rafmacalaba/datause-encoder-data huggingface dataset:
standards increases the fixed costs of exporting, but once firms enter the market, standards do not impact their level of exports.<sup>13</sup> To our knowledge only three studies examine how standards affect firms’ trade patterns. The first study by Chen et al. (2008) uses cross-sectional data from a World Bank survey of firms covering compliance with technical barriers to trade and firm participation in export markets and show that different types of standards exhibit different links with intensive and extensive margins of exports. Quality standards and labeling requirements are positively correlated with firms’ average export volume across destinations and products but also with their export scope (number of destinations and products) while certification procedures are linked to a decline in export scope. A clear limitation of that study is its use of subjective survey responses by firms of whether their exports have been impacted by different types of standards. The second study by Reyes (2012) shows that the harmonization of EU electronics regulations with international standards (whose compliance is not compulsory) led to the entry of new US exporters of electronics products into the EU. Finally, the third study by Fontagné et al. (2015) examines the impact of SPS concerns raised in the WTO Committee on SPS on the export behavior of French firms using customs data. SPS concerns have a negative impact on firms’ probability of exporting and export value and lead to increases in their export prices. They also find a heterogeneous effect across firms, with the negative effect of SPS concerns being lower for larger firms exporting to multiple destinations. While very informative, the key limitation of their study is that relying on SPS concerns as a measure of importer restrictiveness raises potential endogeneity concerns, as only countries whose exports are depressed by an importer’s SPS are likely to raise a concern at the WTO and thus we expect greater SPS concerns to be linked through this reverse causality channel to a more negative impact on exports. The authors, however, argue that their data are preferable to the use of WTO notifications or traditional sources of information on the existence of a regulation.

# **3. Data**

> 13 All studies mentioned focus on mandatory technical regulations—as our MRLs are—but other studies focus on voluntary technical regulations. Czubala et al. (2009) show an inhibiting effect of voluntary EU standards on African exports of textiles, clothing, and footwear (except for those standards that are internationally harmonized) whereas Shepherd and Wilson (2013) show that voluntary standards in EU food and agriculture markets are trade-inhibiting for all countries and for raw or lightly processed goods, but internationally harmonized EU standards have much weaker trade effects, and in some cases are even tradepromoting.

7
**Table 1. Cost of a basic-needs food basket (current Nu/person/month)**

||2017<br>prices|2022<br>prices|
|---|---|---|
|Basket defined on 2017 consumption pattern|1,431|2,349|
|Basket defined on 2022 consumptionpattern|2,035|2,455|

Notes: The reference group for both baskets is households in the first decile of per capita household expenditures; we select items that make 80 percent of total food expenditure (this amounts to 45 different items for 2017 and 53 items for 2022); average quantities are rescaled so that each basket yields 2,200 kcal/person/day.

f f The index 2017 can be used to project food poverty lines back in time, from 2022 to 2017, and therefore to identify the poverty trend. To preserve consistency with the 2022 CCFFCC official poverty estimates, we use F as an anchor for the projections. Accordingly, the 2017 food poverty line becomes: FF<sup>2022</sup> F =<sup>F</sup> 2017 f f<sup>FF2022</sup> FF<sup>2017</sup> CCFFCC The upper-bound CBN poverty line (UBPL) is defined as: U F = F Following the same procedure, we can estimate<sup>2022</sup> FF<sup>2022</sup> U+ NNFFNNF<sup>20172022</sup> .

Table 2 shows the estimates for both food (FPL) and total (UBPL) poverty line values.

**Table 2. Comparable poverty lines (current Nu/person/month)**

||**2017**|**2022**|
|---|---|---|
|FPL|1,737|2,852|
|UBPL|3,448|5,786|

Note: The official 2022 FPL was projected back to 2017 by using the food CPI estimated based on the 2017 basic-needs basket.

7
- [34] Staiger, R., and Sykes, A. (2011). “International Trade, National Treatment, and Domestic Regulation,”Journal of Legal Studies 40: 149-203.

- [35] Swenson, D. (2001). “Tax reforms and evidence of transfer pricing,”National Tax Journal 54 (1), 7–25.

- [36] UNCTAD (2015). International Classi...cation of Non-Tari¤ Measures. United Nations publication.

- [37] UNCTAD, and World Bank (2018). The unseen impact of non-tari¤ measures: insights from a new database.

- [38] World Integrated Trade Solutions (WITS)-Users Manual (2011).

38
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# **4.3 The sample from 1990:01 to 1998:04**

Since the recursive cointegration analysis from 1965 to 1998 indicates at least one cointegration relationship from 1990 onwards, we focus in this section on the period from 1990:Ql to 1998:Q4.

When re-examining our model specification, we find that a lag length of 2 is now sufficient to produce random errors. The model mis-specification tests are presented in Table A4 in the appendix. The X ,test indicates one significant cointegrating vector.

**Table 4**

|Null|Alternative|Lag: 2|95%|90%|
|---|---|---|---|---|
|Hypothesis|Hypothesis|With Constant|Critical Value|Critical Value|
|**X**tracetest<br>r=O|r>O|**41.81**|35.10|31.88|
|rsI|r> 1|17.38|20.17|17.79|
|rs2|r>2|2.89|9.10|7.50|

Rejection at the 5% level of significance _Source:_ Authors' calculations.

Normalising the cointegration vector on the **3rd** element, yields the following estimates for ,B (Table 5) and a (Table 6):

**Table 5**

|_Agr_|1.000|
|---|---|
|_Ind_|0.077|
|_Ser_|-0.722|
|_Constant_|-2.588|

_Source:_ Authors' calculations.

**Table 6**

||a|t-statistics|
|---|---|---|
|AAgr|-0.176|-3.997|
|<br>AInd|-0.182|-2.723|
|ASer|-0.138|-3.374|

_Note:_ A indicates a variable in first differences. _Source:_ Authors' calculations.

The column of,f is the cointegrating parameter vector or, in other words,8 spans the cointegration space. The coefficients of a can be interpreted as adjustment coefficients measuring the relative importance of a deviation from equilibrium on a given 12
|P107981|2008|2013|4/8/2013|12 Closed|Maldives|
|---|---|---|---|---|---|
|P107981|2008|2014|11/1/2013|13 Closed|Maldives|
|P107981|2008|2020|12/7/2019|14 Closed|Maldives|
|P107992|2009|2009||0 Closed|Montenegro|
|P107992|2009|2010|7/23/2009|1 Closed|Montenegro|
|P107992|2009|2010|12/18/2009|2 Closed|Montenegro|
|P107992|2009|2010|6/30/2010|3 Closed|Montenegro|
|P107992|2009|2011|10/31/2010|4 Closed|Montenegro|
|P107992|2009|2011|3/9/2011|5 Closed|Montenegro|
|P107992|2009|2012|9/17/2011|6 Closed|Montenegro|
|P107992|2009|2012|3/23/2012|7 Closed|Montenegro|
|P107992|2009|2012|6/30/2012|8 Closed|Montenegro|
|P107992|2009|2013|11/30/2012|9 Closed|Montenegro|
|P107992|2009|2013|6/18/2013|10 Closed|Montenegro|
|P107992|2009|2014|1/15/2014|11 Closed|Montenegro|
|P107992|2009|2014|6/25/2014|12 Closed|Montenegro|
|P107992|2009|2015|3/10/2015|13 Closed|Montenegro|
|P107992|2009|2016|9/15/2015|14 Closed|Montenegro|
|P107992|2009|2016|4/7/2016|15 Closed|Montenegro|
|P107992|2009|2017|10/3/2016|16 Closed|Montenegro|
|P107992|2009|2017|4/13/2017|17 Closed|Montenegro|
|P107992|2009|2018|11/15/2017|18 Closed|Montenegro|
|P107992|2009|2021|7/14/2020|19 Closed|Montenegro|
|P107998|2009|2009|6/21/2009|1 Closed|Bosnia and Herzegovin|
|P107998|2009|2010|12/23/2009|2 Closed|Bosnia and Herzegovin|
|P107998|2009|2010|3/16/2010|3 Closed|Bosnia and Herzegovin|
|P107998|2009|2010|6/30/2010|4 Closed|Bosnia and Herzegovin|
|P107998|2009|2011|10/31/2010|5 Closed|Bosnia and Herzegovin|
|P107998|2009|2011|5/11/2011|6 Closed|Bosnia and Herzegovin|
|P107998|2009|2012|1/1/2012|7 Closed|Bosnia and Herzegovin|
|P107998|2009|2013|8/26/2012|8 Closed|Bosnia and Herzegovin|
|P107998|2009|2013|12/27/2012|9 Closed|Bosnia and Herzegovin|
|P107998|2009|2014|12/8/2013|10 Closed|Bosnia and Herzegovin|
|P107998|2009|2014|6/24/2014|11 Closed|Bosnia and Herzegovin|
|P107998|2009|2015|1/13/2015|12 Closed|Bosnia and Herzegovin|
|P107998|2009|2016|10/16/2015|13 Closed|Bosnia and Herzegovin|
|P107998|2009|2016|6/14/2016|14 Closed|Bosnia and Herzegovin|
|P107998|2009|2017|10/11/2016|15 Closed|Bosnia and Herzegovin|
|P107998|2009|2017|3/17/2017|16 Closed|Bosnia and Herzegovin|
|P107998|2009|2018|9/7/2017|17 Closed|Bosnia and Herzegovin|
|P107998|2009|2018|11/1/2017|18 Closed|Bosnia and Herzegovin|
|P108005|2010|2010|11/30/2009|1 Closed|Serbia|
|P108005|2010|2010|5/3/2010|2 Closed|Serbia|
|P108005|2010|2011|12/18/2010|3 Closed|Serbia|
|P108005|2010|2011|3/29/2011|4 Closed|Serbia|
|P108005|2010|2012|2/13/2012|5 Closed|Serbia|
|P108005|2010|2013|12/28/2012|6 Closed|Serbia|
# **2 Data**

## **2.1 Patterns in temporary migration**

Many migrant workers return home for a range of reasons. First, returning may be optimal and part of the migrants’ original plan even if they have the option to stay abroad; or because they are not legally permitted to stay in the destination country beyond a specified time frame. In contexts where permanent stays are possible, workers may choose to return because of location-specific preferences for consumption and amenities, such as being near their families or cultural characteristics (Hill 1987; Djaji ́c and Milbourne 1988). They may also choose to return because the income earned abroad and the resulting savings may have higher purchasing power and lead to higher consumption levels (Dustmann 1995; Dustmann 1997; Djaji ́c 1989; Dustmann 2003; Stark, Helmenstein, and Prskawetz 1997). Finally, migrants may have accumulated significant human capital (Dustmann, Fadlon, and Weiss 2011; Dustmann and Kirchkamp 2002; Stark 1991) or financial capital (or both) which may yield higher returns as entrepreneurs, self-employed professionals or investors at home (Mesnard et al. 2004; Bossavie, Gorlach, Ozden, and Wang 2021; Dustmann and Kirchkamp 2002).

To provide insights on the incidence of temporary migration, we first report evidence on the extent of return migration from United States where permanent migration is not only allowed, but tends to be the norm. As in most destination countries, most of the data come from standard nationally representative cross-sectional surveys or censuses. The main issue with these data sources is that returning migrants are simply not observed since they drop out of the sample. These data constraints limit the ability to draw precise conclusions, which happens to be the disclaimer in most of the papers reviewed here.

While we do not directly observe return migration, we proxy for the extent of returns by following a given arrival cohort of immigrants over different waves of the survey or the census. Figure 1 focuses on the different immigrant groups who arrived to the United States in 1998 and 1999 as they are observed in the 2009 and 2019 American Community Surveys (ACS). Those are expressed as a percentage of the initial flow of immigrants to the US in 1998-1999, estimated from the 2000 US Census. The orange and green bars present data from the 2009 and 2019 ACS, respectively. The first two bars on the very left of Panel A look at male migrants who were between the ages of 20-25 at the time of their arrival in 1998 and 1999. These bars imply that, in 2009, the number of immigrants in this group dropped to 92% of its number recorded in the 2000 census. Their number further declined to 75% according to the 2019 ACS. Since the decline in the number of people in the census or the ACS might be due to mortality, we calculate the gender and age specific mortality rates and 4
age.<sup>27</sup>

# **3.3 Descriptive Statistics and Balancing Tests**

Table 1 shows descriptive statistics of socio-demographic information for the full sample and separated by treatment arm. Panel A presents summary statistics of characteristics available for the full sample. On average, caregivers in our sample are 31 years of age,<sup>28</sup> 52 percent are women, and have an oldest child who is 4.6 years old. In terms of phone exclusivity, only 23 percent of participants share a phone with other relatives within the household. Finally, 3 of each 10 participants use a money-transfer technology (Tigo Money). Only 60 and 44 percent of caregivers completed baseline and endline surveys, respectively.

Panel B in Table 1 shows descriptive statistics for variables that are available only for individuals who completed the baseline survey. We find that most caregivers (64 percent) in our sample do not have tertiary education (bachelor’s degree or higher). We also show that 28 percent of the sample were either unemployed or lost their jobs during the pandemic.<sup>29</sup> Both treatment arms are balanced in most socio-demographic characteristics except for gender; there are more women in the WhatsApp (self-completion) group.<sup>30</sup> Since our sample involves participants with and without a baseline survey completed we also test for potential selection in pre-determined characteristics taking into account this feature. These results are presented in Figure A2 and show that there are no significant differences across arms.

Table 2 presents summary statistics of our main outcomes. In panel A, we show that on average, the completion rate was 51 percent and the number of attempts to reach participants (reminders) was greater than three. Moreover, between 60 and 68 percent of participants in our study were consistent in their responses of sensitive information at baseline, and between 58 and 62 percent at endline. Among reasons for

> 27In Table A10 in the Appendix, we present the list of variables that were collected during the enrollment and baseline surveys and merged to the participants of the midline survey.

> 28There are two missing observations in the survey-mode experiment sample for which we were not able to obtain information on age.

> 29As we show in Figure A2 in the Appendix section, we reject the null hypothesis that the completion of the baseline survey was driven by other individual characteristics such as participant’s age, gender, and availability of digital resources.

> 30As we discuss in the next section, to account for this difference, we include the gender indicator as a control in all estimations.

13
Even when two studies may use the same definition, small differences in either the numerator or the denominator can lead to significant differences in the ratio. For example, different sources use varying definitions of the economically active population, which can change the denominator. The labor force definition used by different sources such as the World Bank's Social Indicators of Development or the International Labour Office's "Yearbook of Labour Statistics" are not always clear and the figures may not always agree.

Another problem with coverage ratios is that the numerator is not always clearly defined. Many times it includes only private sector workers covered under a general scheme without considering public sector workers or special schemes which may exist for certain occupations. The military, for example, is rarely included in the estimate of the covered labor force while it is sometimes included the estimated labor force.

Data on provident funds usually include separate figures for both the number of active members (usually defined as those who have contributed at least once during the past year) as well as the number of affiliates. In Ghana, for example, the ratio of active members to registered affiliates was found to be .44. An even smaller percentage of affiliates are active in some of the other African countries. Even in Chile and Singapore, affiliates outnumber active members by a significant margin, in part because some people withdraw from the labor force and in part because some workers evade contributions. In provident funds, a balance remains in the affiliate's name and gathers interest whether or not he contributes during the year. In typical pay-as-you-go schemes, the missed year of contribution will result in one less year toward the minimum needed for retirement with a full pension. In some countries, noncontributory years are credited as service years if certain activities, like higher education are pursued. In short, the value of a missed year in different schemes depends on their design characteristics.

These design features, especially the eligibility and benefit formulas may lead some workers to evade payments during certain portions of their careers in order to increase their pensions. If coverage is defined as the proportion of the labor force which will eventually be eligible for a full pension, the number of contributors in a given year may seriously underestimate the true coverage. On the other hand, to the extent that workers are penalized for missing contribution periods, the liabilities of the system, as well as the probability that a worker will receive a full pension, are reduced.

27
The crisis split many households in that some household members stayed behind while others fled. This holds particularly for IDPs and returnees for whom in 43% of the cases household members remained behind. Refugees on the other hand tended to flee with their whole family and only in 10% of the refugee households did a household member remain behind. When fleeing, household members moved together and rare are the IDPs who report that some members of their household left the country (average 0.4 persons) or the refugees who report that members of their family were displaced inside Mali (average of 0.2 persons).

**Table 4: Household size before and after the crisis**

||**Household**<br>**size prior to**<br>**conflict**|**Number of**<br>**people that were**<br>**displaced within**<br>**Mali**|**Number of**<br>**people that were**<br>**displaced to**<br>**outside Mali**|**Deaths as a**<br>**result of the**<br>**crisis**|**Household**<br>**members**<br>**who stayed**<br>**behind**|
|---|---|---|---|---|---|
|IDPs|12.9|7.7|0.4|0.1|2.5|
|Refugees|7.8|0.2|7.3|0.1|0.3|
|Returnees|7.8|5.8|0.4|0.1|1.5|

Source: Listening to Displaced People Survey, 2014.

Before the crisis the average size of an IDP household was 13 persons (Table 4). Of these 7.7 fled, 2.5 remained, and 1 household member died in 8% of the households. Returnees had an average household size of 8 before the crisis of whom 5.8 became internally displaced, 1.5 remained behind, and 5% reported that 1 to 3 people in their household died. Refugees, with an average household size of 8, reported that on average 7.3 household members left the country, while 0.3 stayed behind. 5% of the refugees reported that 1 to 3 members of their household died.

IDPs and returnees tend to be relatively well educated and their education is more comparable to that of people living in Bamako than to that of people living in northern Mali. The large majority of the population in the North has no formal education but the majority of IDPs and returnees have at least primary education. Refugees on the other hand do have the same level of education as the average person in the North. This can be seen in Figure 3 which presents levels of education for IDPs, refugees and returnees, but also for the population of Bamako and the regions of Gao, Timbuktu and Kidal. The latter information is from the 2009 census.

9
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**_Chart 5.4  Recruitment of Present Civil Servants into the Government of Anjouan, By Year_**

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Note: 2011 data is incomplete as the questionnaire was completed in a rolling fashion over that summer.

The data above show that, like Ngazidja, 2010 was a peak recruitment year as well, correlating with the election for island governor. 2008 was the second largest recruitment year for the entire Government of Comoros, with 859 recruits entering the civil service. The Government of Anjouan primarily hired staff in this year, when 648 civil servants were recruited. 2008 was the same year in which the African Union force removed Mohamed Bacar from power, after his illegal takeover of the executive in 2007. According to interviews with officials and numerous civil servants in communities, the Bacar regime recruited a number of volunteer and contractual workers, often as teachers. After the downfall of the Bacar regime, the newly elected president of the Island Government of Anjouan decided to normalize many of these volunteer and contractual workers into the civil service, leading to the sudden jump in 2008. The motive behind this recruitment was unclear. Various key informants suggested that it represented a gesture of reconciliation with the citizens in Anjouan, though it may have also been motivated by the desire to win their political support, either for the Union or for the executives involved. Such events suggest that devolution leads to an increase in recruitment, though, unlike the more traditional manner described above, recruitment was not the result a ‘chaotic’ decentralization, but rather a reaction to a period of conflict.

12