rafmacalaba/datause-encoder-data huggingface dataset:
# **B.2 Tracking Weights**

In addition to the saturation weights, tracking weights are required to account for the randomized intensive tracking procedure used in round 5. These weights are only used for regressions containing data from round 5; regressions using data from rounds 1-4 only require saturation weights. We implemented this randomized tracking procedure in order to address attrition concerns, which we expected to be more severe two years after treatment. We describe below the details of the procedure and specify the tracking weights for round 5 data.

In round 5, 60 schools do not complete surveys despite being operational. We randomly select half of these schools to be intensively tracked, i.e. our enumerators make multiple visits to these schools to track down the respondent, and, if necessary, survey the respondents over the phone or at non-school premises. These efforts increase our round 5 survey completion rate from 88 to 94 percent. To account for the additional attention received by this tracked subsample, we assign a weight of 2 if the school was selected to be part of the intensively tracked subsample, and 0 if it was not.

24
**Figure 8. Share of population from bottom 40 percent across regions and type of welfare aggregate, Kazakhstan 2010**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>nominal deflated<br>Akmola Aktobe ***Almaty ***Atyrau West_Kaz **Jambyl Karaganda **Kostanay ***Kyzylorda ***Mangystau ***South_Kaz Pavlodar North_kaz **East_Kaz ***Astana_city ***Almaty_city<br><!-- End of picture text --> Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations. Notes: * significant difference in proportions test at 10 percent, ** at 5 percent, *** at 1 percent level.

In addition to changes in the distribution of the population in the bottom 40 percent, the choice of welfare aggregate can also affect the individual profile of the population in the bottom 40. However, as shown in table 4, the difference is small using the example of individual characteristics in Tajikistan. The impact may be stronger if people are very different across the fourth, fifth and sixth deciles where the largest reshuffling takes place.

**Table 4. Individual characteristics of bottom 40 percent in Tajikistan across type of welfare aggregate**

||b40,deflated welfare aggregate|b40,nominal welfare aggregate|
|---|---|---|
||**Education level, 15+**||
|Basic education|22.2|21.9|
|General secondary education|54.4|55.2|
|Special secondary education|8.7|8.6|
|Tertiary education|6.5|6.3|
|None/less than primary education|8.2<br>**Labor force status, 15+**|8.1|
|Employed|17.1|16.7|
|Self‐employed|23.6|23.3|
|Unemployed|5.0|4.9|
|Retired|9.0|8.8|
|Student|7.8|7.6|
|Other labor force status|37.6|38.8|
|Source: Microdataprovided byregional foc|alpoints,members of the Global PovertyW|orkingGroup. Authors’ calculations.|

Theoretically, spatial price adjustments have an impact on the welfare ranking of households and on growth rates of household income and expenditure. However, according to our empirical analysis, unless spatial adjustments were conducted inconsistently over time, the impact of spatial price adjustments on growth rates including those of the bottom 40 percent is minimal. However, carrying out spatial price 14
# **List of Acronyms**

|ATET|average treatment effect on the treated|
|---|---|
|CAE|Country Assistance Evaluation|
|CPIA|<br>Country Policy and Institutional Assessments|
|CS|<br>civil service|
|CSR|civil service reform|
|DAC|Development Assistance Committee|
|DEC|decentralization|
|DO|development objectives|
|DOD|debt outstanding and disbursed|
|DPI|database of political institutions|
|ED|<br>education (sector board)|
|ES|evaluation summaries|
|EM|evaluation memoranda|
|DPL|development policy loan|
|ENV|environment (sector board)|
|EP|economic policy (sector board)|
|FH|Freedom House|
|FPD|financial and private sector development (sector board)|
|FY|fiscal year|
|GDP|gross domestic product|
|GIC|gender and inclusive (sector board)|
|GNI|gross national income|
|HE|health (sector board)|
|IBRD|International Bank for Reconstruction and Development|
|ICR|implementation completion report|
|ICRG|International Country Risk Guide|
|IDA|International Development Association|
|IEG|Independent Evaluation Group|
|IL|investment lending|
|IP|implementation progress|
|ISR|implementation supervision report|
|LAC|<br>Latin America and the Caribbean|
|MDA|ministry, department, agency|
|MS+|“moderately satisfactory” or higher IEG outcome rating|
|MTEF|medium-term expenditure framework|
|ODA|official development assistance|
|OECD|Organisation For Economic Co-operation and Development|
|OED|Operations Evaluation Department|
|OLS|<br>ordinary least squares|
|ORAF|<br>Operational Risk Assessment Framework|
|OVB|<br>omitted variable bias|
|P4R|Program-for-Results|
|PCN|<br>project concept note|
|PCR|<br>project completion report|
|PDIA|<br>problem-driven iterative adaptation|
|PDO|project development objective|
|PEFA|public expenditure and financial accountability|
|PFM|public financial management|
|PPAR|project performance audit report|
# TABLE 610

||_Se_<br>IV<br>Psi<br>Fuions<br>-Dpend<br>(Va<br>Base<br>in|JCb<br>htofA<br> <br>et<br>Vanabl<br>lues in parent<br>d on Value-A<br>Domestic Pric|and T<br>b<br>Cobb<br>Doug<br>e<br>o<br>Log of**Con**<br>heses are sandar<br>dded<br>es|n-Mes:<br>lmz nmuton<br>**t**<br>Price Value<br>d errors)<br>Base<br>in|Added<br>d on Value-A<br>World Price|dded<br>s||
|---|---|---|---|---|---|---|---|
||1|2|3|4|_5_|6||
|Intercept<br>(% Senegalese Private)|4.604<br>(6.766)|1838<br>(6.977)|0.451<br>(1,789)|-0.766<br>(6.787)|18.830<br>(6.970)|0268<br>(1.800)||
|ownership||||||||
|% Selielese State|-1.342<br>(0.777)|2.133'<br>(0.883)||-1309'<br>(0.7&0)|2.161'<br>(0O8M)|||
|% French|-0423<br>(1387)|3.038'<br>(1336)||-0398<br>(1391)|-3.123<br>(1.33S)|||
|% *o-Lebanesc|-0.612<br>(0.814)|_-2.459_<br>(0.790)||45S1<br>(0.17)|-2.484<br>(0.789)|||
|% Other Foip|-2279<br>(5361)|2.658<br>(8.455)||-2.629<br>(5373)|2.902<br>(8.449)|||
|Trade Variables:||||||||
|Effective Rate of<br>Protection|0.401<br>(0.764)|-0.780<br>(1.063)|-0.232<br>(0208)|4.183<br>(0.767)|-1.440<br>(1.062)|0.863'<br>(0.209)<br>(Continue|O|
# **A Appendices**

## **A.1 Data: Share of Employment by Sectors**

The data on employment share by sectors have been taken from the WDI database which in turn are sourced from the ILO’s Key Indicators of the Labour Market database. The specific series for Agriculture, Industry and Services are _SL.AGR.EMPL.ZS_ , _SL.IND.EMPL.ZS_ and _SL.SRV.EMPL.ZS_ respectively. The annual data series for a country is often constructed using different sources for different years. However, as discussed before there are two serious concerns with regards to using the data as provided in the database. First, the source used for a particular year may not be representative with regards to geography, working age population, gender etc. Second, the source survey may have been conducted in a period that captures seasonal allocation of labor force rather than the usual allocation of labor across sectors. Both these factors are bound to interfere with estimation of sectoral gaps in productivity.

The challenge is to filter the spurious data out from the good data and to do this I proceed as follows. For each country, I first identify the years in which the employment share is constructed from a source that is free from any of the concerns listed above. For example, such a source should be nationally representative, should not exclude any age group within the working age population, corresponds to the sector of employment of workers’ usual activity etc. For the purpose of exposition, I name the filtered data points thus obtained as Consistent Data Points (CDP). In the next step, I identify which of the data sources for a particular country contains maximum CDP and term the series generated from this data source as the Longest Reasonable Series (LRS). In case the maximum CDP come from more than one data sources, I pick the one that corresponds to the most recent data. The LRS serves as the skeletal for including or excluding data points from other sources.

Next, I define an objective criterion to determine whether to include a specific data point or not. The motivation behind designing this criterion comes from that the fact that the movement of workers across sectors is a relatively slow process and it is unlikely that there would be large variations in sectoral shares year over year. Hence, inclusion of data points that amount to large deviations should be avoided. Yet, the process may be faster in some countries compared to others with the speed of transformation depending on several factors including where the country is at with respect to its development path. This means that using a common threshold for deviation across countries may not be the best strategy. To overcome this hurdle, I use different thresholds across countries where these thresholds are selected based on changes observed in the LRS. Specifically, the steps that determine the inclusion of a data point are as follows: 44
Policy Research Working Paper 6587

# **Abstract**

Can project evaluation methods be used to evaluate programs: complex interventions involving multiple activities? A program evaluation cannot be based simply on separate evaluations of its components if interactions between the activities are important. In this paper a measure is proposed, the total program effect (TPE), which is an extension of the average treatment effect on the treated (ATET). It explicitly takes into account that in the real world (with heterogeneous treatment effects) individual treatment effects and program assignment are often correlated. The TPE can also deal with the common situation in which such a correlation is the result of decisions on (intended) program participation not being taken centrally. In this context RCTs are less suitable even for the simplest interventions.

The TPE can be estimated by applying regression techniques to observational data from a representative sample from the targeted population. The approach is illustrated with an evaluation of a health insurance program in Vietnam.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at c.t.m.elbers@vu.nl and j.w.gunning@vu.nl.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Armenter and Koren (2014) show that trade data are very sparse, even for a large country such as the U.S., in that the typical number of shipments for a given firm-product-country in a year is often 1. Not surprisingly this is also true in Albania where 61 percent of the 716,225 observations in the data set at the importing firm-HS 6-digit product-origin country-year level are a single shipment, and the average number of shipments per observation is 2.9. However, note that in the estimating sample for the econometric analysis where the panel dimension of the data at the firm-HS 6-digit product-origin country level is exploited (through the calculation of first-differences) the median number of shipments per firm-product-country-year is 2 and the average is 5.<sup>29</sup> We retain single-shipment observations in our estimation of the first-difference equations, assigning 1 to the median inspection if the shipment was inspected and zero if it was not. Since single-shipment observations exhibit inherent variability in this regard (and allocation to the red channel of a single shipment cannot decrease trade because the shipment is already en route), it is likely that this treatment attenuates any evidence that we might find that reductions in inspections increase trade through reductions in time.

# **3.2 Descriptive Evidence**

In this section we present some descriptive statistics characterizing changes over time in the rates of physical inspection, in the days required to clear customs, as well as in compliance with customs regulations over the sample period. Figure 2 shows that over the sample period the share of Albanian import declarations allocated to the red channel for physical inspection declines dramatically, from 42.9% in 2007 to 11.9% in 2012.<sup>30</sup> The proportion of import declarations taking more than one day to clear customs is cut in half over the period, declining from 12.8% in 2007 to 6.8% in 2012. These changes take place in a context of moderate growth in imports as a share of GDP.

> 29 A firm-product-country importing just 1 shipment over the course of a given year is more likely to be a sporadic importer that will not repeat the transaction in the following year, in which case first-differences will not be defined for such firm-productcountry and thus it will not enter the estimating sample.

> 30 The actual number of import declarations allocated to the red channel also declines four-fold in absolute numbers from 2007 to 2012.

13
**Table 1. Sample characteristics by the availability of childcare**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||A: No child<br>or youngest<br>child is 6<br>years and<br>older|B: Youngest<br>child is 0–5<br>years and<br>childcare<br>available|C: Youngest<br>child is 0–5<br>years and<br>no childcare<br>available|ttest<br>(2) – (1)|ttest<br>(3) – (1)|ttest<br>(3) – (2)|
|**Individual characteristics**|||||||
|Average age|33.5|26.2|26.0|-7.3***|-7.5***|-0.2|
|Education|||||||
|No education|45.6|21.2|20.3|-24.5***|-25.4***|-0.8|
|Class 1 to 5|30.4|40.7|47.6|10.1**|17.1***|7.0|
|Class 6 to 8|14.7|19.6|17.3|4.9|2.6|-2.3|
|Class 9|2.4|10.1|7.0|7.7***|4.6*|-3.1|
|SSC and above|6.5|8.3|7.6|1.8|1.1|-0.7|
|Wear_burkha_to travel outside=1|31.1|31.6|27.2|0.5|-4.0|-4.4|
|**Household characteristics**|||||||
|Average head’s age|40.4|33.5|32.9|-6.9***|-7.5***|-0.6|
|Head is female=1|14.5|7.1|4.9|-7.3***|-9.6***|-2.2|
|Head’s education|||||||
|No education|54.0|38.7|31.2|-15.3***|-22.8***|-7.5|
|Class 1 to 5|23.9|29.2|32.4|5.3|8.5*|3.2|
|Class 6 to 8|12.0|15.2|22.8|3.2|10.7**|7.6|
|Class 9|3.1|8.6|4.6|5.5**|1.5|-4.0|
|SSC and above|6.9|8.2|8.9|1.3|2.0|0.7|
|HH receive international|2.0|0.4|1.8|-1.6**|-0.2|1.4|
|remittance=1|||||||
|Average log of HH annual|9.6|9.4|9.5|-0.1***|-0.1|0.1|
|expenditure per capita|||||||
|HH living standard index|-0.06|0.02|0.1|0.08|0.2|0.1|
|Number of children aged 6 to 14|0.8|0.7|0.5|-0.1|-0.2***|-0.1|
|years|||||||
|Number of working-age women aged<br>15–64 years|1.4|1.3|1.1|-0.1|-0.2***|-0.1*|
|Number of working-age men aged|1.4|1.1|1.1|-0.3***|-0.3***|-0.01|
|15–64 years|||||||
|Number of HH members aged 65+|0.07|0.1|0.03|0.06|-0.04**|-0.1***|
|**Regional characteristics**|||||||
|Dhaka City Corporation=1|87.3|88.5|84.2|1.2|-3.1|-4.3|
|Slum =1|14.3|14.8|21.3|0.4|6.9**|6.5*|
|Birthplace (regions)|||||||
|Central|53.4|53.1|54.3|-0.3|0.9|1.2|
|Northern|7.8|6.7|8.0|-1.0|0.2|1.2|
|Eastern|13.3|16.1|8.8|2.8|-4.5|-7.3*|
|Southern|25.5|24.0|28.9|-1.5|3.4|4.9|
|Observations|641|229|206|870|847|435|

Note:<sup>a.</sup> *** 1%, ** 5% and * 10% level of significance. b. Survey weight applied.

8
# **3. A Nutritional Intervention Aimed at Protecting Young Children**

The goal of supplementary feeding program was to maintain and to improve the nutritional status of children under five, particularly those at risk as a result of the 199798 economic crisis.<sup>8</sup> The program was targeted through a two stage process. First, central and regional governments decided which communities or villages would receive the program, and then determined the timing of implementation within communities. Capacity constraints likely affected the timing of program rollout. Districts differed in their capacity to roll out the PMT program, and communities differed in the availability of staff to administer it. As a result, it is not surprising to find differences in duration of program exposure across communities.

Funding was distributed to local public health clinics, the _Pusat Kesehatan Masyarakat (Puskesmas_ ), and a list of children eligible to receive supplementary food was then prepared by the village midwife in consultation with other village leaders. As with other community health programs in Indonesia, program supervision was typically the responsibility of the village midwife if one was present. If a midwife was not available in the community, responsibility was assumed by the _Puskesmas_ .<sup>9</sup> Descriptive information from the IFLS show that in 62 percent of communities, the village midwife administered the program, and in majority of the rest (35 percent of sampled communities), the program was supervised by a member of the Puskesmas staff. Others may have been involved in preparing program’s recipient list, including the

> 8A monthly supplementary feeding program took place prior to the crisis as part of a public health program. In contrast to post-crisis supplementary feeding program, the pre-crisis program was much less intensive and distributed through the Village Integrated Health Post ( _Pos Pelayanan Terpadu_ , _Posyandu)_ . Under this program, food supplements were delivered along with some basic health services to preschool age children and pregnant women residing in the community.

> 9Village midwives are trained to provide basic health services in communities or villages. Their work is coordinated and supervised by the head of _Puskesmas_ whose scope of services include a _kecamatan_ (an administrative area intermediate in size between the district and the village).

7
<!-- Start of picture text --> Male youth not employed baseline: out of labor force later<br>05<br>Oo : 0048<br>E -.0072 0052 -.0054<br>3<br>288<br>8 $-056<br>8<br>Ss + -.088<br>fo}a4 + a<br> Pre-covid cohort<br>“15 ~ 1s @ Covid cohort<br>4 months later*** 8 months later*** 12 months later*** 16 months later***<br><!-- End of picture text --> <!-- Start of picture text --> Female youth not employed baseline: out of labor force later<br>05<br>' .026 * 027<br>> 6 ’ ore 2 071<br>E -01<br>3 -024 -025<br>2 + -.038<br>2-058<br>8<br>Ss<br>Oo3 -A<br> Pre-covid cohort<br>“45 ® Covid cohort<br>4 months later*** 8 months later*** 12 months later*** 16 months later***<br><!-- End of picture text -->
Table 9: Population Size and Probability of Engaging in Self-employment by Migrant Status (LFS)

|||OLS|||2SLS||
|---|---|---|---|---|---|---|
||(1)<br>All|(2)<br>Exclude<br>rural migrant|(3)<br>Exclude<br>any migrant|(4)<br>All|(5)<br>Exclude<br>rural migrant|(6)<br>Exclude<br>any migrant|
|Ln Pop|-0.058<sup>_⇤⇤⇤_</sup>|-0.045<sup>_⇤⇤⇤_</sup>|-0.052<sup>_⇤⇤⇤_</sup>|-0.039<sup>_⇤⇤⇤_</sup>|-0.020<sup>_⇤⇤_</sup>|-0.030<sup>_⇤⇤_</sup>|
||(0.006)|(0.006)|(0.009)|(0.009)|(0.009)|(0.013)|
|Addis Ababa|Yes|Yes|Yes|Yes|Yes|Yes|
|Control|Yes|Yes|Yes|Yes|Yes|Yes|
|N|46,914|31,638|18,370|46,914|31,638|18,370|

Note: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. The estimation includes control variables for demographic: gender, age (ten years increment), marital status, and household size (and square term) and education: no education, incomplete primary, complete primary, incomplete secondary, complete sec- ondary, postsecondary, and adult education. No migrant from rural excluding rural migrant, but include migrations between urban areas.

Table 10: Summary of 2SLS Estimation: Market Accessibility Index (MAI)

||Self-Emp|loyment|Formal:|License|Underem|ployment|Nominal|Earnings|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||OLS|2SLS|OLS|2SLS|OLS|2SLS|OLS|2SLS|
|MAI|-0.050<sup>_⇤⇤⇤_</sup>|-0.109<sup>_⇤⇤⇤_</sup>|0.021<sup>_⇤_</sup>|0.063<sup>_⇤⇤⇤_</sup>|-0.018<sup>_⇤⇤_</sup>|-0.047<sup>_⇤⇤⇤_</sup>|0.064<sup>_⇤⇤_</sup>|0.164<sup>_⇤⇤⇤_</sup>|
||(0.019)|(0.015)|(0.013)|(0.011)|(0.009)|(0.007)|(0.026)|(0.023)|
|Control|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Underidentifcation||3.53||3.63||3.53||3.72|
|Weak identifcation||205.47||201.38||205.47||76.45|
|N|69,571|69,527|65,055|65,012|69,571|69,527|19,577|19,576|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. The estimation includes control variables for demographic: gender, age (ten years increment), marital status, and household size (squared term) and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education.

21
**Table O2: Point estimates corresponding to figure 2**

|Dependent variable|1(Work)|Days<br>worked<br>last week|Hours<br>worked<br>last week|
|---|---|---|---|
|Other worker sick X Choose 3.00/3.00|0.266***|1.499**|16.243**|
||[0.060]|[0.614]|[7.361]|
|Other worker sick X Choose 2.70/5.70|0.054|0.434|2.580|
||[0.076]|[0.379]|[4.064]|
|Other worker sick X Choose 2.40/7.20|‐0.028|‐0.593|‐4.260|
||[0.049]|[0.685]|[5.612]|
|Other worker sick X Choose 1.80/9.00|0.061|0.108|2.630|
||[0.049]|[0.375]|[2.748]|
|Other worker sick X Choose 1.00/11.00|0.031|0.035|1.456|
||[0.117]|[0.658]|[6.002]|
|Other worker sick X Choose 0/12.00|0.082|0.162|7.144|
||[0.110]|[0.830]|[10.501]|
|Female X Other worker sick X Choose 3.00/3.00|0.109|0.588|1.529|
||[0.083]|[0.869]|[10.133]|
|Female X Other worker sick X Choose 2.70/5.70|‐0.045|0.218|3.390|
||[0.077]|[0.660]|[7.599]|
|Female X Other worker sick X Choose 2.40/7.20|‐0.095|‐0.236|0.439|
||[0.079]|[0.899]|[7.877]|
|Female X Other worker sick X Choose 1.80/9.00|‐0.066|‐0.398|‐3.080|
||[0.080]|[0.520]|[4.283]|
|Female X Other worker sick X Choose 1.00/11.00|‐0.087|‐0.597|‐4.785|
||[0.114]|[0.926]|[8.772]|
|Female X Other worker sick X Choose 0/12.00|‐0.074|‐0.124|‐3.709|
||[0.110]|[1.206]|[13.993]|
|Observations|3,951|3,948|3,948|
|R‐squared|0.508|||

_Notes: Other worker sick = 1 if another household member reported missing work for an entire week that she/he was planning to work that week and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. All specifications include individual fixed effects. Column 1 estimated by OLS and columns 2 and 3 least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include week‐of‐interview dummies interacted with gender city X week‐of‐ interview dummies, and controls for the number of household members planning to work in that round interacted with gender.. Standard errors in brackets: *** p<0.01, ** p<0.05, * p<0.1._