rafmacalaba/datause-encoder-data huggingface dataset:
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Enumerator: The purpose of this this module is to get an idea about men’s and women’s time spent in both work and leisure activities and their satisfaction with their time use. FO01: Please record a log of the activities for the the individual in the last complete 24 hours (starting yesterday morning at 4 4 am, finishing 3 am of the the current day). The time intervals are marked in 15 min intervals and one to two activities can be marked for each time period by drawing a line through that activity. If two two activities are marked, they should be distinguished with a P for the primary activity and S for the the secondary activity written next to the to the the lines. Please administer using the protocol in the enumeration the enumeration enumeration manual.

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Table 2: CITL - AMADEUS Matching

|Country|Matc<br>|hing Proportion<br>|(%)<br>|
|---|---|---|---|
||Power*|Cement|Iron<sup>#</sup>|
|Germany|0.289|0.837|0.957|
|Great Britain|0.191|0.538|1.000|
|Italy|0.026|0.836|0.913|
|Poland|0.055|0.708|1.000|
|Spain|0.345|0.879|0.929|
|France|0.196|0.980|0.808|
|Netherlands|0.154|^|#|
|Czech Republic|0.099|0.818|1.000|
|Belgium|0.202|0.545|0.963|
|Austria|0.260|0.750|^|

Note: *: Since the matching proportions are based on CITL 9 sectors, the power industry is just a small part of the combustion sector therefore we have a low probability of matching for power, though we suspect that the non-matched ones do not belong to the power sector.

^: We are not able to match any of the six cement CITL installations in Netherlands, where five of the installations belong to the same firm. Therefore, we do not include any cement firms in Netherlands in our analysis. Similarly for iron industry, we are not able to match the one CITL installation for Austria and we drop all Austrian firms in iron and steel industry.

#: Due to a broader classification of the CITL sectors, we conduct our matching by including all industries under “Manufacture of Basic Metals", which may include manufacture of tubes, pipes, or other products of first processing of steel, but we only isolate firms that are in the primary iron and steel industry class (24.10). Thus we have dropped two Dutch installations, though they are matched, they do not belong to the primary industry class that we are focusing on.

Table 3 presents the summary statistics for both participating and non-participating firms by sector. As we can see from the last column, participants generally are bigger – they incur higher material and labor costs, and also have larger turnovers. This is not surprising given that only large emitters are regulated by EU ETS. However, the difference in size between participating and non-participating firms does impose a difficulty in estimating the causal effect (see more discussion in the next section). In order to create a robust control group to isolate the size effect we use the pre-program year in 2004 to choose non-participant firms that are ‘close enough’ to EU ETS firms. Specifically, we choose non-participant firms who have their 2004 turnover falling between the 25% and the 75% percentiles of the distribution of the turnover of the participant firms as the control group. Figures 1-3 illustrate the trend in unit material cost of participating and non-participant firms. For all the three sectors, the two groups share similar 9
Figure 11: Labor and Investment Policy Effects <!-- Start of picture text --> 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Year<br>Simul Average Demograph y Investmen t Both<br>30000<br>20000<br>10000<br>0<br><!-- End of picture text --> Note (1) Each line represents the actual or the predicted path of GDP per capita using different calibration methods.

Note (2) ”Simul”: Predicted GDP per capita calibrating at fully time-varying parameters, ”Average”: Predicted GDP per capita calibrating at constant parameters of average values during the sample period, ”Demography”: Predicted GDP per capita allowing time-variation only for the labor market demography parameters, ”Investment”: Predicted GDP per capita allowing time-variation only for the investment rate parameter, ”Both”: Predicted GDP per capita allowing time-variation for both labor market demography and investment rate parameters.

29
function of household characteristics, such as asset ownership, housing quality, education levels, etc., that produces a score used to rank households by their estimated level of welfare, when verifiable consumption or income data are not readily available and costly to obtain. Various methods exist for identifying and aggregating household characteristics to produce a PMT score (Coady, Grosh, and Hoddinott 2002; Chen and Schreiner 2009). However, it has become common to use statistical, regression-based methods to identify weights in the PMT from regression coefficients of household consumption or income using household survey data (Brown, Ravallion, and Van de Walle 2018). The PMT weights are then applied to information collected from program applicants to construct a household specific PMT score, which is an out-of-sample prediction of consumption expenditure or income, depending on the welfare variable the National Statistics Office measures. The resulting PMT score is thus an estimate of the household welfare variable, which national poverty and welfare measures are typically based. The PMT score is then used to determine household eligibility for social programs based on a predetermined cutoff (i.e., the PMT score cutoff, similar to a poverty line) and may also be used to determine the level of benefit.

While universal social protection programs that cover the entire population may be desired, they are often not feasible given limited budgets, particularly in developing countries. The use of a PMT to target social programs has been shown to improve targeting incidence and reduce the inclusion of people unintended for public program support, improving program efficiency (Grosh, 1994). Furthermore, because the PMT ranks households by their estimated welfare level, the same tool can be used for targeting various welfare programs. A common targeting approach for the various social programs, based on a single PMT score, can improve consistency across programs, reduce overlap and duplication of benefits, and promote future harmonization of public programs (Sebastian et al. 2018). Due to these benefits, PMTs are now widely used in administering various social programs in numerous countries throughout the world (Grosh, 1994; Mills, Del Ninno, and Leite 2015; Brown, Ravallion, and Van de Walle 2018; Sebastian et al. 2018).

Program eligibility determined by a PMT, however, is susceptible to inaccuracy, because it is an estimate or prediction of a household’s welfare. Errors may arise both by including ineligible applicants whose PMT score predicts them to be poor when in fact they are not (i.e., inclusion errors), and excluding eligible applicants whose estimated PMT score predicts them to be not poor when in fact they are (i.e., exclusion errors). These inaccuracies are due to prediction error arising primarily from unmeasured and costly to collect socioeconomic factors and idiosyncratic behavior, as well as model selection and potential measurement error in the data (Gazeaud, 2020).

6
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Water supply and sanitation

## **Achievements**

Angola has made important progress in reducing reliance on open defecation. In 2007, 24 percent of the population practiced open defecation compared to 49 percent in 2001. Even though the improvement has been significant, the percentage is still high, at almost twice the level of middle-income countries (MICs) (table 13).

**Table 13. Benchmarking water and sanitation indicators**

||Unit|Resource-<br>rich countries<br>Ang|ola|Middle-<br>income<br>countries|
|---|---|---|---|---|
|||Mid-2000s<br>2001|2007|Mid-2000s|
|Access to piped water|% pop|13<br>13|18|52.1|
|Access to stand posts|% pop|12<br>14|10|18.9|
|Access to wells/boreholes|% pop|47<br>39|31|6.0|
|Access to surface water [1]|% pop|27<br>34|40|13.0|
|Access to septic thanks|% pop|13<br>17|31|40.8|
|Access to improved latrines|% pop|37<br>18|22|1.4|
|Access to traditional latrines|% pop|22<br>16|21|30.4|
|Open defecation|% pop|28<br>49|24|14.3|
|||2005|2009||
|Domestic water consumption|liter/capita/day|115<br>75|56|154|
|Revenue collection|% sales|60<br>85|44|100|
|Distribution losses|% production|40<br>62|61|27|
|Cost recovery [2]|% total costs|67<br>81|72|81|
|Operating cost recovery|% operating costs|94<br>114|100|145|
|Labor costs|connections per employee|96<br>25|34|369|
|Total hidden costs as % of revenue|%|194||140|
|||Angola|Low-income,<br>water-scarce<br>resources|Other<br>developing<br>regions|
|||2005<br>2009|||
|Average effective tariff|U.S. cents per cubic meter|120<br>230|60–120|3–60|

_Source:_ Demographic and Health Survey 2006/7 and AICD water and sanitation utilities database <u>(www.infrastructureafrica.org/aicd/tools/data).</u> _Note:_ Access figures from the 2001 Multiple Indicator Survey and 2007 Malaria Indicator Survey.

[1] Surface water includes rivers, lakes, and ponds in the vicinity as the main source of water supply, as well as other nonimproved sources of water such as water from vendors and rain water.

[2] Cost recovery is estimated based on the assumption of a capital cost of 40 cents per cubic meter.

The reduction in open defecation has been achieved by the expansion of higher-end forms of sanitation in urban areas and traditional latrines in rural areas. Access to septic tanks almost doubled between 2001 and 2007, from 17 to 31 percent. In urban areas about 4.8 percent of the population has been gaining access to septic tanks each year, an exceptionally high rate of expansion that mirrors the country’s rapid urbanization process (figure 16b). The use of improved latrines increased from 18 percent 28
**Table S24. Strategies to cope with income lost due to pandemic across rural/urban sector**

||Relied on<br>Saving|Sale of<br>Assets|Reduced<br>Food<br>Consumption|Reduced<br>Non-Food<br>Consumption|Received<br>Assistance<br>from Friends<br>and Family|Received<br>Any<br>Assistance|
|---|---|---|---|---|---|---|
|Urban|0.016|-0.061***|0.088***|0.017|0.046**|0.014|
||(0.023)|(0.012)|(0.021)|(0.019)|(0.022)|(0.011)|
|Ethiopia|0.053*|-0.014|0.108***|0.146***|-0.030*|-0.022*|
||(0.028)|(0.011)|(0.022)|(0.018)|(0.017)|(0.013)|
|Nigeria|0.088***|0.077***|0.581***|0.123***|0.137***|0.016|
||(0.024)|(0.015)|(0.022)|(0.014)|(0.019)|(0.014)|
|Uganda|0.226***|-0.009|0.171***|0.177***|0.135***|0.059***|
||(0.026)|(0.011)|(0.023)|(0.018)|(0.021)|(0.014)|
|Observations|5,595|5,595|5,595|5,595|5,595|8,719|
|R-Squared|0.015|0.035|0.223|0.012|0.033|0.011|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent stated that the household relied on that strategy to cope with the loss of income and 0 if they did not. The binary variable is regressed on indicators for each country, with Malawi as the base case, along with an indicator equal to 1 if the household was located in an urban area, as opposed to a rural area. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia and Nigeria, round 2 in Malawi, and round 1 in Uganda. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

51
The share of lending to SMEs does not seem to have increased considerably. The problem of tracking the evolution in the accessibility of commercial financing is exacerbated when attempting to review lending by firm size, since there are different SME definitions used by the CNBV (from which most bank reporting is derived) and by the Ministry of Economy<sup>42</sup> . For example, according to the former definition and the R04C database, only around 5 percent of the banks’ commercial loan portfolio as of end2005 comprised of SME loans. By contrast, according to recent BOM analysis based on the SENICREB database cross-referenced with information from the Ministry of Economy’s _Padrón SIEM_ and the magazine _Expansión 500_ databases, micro and SME lending made up 30 percent of bank commercial lending as of end-2005. As can be seen in figure 30, that proportion does not appear to have increased considerably in recent years, although the reported figure might be understated for several reasons<sup>43</sup> .

Figure 30: Outstanding Bank Credit to Firms by Firm Size (2001Q4-2005Q3) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Dec-01 Dec-02 Dec-03 Dec-04<br>Unidentified Micro Small Medium Menores Large<br>% of Banks' Commercial Credit Portfolio<br><!-- End of picture text --> Source: BOM’s _SENICREB_ database cross-referenced with information from _Padrón SIEM_ (Secretaria de Economía) and _Expansión 500_ databases.

Note: Firm size uses the Ministry of Economy definition, which is based on the number of employees and varies by economic sector. Restructured FOBAPROA loans are excluded, even when they are performing.

The experience of various government programs to support micro and SME lending has been generally positive but limited to-date. According to the 1999 census, 99.7 percent of all firms are classified as micro and SMEs (see figure 31), which support 7 out

> 42 The Ministry of Economy’s definitions of firm size are based on the number of employees and vary by economic sector (manufacturing, trade and services). By contrast, according to the CNBV, only companies with loans from commercial banks below 900,000 UDIs are considered to be SMEs.

> 43 These include banks reporting reverse factoring arrangements with SME suppliers of large corporates as loans to the latter as opposed to the former, and reporting short-term revolving loans to micro-entrepreneurs via credit cards and similar instruments as consumer loans. However, these forms of financing are not yet large in volume.

30
# **C Robustness Tests**

Table C.1. Extremism Impact on Integration: controlling for Social Desirability Index

|Variables in SD.|Integration<br>Index<br>(1)|Feel Comfortable<br>if your Child<br>Socialize with Host<br>Children<br>(2)|Feel Welcomed<br>in this<br>City<br>(3)|Feel Part<br>of this<br>Village<br>(4)|Feel that<br>Tajikistan is<br>your Home<br>(5)|Number<br>of Refugee<br>Friends<br>(6)|Number of<br>Tajikistan<br>Friends<br>(7)|
|---|---|---|---|---|---|---|---|
|_Panel A. Taliban Exposure_||||||||
|_ETi_|-0.578***|-0.509***|-0.207|-0.277*|-0.356**|-0.343**|-0.450***|
||(0.160)|(0.173)|(0.160)|(0.160)|(0.164)|(0.163)|(0.138)|
|FDR q-values|[0.001]|[0.006]|[0.060]|[0.054]|[0.030]|[0.030]|[0.004]|
|R-squared|0.031|0.032|0.039|0.031|0.049|0.061|0.037|
|_Panel B. Contested Territories Exposure_||||||||
|_ECi_|-0.093|-0.096|-0.123|-0.099|0.040|-0.033|-0.050|
||(0.062)|(0.067)|(0.062)|(0.063)|(0.063)|(0.063)|(0.054)|
|FDR q-values|[0.360]|[0.360]|[0.360]|[0.360]|[0.360]|[0.360]|[0.360]|
|R-squared|0.029|0.031|0.039|0.030|0.048|0.060|0.035|
|_Panel C. U.S.- Allies Exposure_||||||||
|_EAi_|0.107|0.103|0.092|0.085|0.008|0.051|0.071|
||(0.048)|(0.052)|(0.048)|(0.048)|(0.049)|(0.049)|(0.041)|
|FDR q-values|[0.137]|[0.137]|[0.137]|[0.137]|[0.331]|[0.137]|[0.137]|
|R-squared|0.030|0.031|0.039|0.031|0.048|0.061|0.036|
|Mean Dependent Variable|0.002|-0.027|0.026|0.038|0.074|-0.007|-0.099|
|Observations|5,592|5,285|5,535|5,469|5,592|5,592|5,589|
|_Controls in All Panels_||||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ See Appendix B for details of variable’s constructions. We used the same controls listed in Table 3. We measure social desirability bias by using four questions from Marlone and Crowe’s social desirability scale. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
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these areas. As such, the survey is representative of this part of the Philippines. These list randomization questions were added to the survey partway during the baseline, and were able to be asked of 2983 individuals. Randomization was done by a computer draw which determined which version of the question an interviewer should ask each interviewee.

# **4. Results**

# **4.1 Sample Characteristics and Verification of Randomization**

Table 1 provides sample means for the treatment (group A) and control (group B) in each of the four countries for a set of variables that are common to all four surveys. We see the different settings enable us to trial this methodology across a range of demographic settings.

The Ethiopian sample is the least educated and poorest, with 75 percent of respondents having primary education or lower, only 2 percent of households owning a car, and only 39 percent having a television. The oversampling of migrants is reflected in 35 percent of the sample reporting that they have a household member abroad, compared to a national average of only 0.7 percent of the population being emigrants (World Bank, 2011). The Moroccan sample is more heavily dominated by males (the main respondent was chosen as the person who has most control over household finances), still has half the sample with primary education or lower, and has 22 percent owning a car, but almost universal television and telephone ownership. Oversampling of migrants has 28 percent of the sample reporting they have a migrant, compared to a national average of 9.3 percent of the population being emigrants (World Bank, 2011).

The Mexican sample has relatively young and the most educated respondents, with an average age of 34 and 39 percent having university education. They are richer than the Ethiopian and Moroccan samples, with 52 percent owning a car and 68 percent having internet access at home. Ten percent report that they live in a household with a migrant abroad, which is similar to the national average of 10.7 percent of the population being emigrants (World Bank, 2011). The Philippines sample has an average age of 32, and is the most likely to be female (68 percent). In this part of the Philippines car ownership is rare (1 percent), although many households use motorcycles. Fourteen percent have an intermediate family member who is a current migrant, 8
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