rafmacalaba/datause-encoder-data huggingface dataset:
the combined effect of the collapse in domestic production, sanctions, devaluation and increasing reliance on imports of basic commodities.

_Figure 9: GDP deflator and CPI growth index (2003=100)_ <!-- Start of picture text --> 3300<br>3000<br>2700<br>2400<br>2100<br>CPI GDP Deflator<br>1800<br>1500<br>1200<br>900<br>600<br>300<br>0<br><!-- End of picture text --> _Source:_ WB staff calculations based on WDI and CBS _Based on these considerations, the most appropriate approach to project welfare growth would be to use per capita GDP in local currency units deflated using the CPI._<sup>_26_</sup>

# 4.1.2 Population estimates

Data challenges also affect population estimates, which are needed to convert macro aggregates in per capita terms.

The last population Census in Syria was conducted in 2004. Since then, population estimates for Syria have been revised multiple times. Pre-conflict population estimates and projections to account for the tremendous impact of conflict which claimed the lives of at least 350,209 individuals<sup>27</sup> and forced millions of Syrians to leave the country.<sup>28</sup> As shown in Figure 10, sizeable differences exist between different revisions of Population Prospects data post conflict. In particular, the latest available estimates from the 2022 Prospects revision – which have been recently adopted in WDI – place the Syrian population for 2022 at about 22 million, in line with direct population counts from HNAP and with the 2015 Prospects revision, but as much as 15 percent higher compared to the 2019 Prospects revision. Moreover, while population estimates for most recent years are relatively similar between the 2015 and 2022 population prospects revisions, major differences emerge for the years between 2009 and 2015.

> 26 See Section 4.2 and Section 5 for further discussion.

> 27 Estimates of the total death toll of the Syrian war vary depending on the methodology and reporting agency. In 2021, the UN’s High Commissioner for Human Rights (OHCHR) office released a tally of <u>350,200 deaths including both civilians and combatants.</u> The count is based on a strict methodology requiring the deceased full name, date of death and location of the body and should therefore be interpreted as an under-estimation of the actual number of was related deaths.  The Syrian Observatory for Human Rights (SOHR) estimates an overall death toll of 610,000 people over 11 years of conflict, of which 160,681 are represented by civilians (120,158 men, 15,237 women and 25,286 children).

> 28 5.6 million Syrian refugees are currently hosted in neighboring countries and an additional 1 million reside in Europe, mostly in Germany and Sweden (UNHCR).
respect to encouraging vaccination. Another meta-analysis from Mayer and Fontelo (2017) found significant positive effects of text message reminders for HIV-related treatment compliance. Yet another meta-analysis found significant impacts from reminders on treatment for coronary heart disease (Zhao, 2019). Other studies have found significant impacts on feeding programs (e.g. Zhou et al.) and case management practices (e.g. Zurovac, 2011).

In the face of both past success using similar methods, suggestive evidence of positive uptake in crosssectional survey measures, and initial null effects from an RCT evaluation of a similar project design, uncertainty remains as to whether and under what circumstances SMS-based messages can be effective at encouraging risk reducing behavior during a public health crisis like COVID-19.

In the following, I describe a mass SMS text-messaging campaign conducted together with the health authorities in Tajikistan and implemented alongside a monthly individual panel survey that measured self-reported risk reduction behavior. The results demonstrate that receiving and relying on text messages for information on COVID-19 led to a significant increase in self-reported risk reduction behaviors. Listing SMS as a primary source of information increased the number of reported behaviors by .15 units (p = 0.000) and receiving an official text message in the past week increased the number by .47 units (p = 0.000). These effects were driven by more positive responses for wearing masks, reducing visits with friends and relatives, reducing travel, practicing safer greetings (fewer handshakes, etc.) and safety-related changes to work. The results suggest that SMS-based public health messaging was a cost-effective means of increasing awareness in a large and geographically dispersed audience during the COVID-19 pandemic, and that the program led to an increase in self-reported risk reducing behaviors.

The remainder of the paper is structured as follows. Section II provides details on the country context, discussion of the messaging campaign, and a description of the data used in the study. Section III briefly describes the pooled ordinary least squares (OLS), and fixed effects estimators used to measure differences in reporting behaviors between message-receiving and non-receiving respondents. Section IV includes of the results, and section V a discussion of the study’s implications.

# II – Context, Messaging, and Data

About 93 percent of Tajikistan’s territory is occupied by mountains and only about 6 percent of land is arable. About 70 percent of the population lives in rural areas and most reside in the valleys in the western half of the country. There are numerous small communities scattered in difficult-to-reach mountainous regions. Due in part to this challenging terrain, authorities often struggle to communicate with and provide public services to people living in remote areas where long travel times are common. National surveys, including the main data source for this study, find that large majorities of respondents see improving basic public services (such as health and education) as high priority issues, 3
# Tables

|Table 1: Descriptive statistics for theEU Standards Database (EUSDB).<br>Clothing!<br>Footwear”<br>Textiles?<br>Total<br>%ISO<br>Total<br>%ISO<br>Total<br>% ISO<br>1995<br>15<br>6.67<br>5<br>0.00<br>102<br>= 50.98<br>1996<br>18<br>5.56<br>10<br>40.00<br>125<br>55.20<br>1997<br>20<br>5.00<br>15<br>33.33<br>164<br>57.93<br>1998<br>28<br>17.86<br>18<br>27.78<br>187<br>60.96<br>1999<br>32<br>18.75<br>31<br>54.84<br>223<br>62.33<br>2000<br>28<br>21.43<br>39<br>56.41<br>249<br>59.84<br>2001<br>32<br>21.88<br>57<br>64.91<br>268<br>56.34<br>2002<br>35<br>22.86<br>60<br>61.67<br>281<br>56.94<br>2003<br>41<br>19.51<br>67<br>58.21<br>303<br>56.11|
|---|

1. HS chapters 61-63.

2. HS chapter 64.

3. HS chapters 50-60.

4. Total refers to the total number of CEN European standards in force for each sector-year. % ISO is the proportion of the total number of standards that are internationally harmonized (i.e., identical or equivalent to an ISO standard).

<u>Table 2: Descriptive statistics for product variety (A,..;). Clothing’ Footwear* Textiles”</u> Median Max. Countries Median Max. Countries Median Max. Countries 1995 0.22 0.94 185 0.26 0.96 134 0.08 0.81 180 1996 0.21 0.95 193 0.27 0.99 135 0.08 0.82 179 1997 0.28 0.95 185 0.34 0.99 131 0.10 0.82 168 1998 0.24 0.95 187 0.34 0.99 129 0.09 0.83 166 1999 0.26 0.96 186 0.32 0.99 135 0.08 0.83 178 2000 0.26 0.96 191 0.26 0.99 137 0.10 0.83 171 2001 0.28 0.96 203 0.27 0.99 145 0.08 0.83. 179 2002 0.28 0.96 200 0.36 0.99 136 0.09 0.83. 179 2003 0.27 0.96 204 0.33 0.99 141 0.07 0.82 187 5. Axst is calculated as described in the text, using 8-digit Eurostat import data.

29
schedule are not correlated with wealth. This is indirect evidence that the steps we took to homogenize the items were successful.<sup>14</sup>

# **The value of forgone consumption**

Recall from section 2 that the quantity of consumption forgone is given by _Qhi_ = _qhi_<sup>_∗−qhi_,</sup> where the first term is the inverse of the expenditure function evaluated at total adjusted expenditure _e_<sup>_∗_</sup> _hi_<sup>,andthesecondtermistotalobservedquantity.Likewise,thefinancialloss,</sup> ˆ or value of forgone consumption, is defined as _Lhi_ = _ehi − e_<sup>_∗_</sup> _hi_<sup>,where</sup><sup>_e_ˆ</sup><sup>_hi_istotaladjusted</sup> expenditure and _e_<sup>_∗_</sup> _hi_<sup>isthecostofbuying</sup><sup>_qhi_inasingletransaction.Summingacrossitems</sup> at the household level gives _Lh_ =<sup>�</sup> _i_<sup>_Lhi_.</sup> Columns 1–3 of Table 4 report the item-level means of total observed quantity, _qhi_ , counterfactual quantity, _qhi_<sup>_∗_, and the counterfactual percent increase in quantity,</sup><sup>_Q_ ̃</sup><sup>_hi_.Calcu-</sup> lations in this table are based on all households that purchase an item more than once. The results are striking: without changing total expenditure, households could increase quantity purchased by over 15% on average. Potential quantity increases are over 25% for kerosene, onions, cooking bananas, cooking oil, and tea leaves, and are almost 18% for dried sardines. These are staple goods: kerosene is the primary lighting fuel in much of Tanzania, cooking bananas are a staple carbohydrate in the two districts where they are commonly purchased, dried sardines are a key source of protein, cooking oil is the main source of cooking fat. Most households purchase one or more of these goods: 85% purchase kerosene, 78% purchase cooking oil, 70% purchase dried sardines, and 22% purchase cooking bananas (Table 2). The consumption losses implied by columns 1–3 of Table 4 are substantial at face value.

The money-metric measures of loss tell a similar story. In columns 5–7 of Table 4 we report summary statistics for _e_ ˆ _hi_ , _Lhi_ , and _L_<sup> ̃</sup> _hi_ . Items are listed by decreasing values of column 7, so that high loss items are at the top (from now on we will usually display items in that order.) On average, losses represent 8.7% of total expenditure at the householditem level. For a number of frequently purchased items – kerosene, onions, cooking bananas,

> 14In Appendix S3 we also argue that bulk discounts cannot be the manifestation social capital or buyerseller relationships, because the prices collected by enumerators in the market price surveys also reflect bulk discounts.

16
# **Table A6: Panel A – Nested Logit Regressions, 2006**

Log Odds ratio between Renting and Owning

|**Country**|**parttime**|**unemp**|**retired**|**urban**|**suburban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|
|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_||||||||||
|Czech Republic|-0.12|0.78 ***|-0.26|0.67 ***|||-0.24||-0.19|
|Estonia|-0.06|-1.71|-3.79 *|-0.45|||-1.42 *||-0.25|
|Hungary|-0.57|-0.12|-1.26|0.84 ***|0.64 **|-1.00|-0.56|-0.32|-0.89|
|Latvia|-0.19|-0.57|-0.66|-0.08||0.08|-0.43|-0.32|-0.66|
|Lithuania||1.98||-0.39|||-2.82 *||-0.22|
|Poland|-3.52 *|0.64|-1.70|-0.07|-0.29|0.07|1.55|-2.79 **|-2.23 *|
|Slovak Republic|-0.81|-0.56|-0.48|-0.84 ***|-0.88 ***|1.07|0.01|-1.43|-1.48|
|_EU-15 and Cyprus_||||||||||
|Austria|1.57 *|1.61|-0.16|0.81 ***|0.45 **|-2.92|-2.81|-0.40|-0.22|
|Belgium|-0.87 ***|-0.19|-0.74 **|0.30|0.09|-1.92 ***|-2.10 ***|-0.82 ***|-0.85 ***|
|Cyprus|1.01|1.24|0.07|0.92 **|0.34|-0.55|0.80|-1.81 *|-1.55 *|
|Denmark|-0.68 **|-0.14|-0.60 ***|0.26 **|||-0.59 *||-0.89 ***|
|Finland|-0.44|0.28|-0.35|0.35 ***|-0.21|-4.33 ***|-4.73 ***|-0.34|0.02|
|France|-0.23|0.46|-0.54 **|0.15|0.16|-2.56 ***|-2.41 ***|-1.01 ***|-1.27 ***|
|Greece|-10.72|-9.01|-7.90|-0.90|-2.51|6.84|12.97|0.84|-1.58|
|Ireland|-0.53|-0.29|-0.70 ***|0.60 **|0.48 *|-3.00 ***|-3.38 ***|-1.05 ***|-1.02 ***|
|Italy|-0.87|0.19|-0.62 **|-0.31|-0.47|-2.47 ***|-2.45 ***|-0.14|0.14|
|Luxembourg|-0.33|0.94 *|-0.32|-0.21|-0.07|-1.36 ***|-1.25 **|-1.30 ***|-0.35|
|Netherlands|-0.77 ***|-0.46|-1.06 ***|-0.03|||-1.35 ***||-0.70 ***|
|Portugal|-0.43|-0.44|-0.57|0.60 ***|0.50 **|-1.70 ***|-0.52|-0.58 **|-0.95 ***|
|Spain|0.88 ***|0.60 **|0.18|-0.12|-0.13|-0.69 *|-1.12 ***|-0.16|-0.33|
|Sweden|-0.68|0.05|-0.66|-0.22|0.00|-1.71 **|-1.63 **|-0.64 **|-0.58 *|
|United Kingdom|-1.27 ***|0.49 *|-2.11 ***|0.08|||-2.45 ***||-1.01 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2006 40
speed of both small and large-claim cases (cols 1 and 2, Table 4). Second, claim size does not predict the rate at which cases are desk-rejected or fast-tracked after the decree, and we fail to detect a differential intensification of pre-trial hearings (cols 3–6, Table 4). Third, we find that judges are more likely to impose stricter adjournment rules on the parties of larger cases after the decree, while the effect on smaller cases is small and imprecisely estimated (col 7, Table 4). We conclude that the decree improved judicial efficiency similarly for different types of cases, but to achieve these gains judges had to put more pressure on the parties of larger cases.

Do relatively slow and fast judges apply the reform differently? We find no evidence of differential effects by judge baseline speed on pre-trial duration, desk rejection, fast tracking or stricter adjournment rules imposed on parties (cols 1–4 and 8, Table 5). Fast judges are significantly less likely to increase pre-trial likelihood of being heard relative to slow judges, although neither type of judge significantly responds to the decree at that margin (col 6, Table 5).

Overall, these results suggest all case types and judge types responded to the reform by moving to a new equilibrium in which pre-trials are faster. The absence of differential intensification of the procedure for large cases supports the notion that judges do not manipulate delays for their private gains.<sup>23</sup>

# **5.2.3 Robustness of the court event study design**

Our main identifying assumption is that in the absence of the reform, the speed of justice would have followed a steady trend within each chamber. We consider identification threats coming from four types of selection issues: selection on level, selection on trend, selection on shock, and selection on trend shock. First, bias from potential selection on level is eliminated by the within-chamber differencing. Second, the event study graphs allow us to

> 23 Assuming common trends across chambers does not change our point estimates (Table A3).

17
respectively. However, the net benefit stays the same since preference margins also increase due to CBTPA (see Graph 3b). For example, the average preference margin in 2002 is 13% which means the average export price increase due to preferential access is again around 10.5%.

We also compared the original CBI regime with the new CBTPA regime and report the results in Table 5. The beneficiaries capture a higher portion of the rents in the old regime - 71% vs 64%. However, the average preference margin under the old regime is 9.5% while it is 12.6% under CBTPA. This means the average increase in export prices due to preferences is 6.7% in the old regime while it is 8.1% under CBTPA. This once more explains why the CBI countries were so keen on the implementation of CBTPA.

5.3. Impact of Quotas The final question we address is the impact of the US apparel quotas (imposed on third countries, mainly Asian countries such as China, India, Korea etc.) on the export prices and tariff rents received by the CBI beneficiaries. Unfortunately, quota data and import data (with unit prices and preference programs) are collected under two different classification schemes. Furthermore, in most cases, a quota category covers multiple HS 8-digit categories. So it is rather difficult to merge the two datasets and we need to use indirect measures. We first compiled the list of the top 16 apparel exporters to the US who face significant quotas!’ and calculated their market share in each category in each time period in our sample. We divided the data into two sub-samples; one in which the quota countries have above median market share and one below median. We estimated the main equation [3] separately for the two sub-samples. Column 2 and 3 in Table 6 respectively report these results.

Quota countries may have low market share in a category for two reasons: The quotas might be restrictive or the exporting countries might not be competitive. For categories in which the quota countries have below median market share, the CBI beneficiaries capture almost all of the tariff rent; the coefficient of the Tarif f Dif ference variable is 1.009. On the other hand, when quota <u>17 Phese are China, Turkey, India, Pakistan,</u> Bangladesh, Sri Lanka, Thailand, Vietnam, Cambodia, Malaysia, Indonesia, Phillipines, Macao, Korea, Hong Kong, Taiwan. The others in the top 20 exporters of apparel are Mexico, Canada and CBI countries.

16
Abidjan are least affected by the application of H-S weights, although even here, mean household size is reduced by over 10 percent.

Similarly, these adjustments in the data have varying effects on different groupings of households. Table 8 reports mean household size with and without the application of H-S weights for each of eight socio-economic groups.4/ The mean size of export-crop farming households is reduced by about a quarter, with similar orders of magnitude applying to the self employed, informal-sector employees (at least in 1986) and the inactive. There are two reasons why the over-representation of larger households in the sample affects some socioeconomic groups more than others. First, the main geographical location is not the same for each group. For example, export-crop farmers are located mainly in East and West Forest, where over-representation of larger households was greater, rather than the Savannah, where the sampling bias was not as significant. Similarly, the relatively small effect that H-S weighting has on formal-sector workers (both private and public) arises from their location predominantly in Abidjan. The second reason is that household size is related systematically - to socio-economic category. For example, very large households where the re-weighting - has the most pronounced effect are more prevalent among export crop farming and urban self-employed households.

# IV. **Inplications for Estimates of** Mean **Household Expenditures**

The fact that the application of H-S weights to the CILSS data set reduces mean household size significantly, and that the effects are concentrated among certain areas and socio-economic groups, suggests that this procedure may have an important bearing on other findings of the survey. Our interest here is to assess the effects of correcting for householdsize-related sampling bias on three types of variables: total household expenditure per capita; estimates of poverty incidence based on per capita expenditures as an indicator of welfare; and basic needs indicators. Our intention is to answer two important questions:

- 4/ The categorization of households into each of these groups is based on the main source of income for the household and the employment status of the household head (it follows Grootaert, 1992).

16
57

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- 27 -

Table 2: COMPONENTS TO ASSIST SUPERVISION IN BANK PROJECTS

|Type of Component|Frequency|
|---|---|
|In-service<br>training<br>for supervisors|11|
|Provision<br>of transportation|7|
|Creation<br>of new category<br>of supervisor<br>Establish/Strengthen<br>local<br>administrative<br>capacity<br>to assist with supervision|6<br>6|
|Publish, distribute supervision<br>guidance manuals|2|
|Provision of office facilities for district supervisors|1|
|Establisbment of supervision<br>norms|1|
|Change supervisory staff functions|1|
|Television support to supervisors|1|

Total projects review - 24
26 <u>Table 8: Median rates of impoverishment due to out-of-pocket health expenditures, $1.90-a-day poverty line, latest year</u>

||Low-<br>income|Lower<br>middle-<br>income|Upper<br>middle-<br>income|High<br>income|Total|
|---|---|---|---|---|---|
|East Asia & Pacific|1.70|0.25|0.78|0.18|0.25|
|Europe & Central Asia|1.98|0.00|0.00|0.04|0.03|
|Latin America & Caribbean|0.34|0.75|0.47|0.70|0.50|
|Middle East & North Africa||0.13|0.27|0.44|0.20|
|North America||||0.18|0.18|
|South Asia|3.19|0.79|||1.36|
|Sub-Saharan Africa|1.34|1.36|0.45||1.21|
|Total|1.38|0.48|0.10|0.06|0.30|

<u>Table 9: Median contribution of out-of-pocket health expenditures to per capita poverty gap in $US, $1.90-a-day poverty line, latest year</u>

||Low-<br>income|Lower<br>middle-<br>income|Upper<br>middle-<br>income|High<br>income|Total|
|---|---|---|---|---|---|
|East Asia & Pacific|0.01|0.00|0.00|0.00|0.00|
|Europe & Central Asia|0.02|0.00|0.00|0.00|0.00|
|Latin America & Caribbean|0.00|0.01|0.00|0.01|0.00|
|Middle East & North Africa||0.00|0.00|0.01|0.00|
|North America||||0.00|0.00|
|South Asia|0.02|0.00|||0.01|
|Sub-Saharan Africa|0.02|0.01|0.00||0.01|
|Total|0.02|0.00|0.00|0.00|0.00|