustained over time, and that ti a greatest benefits from vaccination appear to be found among the most vulnerable children. As such, measles vaccination may reduce not only overall mortality levels but also inequalities between groups in their survival chances.

# Potential Selection Bias

A primary issue of concern in all assessments of measles vaccination impact is that vaccinated children represent a spegial group of children who may have better health and survival prospects, even in the absence of measles vaccination. If this were the case, then the lower mortality risks observed may be due in large part to the special nature of vaccinated children, rather than to measles vaccination per se. The studies of measles vaccination impact have adopted a number of different approaches to dealing with the issue of potential selection bias.

The issue of pctential selection bias was not directly addressed in the ;asongo study in Zaire. However, the authors presented survival data from the area where measles vaccination had been offered for both the 255 eligible children who were vaccinated as well as the 51 eligible children who were not; many of these latter children may have been self-selected for poorer survival outcomes. Analysis of the survival probabilities of all 306 children together takes into account any possible selection bias between vaccinated and unvaccinated children. The 7-35 month mortality rate for this group was 48.3 per 1000, an increase from the rate of 39.1 per 1000 for vaccinated children alone. However, this still translates into a reduction in mortality relative to the other three control groups of between 30 to 49 percent, indicating a sizeable impact of vaccination upon 13
members who normally live in the household, even if they are temporarily studying or working elsewhere. An additional question asks whether any household members have gone to live in another country in the past five years. The survey asks migrants how long they have been living in their current location, the year of their last trip to the U.S., and the number of times they have been to the U.S., but does not ask the year of their first U.S. trip. We therefore classify individuals as having migrated prior to 1 January 1994 if their last trip was before this date, or if they have made at least two trips to the U.S. and their last trip was during 1994, or if they have made three (four) (five) trips to the U.S. and their last trip was during 1995 (1996) (1997). In addition to a binary classification into migrant and non-migrant households, we also construct household migration prevalence ratios, defined as the proportion of individuals aged 15 and over in the household who had been to the U.S. prior to 1 January 1994.

Table 1 presents summary statistics for the sample of households which had a child born since the start of 1994, along with t-tests which test for differences in means between migrant and nonmigrant households. Nineteen percent of households are seen to have at least one migrant, with nine percent of all adults in the sample having migrated to the United States at least once before 1994. The infant mortality rate in our sample is 23.7 per 1000 live births, which is close to the adjusted rate for all of Mexico in 1995 of 25.9 (PAHO, 1998).<sup>7</sup> According to the international standard whereby low birthweight is classified as any weight less than 2.5 Kilograms, 7.5 percent of births in our sample are estimated to be underweight. This compares with a 1997 estimate of 8 percent for Mexico as a whole (UNICEF, 1997). Our data therefore appear to be well representative of general child health conditions in Mexico.

An initial comparison of migrant to non-migrant households reveals differences in child health outcomes, health inputs, and household characteristics. Children in migrant households are less likely to be born underweight, have higher average birthweights, and were more likely to have been delivered by a doctor. Infant mortality rates are higher in non-migrant households, but this difference is not statistically significant. However, children in non-migrant households appear to be slightly more likely to be breastfed and to have visited a doctor during their first year of life. 7 Since our sample excludes urban areas, one would expect a higher infant mortality for our sample than for all of Mexico. The unadjusted national infant mortality rate was 17.5 per 1000 in 1995, which perhaps is more directly comparable to our rates (PAHO, 1998).

- 6 -
and Portugal, much of Spain, and some parts of the United Kingdom. Among our sample of countries, all regions are included, with the exception of Prague (Czech Republic), Bratislava (Slovakia) and the following Hungarian regions: Eszak-Alf ̈old,<sup> ́</sup> D ́el-Dun ́ant ́ul, ́Eszak-Magyarorsz ́ag, K ̈ozp-Dun ́ant ́ul, Nyugat-Dun ́ant ́ul.

We use data from the 2008 round of the European Union’s Survey on Income and Living Conditions (EU-SILC). As income variable, we use the household equivalent disposable income using the modified OECD scale. We then deflate incomes using the harmonized consumer price indexes provided by Eurostat (2011), in order to have real and comparable incomes. As poverty line _z_ , we use 11 USD _per day_ which is the threshold set by the World Bank for these set of countries. Since income data are expressed in euros, we use the exchange rate euro-dollar (1 _._ 43 euro per 1 dollar in 2008). Following Medeiros (2006) and Eisenhauer (2011), we define the richness line as a function of the poverty line. In particular, considering that the absolute poverty line _z_ may be interpreted as 60% of the median income _ym_ of an hypothetical income distribution, we set the richness line at 200% of _ym_ (see Peichl, Schaefer, and Scheicher, 2010).

Table 2 summarizes the values of the different indexes ( _P_<sup>0</sup> , _P_<sup>1</sup> , _IL_<sup>_prop_</sup> , _IL_<sup>_flatA_</sup> , _IL_<sup>_progA_</sup> , _IL_<sup>_progB_</sup> , _IL_<sup>_progC_</sup> ) for the sample of European countries. Recall that _P_<sup>0</sup> stands for the percentage of poor individuals in the population (Headcount index) and _P_<sup>1</sup> is the poverty gap index.

_IL_<sup>_prop_</sup> can be interpreted as the average tax rate to be levied on non-poor individuals to collect enough revenue to rise all poor individuals to the poverty line. For Romania we see that the index is greater than 1 (3.62). It means that, to defeat poverty, the income of non-poor individuals should almost quadruplicate. Estonia and Hungaria have the same proportion of poor individuals and almost the same poverty gap index. Note however that in Estonia it is sufficient a proportional tax rate of 5 _._ 5% levied on the non-poor to solve poverty, while in Hungaria the tax rate should be fixed at 8 _._ 8%.

_IL_<sup>_flatA_</sup> is the flat tax _f_ to be levied on all non-poor individuals who can bear this cost without crossing the poverty line. For Romania no flat-tax can be collected without having non-poor individuals crossing the poverty line.

21
and test for heterogeneity for a variety of firm characteristics. For each firm characteristic, a firm’s categorization is determined based on the first year the firm appears in the business registry. The firm characteristics we consider are the size of the firm, the foreign ownership status of the firm, the productivity of the firm, and the new importer status of the firm. For the size of the firm, most Serbian firms are small, i.e., have less than 50 employees, so we test the difference between small and medium/large (more than 50 employees) firms. In terms of the foreign ownership status, we use a 10% foreign ownership stake as the cutoff for determining whether a firm is domestic of foreign. In Serbia, most firms are classified as domestic by this measure. For firm productivity, we calculate the revenue per worker for each firm as well as the median value of this measure in each year and categorize firms relative to the median. Thus, a firm is considered low productivity if its revenue per worker is below the median for Serbian firms. Finally, we define a firm as a new importer if it has no previous experience importing from any market. In the analysis section we test the heterogeneity in the effects of firm learning for each of these characteristics in separate regressions as well as in a single combined specification. For each of the characteristics described above, the share of total entries by Serbian firms into new foreign markets over the entire sample period is summarized in Table 3. As stated previously, most entries into new markets are for countries that are part of the EU. Additionally, most entries are observed in small firms, which also compose the majority of firms overall. The same is true for domestic firms. Firms with productivity above the median represent a disproportionate share of entries. Lastly, firms that have previous importing experience (from any foreign market) make up most of the entries into new markets in the data.

# **3.4 Data**

Using data on 38,907 unique Serbian firms from the Serbian BRA we construct an unbalanced panel of 31,482 firms to include in the final analysis. The data includes balance sheet information of firms, for example, their revenue, capital, employment, age, and ownership.

13
<!-- Start of picture text --> 20,000-<br>18,000 o<br>16,000i<br>14,000,<br>12,000- ,<br>I10,000- - 1'_ -<br>8,000- ; , > X !<br>6,000- ,'-<br>4,000- 1|1 ' * j<br>* 2,000- , .<br>1997 1998 1999 2000 2001<br><!-- End of picture text -->

Figure 13: Estimated Internet Users in Malawi. Source: International Telecommunication Union (2001).

In addition to allowing entry into cellular telephony, the 1998 Communications Sector Policy and Act also envisaged the liberalization of other sectors. In particular, although e - the 1998 Communications Sector Policy required Internet Service Providers to obtain licenses from MACRA before starting _operations, there were no restrictions on the number of licenses that MACRA could issue. By mid-2001, MACRA had approved about 15 licenses and there were 10 operational ISPs (Ministry of Finance and Economic Development, 2001). The increase in the number of licenses coincided with a massive increase in the number of Internet users in Malawi, from about 500 at the end of 1997 to 20,000 by the end of 2001 (see Figure 13).47 In addition to expanding competition in Internet and cellular services, and despite the fact that the Communications Sector Policy Statement did not require full privatization, the Government quickly initiated plans to privatize the telecommunications operations of MPTC. The first step towards doing so was splitting MPTC into two companies, Malawi Telecom Limited (MTL) and Malawi Postal Corporation (MPC). Malawi Telecom Ltd. (MTL) was corporatized on May 31, 2000. Under the 1998 Communications Sector Policy, it was given five years exclusivity in fixed-line and international services.

> **47** According to Ministry of Finance and Economic Development (2001), the 10 operational ISPs had 4,400 subscribers by mid-2001. However, it is important to note that several users could use each individual subscription. For example, Ministry of Finance and Economic Development (2001) notes that several Intranets, including the Malawi Government Wide Area Network, MTL, and the Limbe Leaf Tobacco Company had only a single subscription. At that time, the Malawi Govemment Wide Area Network alone had 450 active users.

39
Capital Index (HCI) data set developed by the World Bank and its measure of Learning-Adjusted Years of Schooling (LAYS). The LAYS measure is made up of two components:

- Expected years of schooling. This measures the number of years of school a child born today can expect to obtain by age 18. It is based on age-specific enrollment rates between ages 4 and 17 and has a maximum value of 14.

- Harmonized measures of learning from global and regional learning assessments (Altinok, Angrist, and Patrinos 2018; Patrinos and Angrist 2018). Relative scores for learning are calculated for each country by dividing its harmonized score by 625, which corresponds to the “advanced” benchmark on the TIMSS and PIRLS learning assessments.

These two measures are multiplied together to arrive at the expected _learning-adjusted years of schooling_ (LAYS) we use as our measure of education outcomes (Filmer et al. 2018; Kraay 2018). For example, if a country had 10 years of expected years of schooling and an average test score of 500 (80 percent of 625), then its LAYS would be 8 years.

A crucial benefit of the LAYS measure is that it captures changes in both educational attainment and average learning outcomes. If a country maintains its average learning levels while increasing enrollment rates, our measure will increase since the average levels of learning of the population are now higher. If average learning levels increase because some children drop out of school and enrollments decrease as a result, the level of the LAYS will depend on the magnitude of those changes. Its limitations are mainly related to a lack of data in some countries.

We propose to measure spending as _total expenditure per child_ calculated as total spending in primary and secondary education divided by the total number of school-aged children in the country. This captures how much a country is spending on the population that it needs to serve (not just the population that it is currently serving). Thus, if a country is spending a lot on its students, but enrollment rates are very low, the country would effectively be spending too little on the population that it should be serving. Because it measures total spending in U.S. dollars (constant PPP-adjusted dollars), this measure is strongly correlated with per capita GDP.

26