rafmacalaba/datause-encoder-data huggingface dataset:
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Box 2. Understanding the differences between JMP and government data**

The AICD uses the Joint Monitoring Program (JMP) coverage statistics as the main source of access data on water supply and sanitation, and proceeds under a standardized methodology to allow cross-country comparisons. These data might differ from those reported by governments. Whereas the JMP data are based on household surveys and therefore reported by users of the services, the government data are based on utility reports. This implies that there is a time lag between output data (provider) and outcome data (users). Other underlying factors explaining potential differences are the definition of what technologies constitute improved access to water supply and sanitation, and the JMP’s use of several household surveys vis-à-vis the use of a single data point by several governments. Therefore, the conclusion on progress toward the MDGs might differ according to the data source used.

_<u>Source:</u>_ Adapted from AMCOW (2010).

**Table 11. Benchmarking water and sanitation indicators**

||Unit|Low-income<br>countries||**Mozambique**||Middle-<br>income<br>countries|
|---|---|---|---|---|---|---|
|||**Mid-2000s**|**1997**|**2003**|**2008**|**Mid-2000s**|
|Access to piped water|% pop|10.5|7.0|8.0|8.7|52.1|
|Access to standposts|% pop|16.2|19.0|20.6|16.7|18.9|
|Access to wells/boreholes|% pop|38.3|47.0|54.7|59.0|6.0|
|Access to septic tanks|% pop|4.9|4.4|2.6|5.5|40.8|
|Access to improved latrines|% pop|9.9|10.0|14.2|15.5|1.4|
|Access to traditional latrines|% pop|50.1|23.4|31.5|38.3|30.4|
|Open defecation|% pop|40.3|61.5|51.7|40.1|14.3|
||||**2002**|**2006**|**2009**||
|Domestic water consumption|liter/capita/day|72.4|33.3|37.0|—|165.9|
|Revenue collection|% sales|92.7|61|71|90|100.0|
|Distribution losses|% production|34.3|55|56|45|26.8|
|Cost recovery|% total costs|56|35|32|57|81|
|Operating cost recovery|% operating costs|65|65|51|88|145|
|Labor costs|connections per<br>employee|159|104|137|—|369|
|Total hidden costs as % of revenue|%|163|294|225|113|140|
|||**Mo**|**zambique**|Coun|tries with|Other|
|||||scar|e water|developing|
|||**Mid-2000s**|**Late 20**|<br>res<br>**00s**|<br>ources|<br>regions|
|Residential tariff|U.S. cents per m3|3|2|**64**|60.26|3.0–60.0|

_Source:_ Demographic and Health Survey and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). Access figures from DHS surveys (1997 and 2003) and MICS Survey (2008).

Utilities figures are the weighted average by water production of the following utilities: Beira, Maputo, Nampula, Pemba, and Quilimane. — = Not available.

Mozambique’s reforms of the water and sanitation sector attracted about $350 million in investments between 2007 and 2008. This has allowed for enhancing the level of service in cities served by the holding company. Hours of supply increased from 11 to 16 on average between 2002 and 2006, which has led to an increase in the domestic water consumption from 33.3 to 37 liters per capita in the same 27
TABLE A.6 Intent-To-Treat Effects on Student Test Scores, Breakdown by School Type and Subject

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||All|school ty|pes|P|anel A: S|Pri<br>tudent te|mary sch<br>st score d|ool<br>ata measu|red at _Y_|2|Junior|secondary|school||
||Math|Science|Indon.|English|Pooled|Math|Science|Indon.|English|Pooled|Math|Science|Indon.|English|Pooled|
|Treatment efect|0.00|-0.01|0.00|-0.03|-0.01|0.04|0.03|0.04|_d.n.a._|0.03|-0.05|-0.05|-0.04|-0.03|-0.04|
||(0.03)|(0.03)|(0.02)|(0.05)|(0.02)|(0.03)|(0.03)|(0.03)||(0.02)|(0.05)|(0.04)|(0.03)|(0.05)|(0.04)|
|Observations|79,510|79,373|79,510|40,673|279,066|38,700|38,700|38,700||116,100|40,810|40,673|40,810|40,673|162,966|
|_R_<sup>2</sup>|0.28|0.29|0.28|0.34|0.28|0.25|0.28|0.27||0.25|0.33|0.32|0.30|0.34|0.31|
||Math|Science|Indon.|English|P<br>Pooled|anel B: S<br>Math|tudent te<br>Science|st score d<br>Indon.|ata measu<br>English|red at _Y_ <br>Pooled|3<br>Math|Science|Indon.|English|Pooled|
|Treatment efect|0.01|0.02|0.02|-0.03|0.01|0.04|0.04<sup>_∗_</sup>|0.03|_d.n.a._|0.03|-0.03|0.01|0.00|-0.03|-0.01|
||(0.03)|(0.03)|(0.02)|(0.05)|(0.03)|(0.03)|(0.03)|(0.03)||(0.03)|(0.05)|(0.04)|(0.04)|(0.05)|(0.04)|
|Observations|78,164|78,126|78,164|40,539|274,993|37,587|37,587|37,587||112,761|40,577|40,539|40,577|40,539|162,232|
|_R_<sup>2</sup>|0.26|0.25|0.21|0.34|0.24|0.23|0.23|0.22||0.22|0.29|0.27|0.21|0.34|0.26|

_Notes:_ The table reports intent-to-treat effects on student-level test scores. Estimates are reported separately for _Y_ 2 (panel A) and _Y_ 3 data (panel B). Test score data are constructed by first standardizing by subject-grade-year (so that mean and variance in the control group are 0 and 1 respectively), then stacked so that the unit of observation is student-subject-year. These test scores are then regressed on a dummy variable indicating a treatment school. The estimated parameter on the treatment dummy is reported in column (1) and (2). The regression model further includes district-triplet fixed effects (the strata used for randomization), baseline standardized student-level test scores, baseline standardized averaged school-level test scores. For observations for which baseline test scores are not observed, the baseline values are set to zero. Two dummy variables indicating observations for which individual baseline test scores or school averaged baseline scores are not observed, are also included in the regression model. Weights are applied to scale the student-subject level data back to the level of the student (only for the pooled results of column (5), (10) and (15)). School-level clustered standard errors reported in parentheses.
savings at NVRB. Column 3 also shows that the treatment effect on net deposits dropped to zero during the follow-up, post-commitment period. This is not surprising, since many participants withdrew their SSP savings during this period. Interestingly, in the post-survey, post-commitment period the treatment effect on net deposits (GHS 32.5) exceeds the commitment period treatment effect. This suggests that SSP had persistent effects on savings behavior at NVRB, subject to the caveat that the point estimate is not significantly different from zero.

Finally, column 4 shows that SSP generated a significant and highly persistent increase in NVRB balances: the average daily balance during the commitment period increased by GHS 244, and remained GHS 140-141 higher during the two post-commitment periods. These treatment effects are large relative to control groups means ranging from GHS 90-111 across the three focal time periods.

# **3.3 Overall Effects: Results from Survey Data**

We now turn to the survey data to assess whether impacts on NVRB balances reflect new savings or crowd out. We use a regression specification that mirrors equation 1, except _t_ now references survey round, the interaction between treatment and the post-commitment, post-survey period is dropped, and _yi_ 0 references values measured at the baseline survey.

The first two columns of Table 3 present treatment effects on NVRB savings. The first column uses the same administrative data used in the earlier section, but focuses on NVRB savings on the day of the relevant survey. In order to make the measure comparable with survey reports, negative NVRB account balances are coded as zero NVRB savings. The second column studies savings based on respondent self-reports. Both variables paint a similar picture: SSP more than doubled NVRB savings during the commitment period. The administrative data also show a marginally significant impact on NVRB savings in the post-commitment period, while the point estimate for the survey data is smaller and not significantly different from zero. Columns 3 and 4 show that SSP had no significant impact on other formal or informal savings.

Individuals may also offset commitments by taking on more debt. Columns 6 and 7 of Table 3 study SSP’s impacts on debt.<sup>13</sup> Here, we find a significant, 7.6 percentage point (40 percent) increase in the likelihood of taking on new debt during the commitment period. This could reflect increased financial strain driven by the regular monthly commitment.<sup>14</sup> Recall that we do not see any parallel impacts on the overdraft rate or overdraft fees at

> 13Appendix Table A4 shows impacts on debt by source.

> 14We do not, however, find that SSP increased self-reported markers of financial strain, like food insecurity and difficulty paying bills (Appendix Table A5).

9
# **Table 19a.  Impact on Output by Sector and Regional Market of WTO Accession and Simultaneous Emissions Intensity Standards to Reduce Carbon Emissions by 20 Percent Impact. (Results are Percentage Change from Base Year)**

|**Good**|**`Central`**|**`Siberia`**|**`South`**|**`North`**|**`Urals`**|**`Far East`**|**`Moscow`**|**`St. Pete.`**|**`Tumen`**|**`Northwest`**|
|---|---|---|---|---|---|---|---|---|---|---|
|**`FME`**|18.1|6.6|23.3|17.2|5.7|19.9|28.5|210.6||5.7|
|**`CHM`**|9.7|22.0|10.2|2.6|11.6|6.4|7.2|12.8|14.5|31.7|
|**`TMS`**|12.1|7.3|11.7|18.8|-0.8|20.0|-2.0|19.8|8.5|20.0|
|**`NFM`**|8.6|4.7|19.6|10.3|15.2|8.8|31.0|4.0||-10.6|
|**ELE**|10.7|8.6|9.9|9.1|8.7|9.1|8.0|10.7|3.3|14.5|
|**`TRK`**|5.2|2.8|4.7|7.2|0.4|8.8|0.6|9.3|3.9|9.7|
|**TRD**|2.4|2.0|2.3|2.9|1.6|3.3|1.8|4.0|3.6|4.7|
|**`SCI`**|1.8|2.0|0.3|1.9|-0.2|6.1|1.3|8.1|-6.2|7.4|
|**PST**|1.7|1.3|2.1|2.6|0.5|3.0|1.0|2.3|1.8|2.3|
|**`CRU`**||0.9|1.6|2.1|0.7|2.5|||1.4|0.7|
|**`OIL`**|3.9|-0.4|1.9|3.6|-0.5|5.9|-1.8|0.0|-0.8||
|**HOU**|1.2|0.9|1.5|1.6|0.4|1.8|1.1|1.6|-1.1|1.8|
|**HEA**|1.0|0.9|1.1|1.0|0.8|1.1|0.9|1.2|0.3|1.3|
|**`RLW`**|1.0|-1.0|-0.6|-0.3|-1.0|0.3|-1.3|3.2|-2.0|1.4|
|**`CON`**|-0.1|-1.1|-0.2|-0.3|-0.5|0.1|1.3|-1.9|-0.5|-4.0|
|**`COL`**|-0.9|-1.7|-1.0|0.6|-2.5|0.8|||||
|**`MWO`**|-5.0|-1.8|-1.1|-1.2|-6.3|2.5|7.7|2.9|-4.4|-5.6|
|**`OTI`**|-1.8|-4.1|-1.5|-1.4|-2.7|-1.8|1.6|-0.4|-6.4|-3.3|
|**CLI**|-1.1|-2.7|-1.5|-1.2|-2.1|-1.1|-2.0|-3.7|-2.6|-5.0|
|**`AGR`**|-2.1|-2.7|-2.1|-3.0|-1.2|-3.4|-1.4|-6.8|-2.8|-8.9|
|**`CNM`**|-2.8|-5.3|-2.2|-1.8|-3.6|-1.9|-4.9|-5.5|-0.6|-8.0|
|**`TPP`**|-7.8|-9.2|-12.0|-2.7|-4.2|7.3|-12.9|-8.6|9.0|-11.2|
|**`FIN`**|-5.7|-6.3|-5.7|-6.1|-6.5|-5.5|-5.9|-5.1|-8.4|-4.3|
|**`OTH`**|-4.4|-7.7|-5.8|-5.5|-3.9|-7.0|-2.6|-10.3|-3.6|-14.9|
|**`FOO`**|-9.3|-8.3|-8.0|-5.6|-7.8|-8.2|-11.9|-9.0|-4.2|-12.6|
|**`TRO`**|-6.3|-11.3|-8.3|-3.4|-16.9|-1.5|-19.9|-1.2|-15.6|-0.5|
|**`MAR`**|-12.7|-15.7|-15.0|-12.0|-12.0|-10.6|-15.0|-12.8|-15.1|-15.1|
|**`PIP`**|-15.0|-13.0|-11.0|-11.7|-9.9|-10.5|-24.8|-13.6|-3.3|-28.4|
|**`GAS`**||-20.5|-17.3|-18.2|-19.4|-17.8|||-21.2|-26.0|
|**`AIR`**|-21.7|-22.0|-21.5|-22.5|-20.5|-22.6|-19.5|-24.0|-23.3|-23.9|

See table 1 for sector codes. **Source: Authors’ estimates.** 62
World Bank study. The two models differ in the captured heterogeneity in risk of infection and in the intent. Since the pandemic in 1918 had unique features leading to a severe impact, when constructing a model for a high risk Asian country it is important to base it on Asian experience in 1957 and 1968 (as in Longini _et al._ for SE Asia). In general, some parameters are exogenously imposed constants not varied in modeling. Varying parameters allow sensitivity to the modeling assumptions to be tested, necessary given the uncertainty in estimates, e.g., of contact rates. The model should also be portable, with important variables generated endogenously rather than assumed.

13
dimension and very slow progress over time: 84 percent of people are deprived in this dimension in 2011 and only 8 percent improvement was recorded between 2000 and 2011.

Finally, unlike the MPI, the approach used in this study does not easily allow crosscountry comparisons. The MPI allows the comparison on a broad range of dimensions in one index, and it usefully draws attention to the further need for progress in Ethiopia. However, using the aggregate measure alone as a statement about the level of poverty and changes in poverty over time does not reflect the full reality.

# **6. Conclusion**

This study analyzed multidimensional poverty in Ethiopia focusing on selected dimensions of education, health, culture and living standards. It used nationally representative data from the Household Consumption and Expenditure and Welfare Monitoring surveys implemented in 2000, 2005 and 2011. Single and multiple deprivation analyses are conducted on 11 multidimensional poverty indicators. The single deprivation analysis investigated levels and trends taking one indicator at a time. The multiple (overlap) analysis used Venn diagrams to analyze changes in multiple deprivations. Each Venn diagram represented a set of three deprivation indicators. This approach is graphically compelling to view the trends in multidimensional poverty.

The single deprivation analysis reveals that rural areas have seen large reductions in different dimensions of poverty, but the extent of deprivation incidence is still higher in rural areas. The movements of most of the indicators over the 2000-2011 period show considerable improvement in education, health and living standards dimensions. This confirms the positive story of monetary poverty reduction and considerable GDP growth presented in other analyses and official statistics. This indicates a continued emphasis in successful execution of pro-poor 25
unskilled manual workers to facilitate self-selection of the poorest households into the program.<sup>9</sup> Where there are more applications than worker slots a household categorical criteria is expected to be used to select participants: households whose heads are manual laborers and own less than 0.5 acre of land<sup>10</sup> are eligible to apply. Beyond that there are no clear guidelines on how rationing of worker slots will happen if the program is oversubscribed in each Union.<sup>11</sup> Workers are selected on an annual basis based on fresh applications at the local level. In each phase, an enrolled participant is entitled to receiving up to BDT 8000, or about $104, or a little over $200 if work if taken up in both seasons for the full number of days. This implies that on a monthly basis, over the course of a year, an EGPP worker can receive a maximum of monthly transfer of BDT 1300 (USD 16), or about 15 percent of the estimated average monthly per capita expenditure of the targeted extreme poor population who are expected to consume at around the lower national poverty line estimated for 2015.<sup>12</sup> The nature of the works supported under the program are locally identified and implemented. Ministry level officials with the help of local government officials implement the program.

For the purpose of this paper, it is also important to understand the political context within which programs like EGPP are implemented in rural Bangladesh. While not widely documented with evidence, it is generally understood that both informal and formal social protection is provided through local patronage politics (Ahmed et al, 2004). The role of local politicians in beneficiary selection is encouraged by the lack of formal national targeting system and weak administrative capacity of many safety net programs (World Bank, 2008 and 2013). Local politicians play a particularly crucial role in preparing beneficiary lists, and sometimes in the delivery of benefits as well (Ibid). Such a context allows for an ideal setting to study the links between politicians and participation in PWPs, and the impact of participation on household poverty.

> 9 Currently the wage is below the market rate at BDT200 per day. This rate is adjusted for inflation over time.

> 10 These two variables are highly correlated to poverty in rural Bangladesh. The definition of functionally landless in Bangladesh is landownership of less than 0.5 acre.

> 11 Union is the lowest local administrative unit in rural Bangladesh.

> 12 Calculations are based on the 2010 national poverty line (adjusted for inflation). The benefit size is almost 2.5 times the average benefit offered by other safety net programs in Bangladesh.

8
**Figure 1: Oaxaca Decomposition [Low-skilled Employees only]**

<!-- Start of picture text --> 1<br>.8<br>.6<br>.4<br>.2<br>0<br>2 3 4 5 6 7<br>lhwage<br>c.d.f. of female c.d.f. of male<br>Cumulative Probability<br><!-- End of picture text --> Source: World Author’s estimates using 2006 Macedonia LFS data.

14
12 degree days were practically the same in 2017 and in 2018, the two years that we examine in detail below.

In addition to documenting expenditures, income and government transfers, the HBS does contain information about housing tenure, the structural characteristics of dwelling, and some of the appliances therein. For example, we know if the home is served by piped natural gas (gascentr=1), district heating (heating=1), is equipped with a gas water heater (gaskol=1), or has an electric stove (elektrpl=1). The exact nature of the heating system however must be inferred. We assume that if the household has district heating (heating=1) but is also served by piped natural gas, then the latter is used for either cooking or for heating water (if gaskol=1), or both.

If a home is connected to the natural gas network and zero district heating expenditure is reported, then we assume natural gas is used for space heating, for heating water (unless gaskol=0), and for cooking (unless elektrpl=1). The data sets also specify whether the dwelling is on a centralized, or on a separate and independent, heating system.

# _3.B. Homes and Heating_

Homeownership (92%) was stable over 2014-19, as were the shares of households living in single-family homes (52%), or in units in multi-family buildings (41%). The exact size of the dwelling is only available in the 2014 and 2016 HBS; Ukrainian families live in and must heat on average about 40 square meters. The shares of the samples served by the different types of fuels or heating are likewise very stable over the 6 years. About three-quarters of the sample are served by piped natural gas. About 31% of the households are served by district heating. About 45% of the sample uses piped natural gas for heating, 15% uses piped natural gas to feed its water heater, and only 5% uses electric cooking stoves.
by country pair. Imports in 2016 come from UN Comtrade<sup>3</sup> and information on PTAs in force during the same year comes from the new World Bank data set on the of content PTAs (Hofmann et at, 2017).<sup>4</sup> ITC database description The ITC Market Access Map database includes customs duties at the national tariff line code (NTLC) for 201 reporters and faced by 239 partners under MFN, non‐MFN and preferential regimes and tariff rate quotas. The database is continuously updated with tariff data that ITC collects directly from national authorities such as customs offices, ministries and other governmental institutions. When the national sources cannot provide ITC with the preferential rates under a preferential trade agreement that is known to be in force, then ITC obtains the missing information from the tariff phase‐out schedules of the agreement to complement.

The ITC database contains pre‐calculated ad valorem equivalents (AVE) for non‐ad valorem duties and tariff rate quotas (TRQ) (Table 1).

Table 1: Non‐ad‐valorem tariffs and ad‐valorem equivalent composition

|NAV tariff category|Example|Final AVE composition|
|---|---|---|
|**_Specific tariff_**<br>**_Compound tariff_**|$2 per kg<br>10% plus $2 per kg|AVE of the specific tariff<br>Ad valorem component added to (or subtracted from) the AVE of the<br>specific component|
|**_Mixed tariff_**<br>**_Tariff rate quota_**|30% or € 2 per kg, whichever<br>is highest<br>5% for imports within quota<br>and 20% for out of quota<br>imports|AVE of the specific component subject to the conditional choice<br>expressed in the tariff<br>AVE depends on the real volume of imports in the year of reference. The<br>marginal level of protection of a TRQ consists of the average of the inside<br>and outside tariff rates if the import volume is less than or equal to 80%<br>of the contingent, or the outside tariff if beyond|
|**_Technical tariff_**|9% on dairy spreads with a fat<br>content between 39% and<br>60%|Not calculated due to a lack of information on technical product<br>specifications|

Source: ITC Market Access Map methodology ‐ User guide AVEs express non‐ad valorem tariffs in percentage terms as follows: Where SP is the monetary value of duty per unit of imports; UV is the import unit value that is calculated as the ratio between the value of imports (V) and the quantity of imports (Q); XR is the currency exchange rate when appropriate. The accuracy of the AVEs depends on the UV estimates, which are sensitive to variations in the data. ITC’s strategy to select the most accurate UV estimates is schematized in Appendix figure A1 and the entire calculation process is detailed in the World Tariff Profiles 2006.<sup>5</sup>

> 3 TRAINS and COMTRADE information is taken from the World Integrated Solution (WITS).

> 4 The data set is available at <u>http://data.worldbank.org/data‐catalog/deep‐trade‐agreements.</u>

> 5 See World Tariff Profiles 2006, pages 186 ‐197.

4
_Table 9: Average Relative Bias, Average correlation, Root Mean Squared Error, and Coverage rate of target area estimates of headcount non-monetary poverty, by country and method_

|Accuracy and<br>Coverage rate|Sri Lan<br>|kan subdistric<br>|ts<br>||Tanza<br>|nian districts<br>|||
|---|---|---|---|---|---|---|---|---|
||ARB|Correlation|RMSE|CR|ARB|Correlation|RMSE|CR|
|Direct survey<br>estimates|||||||||
|H-T|0.257|72.5|0.049|82.2|0.043|77.0|0.088|85.5|
|EA-Clustering|0.257|72.5|0.049|76.1|0.043|77.0|0.088|76.1|
|<br>Area level Fay-<br>Herriot model|0.395|84.1|0.026|91.8|0.071|88.3|0.052|91.8|
|Household-<br>level EB model|0.293|87.6|0.034|84.3|0.062|87.6|0.053|74.8|

_Notes: ARB refers to average relative bias, which is the average ratio of the difference between estimated and census poverty rates to the census poverty rate. Correlation refers to the unweighted correlation across areas between estimated and census non-monetary poverty rates. RMSE refers to root mean squared error, which is the square root of the average squared difference between estimated and census poverty rates. CR stands for coverage rate, which is the share of areas for which the estimated 95 percent confidence intervals for the area non-monetary poverty rate contains the census non-monetary poverty rate. Point estimates have been rescaled to match direct survey estimates at the regional level and mean squared error adjusted accordingly. H-T refers to the Horvitz-Thompson approximation, while EA clustering refers to standard variance estimates clustered by enumeration area._ Figures 2 and 3 give a detailed look at the point estimates and mean squared errors for the area poverty estimates for each method. Sri Lanka is shown on the left panel and Tanzania on the right, and in each country the areas are ordered according to their non-monetary poverty rate in the census. The results clearly show that both Fay-Herriot and Household-level models greatly improve on the accuracy of the direct estimates, especially in areas with higher poverty rates. In Sri Lanka, many of the direct estimates are zero, even in high-poverty areas. The comparison between household models and Fay-Herriot models is less clear, but the Fay-Herriot estimates appear to be more prone to overestimate poverty for low-poverty areas and underestimate poverty for high-poverty areas.<sup>29</sup> Figure 3 presents the mean squared errors produced by the different methods. It clearly demonstrates that the household model estimates are substantially more precise than both the Fay-Herriot estimates and the direct estimates in both countries.

> 29 As described above, the Fay-Herriot estimates use the predicted poverty rate from the model in areas where no sampled households are poor.

26