rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Average School Achievement and Years in PEC, Evaluation Sample

|Intensity|2005|2006|2007|2008|
|---|---|---|---|---|
|0 PEC|563|560|557|560|
|1 PEC year|491|501|496|504|
|2 PEC years|515|527|525|529|
|3 PEC years|499|508|508|504|
|4 PEC years|507|518|515|524|
|5 PECyears|500|513|510|511|

Source: ENLACE and Evaluation roster

# **EMPIRICAL STRATEGY**

We follow two alternative methodologies to identify the effects of PEC on student-level test scores: an experimental design exploiting the random assignment of schools to the treatment and control groups and a differences-in-difference (DD) approach exploiting post-treatment differentiated performance on test scores between PEC and non-PEC schools. In both methodologies, schools are the unit of intervention and the unit of analysis is the student.

# **Experimental Design**

Following the experimental design of the impact evaluation of PEC-Colima, the randomization of the program establishes a causal linkage between PEC (treatment) and school performance (outcome). The counterfactual is a group of comparison, which simulates what would happen to the target group in the absence of the intervention. A randomized experimental evaluation reduces the possibility that observed changes in outcomes in the intervention group are due to exogenous factors. Therefore, random selections of schools receive treatment and the remaining schools serve as controls. This process ensures a robust counterfactual measure of the causal effect of the intervention. The intervention consisted in two steps; the first step was the application process to get the PEC program. The second stage was the randomization among those schools that applied to the program. The evaluation selected 50 schools as treatment and 50 as control. The first intervention was winning the grant. This included two schools with two teachers (one control and one treatment), which were taken out from the analysis. The second part of the intervention was the implementation of the school plan given the grant.

Using empirical design from a randomized experiment framework helps us to create homogeneous groups in the observed and unobserved characteristics at the initial stage of the analysis. Consequently, this reduces bias and allows the greatest reliability and validity of statistical estimates of the treatment effect. To test for theoretical prediction, this paper first estimates the differences between the control and treatment groups. This follows the functional form:

# Score�� �β� �β��treatment����β��SchoolInputs����β��SES����∈��

where score is the test score for school _s_ in year _t._ The variable _Treatment_ is a dummy variable in which 1 indicates that the group received the second component. The coefficient, β�, is of interest.

17
# 6 Conclusions

The purpose of this study was to compare three approaches to model linkages between a macroeconomic model with representative households and micro household income data to evaluate the distributional and poverty effects of poverty reduction strategies and other exogenous shocks. The three methods were evaluated by performing a set of simulations with Mini-IMMPA, a disaggregated dynamic computable general equilibrium model that can readily be linked to a household survey, and two typical labor market policies: a cut in the minimum wage and an increase in employment subsidies for unskilled labor. The results of these simulations were discussed and the three methods compared.

The distributional and poverty effects indicated by the three approaches are not fundamentally different, they differ neither in the direction of the effects nor in the ranking of the household categories with respect to poverty. However, from both a conceptual and practical point of view, it is tempting to view the micro-accounting method combined with reweighting for changes in the employment structure as constituting the most appealing method among the three, despite the fact that it has its own shortcomings. The reason is that the simple micro-accounting method ignores changes in the employment structure, whereas the distribution approach relies on approximate, instead of real, income distributions and depends therefore on the quality of the corresponding estimates of the shape parameters.

Our results indicate that the two other methods, and especially the Beta distribution approach, induce significant differences concerning the magnitude of poverty changes. In addition, as noted earlier, the distribution approach may not be adapted if one is interested in the exact level of poverty and not only in changes relative to the baseline. Of course, the problem of predicting the initial levels might partly be due to the fact that we try to model with Beta distributions our artificial household data generated by log-normal distributions. The Beta distribution approach might work better with real household data. However, the nonparametric method, that is, the extended micro-accounting framework, can readily be used with actual survey data. Given the performance of standard micro-computers and statistical software nowadays, there is really no need to use the distribution approach, except perhaps for very large household surveys. However, economists who advocate this approach suggest using it if the sample of households is small, 30
57. **Despite the rebound, lingering impacts are still felt on employment and incomes of poorer and urban households.** While employment and income impacts were widespread, those already economically insecure prior to the pandemic were hit harder. Among primary breadwinners, household heads with lower education were initially more likely to experience work stoppages (Purnamasari et al 2020). Those and households with young children more often reported an income shock. Two years into the pandemic, these households – as well as households in the bottom 40 percent – were recovering more slowly than other households. The lack of resilience of bottom 40 households exacerbates the impact, and, hence, pre-existing inequalities.

# **5. Conclusion**

58. **Informality trends observed in EMDEs were upheld in Indonesia to a large extent.** Like in other EMDEs, Indonesia witnessed a general decline in informality during the past few decades but it remains elevated and broadly above comparator countries. At 35 percent of its output and 54 percent of its employment, informality remains an important development challenge for Indonesia. First, informality in Indonesia is mostly concentrated in the agriculture and low-skilled service sectors. Second, it is associated with a higher level of poverty at the provincial level. Combing evidence at the macro and micro levels, there appear to be productivity, education, and salary gaps between formal and informal workers. Moreover, markets are not segregated as informal firms compete strongly with formal ones. Finally, informality seems to pose macroeconomic challenges as tax efforts and financial sector depth in Indonesia remain below the EMDE averages. Table 1 summarizes the main findings of the paper and compares the country’s informality features to findings in the literature on informality in EMDEs.

**Table 1: Summary of informality features in EMDEs and Indonesia**

|**#**|**Feature of Informality in EMDES**|**Feature of Informality in Indonesia**|
|---|---|---|
|1|Informality is more pervasive in countries with<br>a lower level of per capita income.|Trend upheld. Informality declined in<br>Indonesia overall with rising GDP per capita.<br>Moreover, provinces with lower sub-regional<br>GDP per capita had more pervasive output and<br>employment informality.|
|2|Informality is associated with a large rural<br>population and a lack of urbanization.|While this is the case, informality is also<br>dominant in some urban areas like those with<br>large mining sector activity.|
|3|Informal participants tend to be concentrated in<br>the agriculture sector, especially in EMDEs.|Informality is higher in regions with a larger<br>agriculture sector. However, informality is also<br>strong in regions with a large low-skilled<br>service sector such as trade, transport and<br>construction.|
|4|Informal workers tend to be less skilled and<br>lower paid than their formal counterparts.|Trend upheld. There appears to be a<br>productivity gap, an education gap, and a<br>salary gap between formal and informal.<br>Moreover, informality seems to be associated<br>with higher poverty at the sub-regional level.|

25
protection programs and other welfare indicators in the EU-SILC. Traditionally, it is possible to combine information via record linking by relying on common household identifiers between two surveys. This is not possible in the case of HBS and EU-SILC, as the sample of households included in each survey does not overlap.

**In this paper, we explore statistical matching techniques to combine both datasets and impute energy spending shares into the EU-SILC.** While datasets are traditionally merged via record linking, an increasing number of researchers has explored the potential of statistical matching for combining datasets without a common identifier, especially in the European Union (see, for example, Donatiello et al. (2016b) for an application to Italy; Serafino and Tolkin (2017) for an example using data from six European countries; Lamarche et al. (2020) for the European context more broadly speaking; Schaller (2021) for an application in Germany, France, and the Netherlands; Emmenegger et al. (2022) for an application in Germany). We build upon the existing body of literature and apply statistical matching techniques to impute the energy spending share in Bulgaria reported in the HBS 2019 into EU-SILC data collected in 2020. We use EU-SILC data from 2020, as income data in the EU-SILC always refers to the previous year (so 2019 in this case).

**Imputing energy expenditure shares and energy poverty into the EU-SILC facilitates future research questions, such as exploring the impact of rising energy prices on the poorest households.** Our imputation exercise is helpful given that we can identify if those households belonging to the lowest income quintiles are those most affected by energy poverty. In addition, having information on energy expenditure shares in surveys that provide information on monetary poverty indicators allows us to evaluate the impact of rising energy prices on the poorest households. Moreover, given that the EUSILC contains detailed information on social protection programs, we can explore the impact of potential mitigation strategies in response to rising energy prices by combining information from both surveys. Lastly, given that EU-SILC survey questionnaires also contain extensive information on

4
**Table 7. Steps 6 and 7 from the hierarchical two-level linear regression models for mathematics and science achievement, grade 8, TIMSS 2019**

||**Mathe**|**matics**|**Scie**|**nce**|
|---|---|---|---|---|
||**Step 6**|**Step 7**|**Step 6**|**Step 7**|
|_R_<sup>_2_</sup><br>student-level (%)|26.7|27.3|25.5|27.2|
|class/school-level(%)|64.0|74.4|69.8|79.0|
|**Intercept (****_SE_)**|367.09(23.42)|310.38(36.78)|441.38(19.49)|426.88(24.75)|
|**Student-level variables(reference category)**|**_B(SE)_**|**_B(SE)_**|**_B(SE)_**|**_B(SE)_**|
|Student gender (male)|16.29 (6.98)*|90.11 (35.49)*|39.89 (7.52)***|74.88 (25.07)**|
|Student immigration status (native)|||||
|second-generation immigrant students|17.59 (7.92)*|17.51 (7.97)*|31.81 (8.36)***|31.84 (8.36)***|
|first-generation immigrant students|31.83 (9.37)**|31.92 (9.33)**|43.75 (9.67)***|43.76 (9.57)***|
|Home educational resources|4.09 (1.03)***|4.10 (1.03)***|5.39 (1.40)***|5.44 (1.40)***|
|Student owns mobile phone (no)|4.20 (5.13)|3.80 (5.07)|-0.32 (6.32)|-1.32 (6.53)|
|Student absenteeism (never or almost never)|||||
|once every two months|-8.73 (5.81)|-9.10 (5.78)|-5.55 (6.26)|-5.16 (6.26)|
|once a month|-19.47 (5.06)***|-20.02 (5.20)***|-13.34 (5.34)*|-12.49 (5.24)*|
|once every two weeks|-29.92 (4.84)***|-30.31 (4.83)***|-10.40 (7.47)|-10.19 (7.49)|
|once a week|-44.23 (6.40)***|-44.52 (6.53)***|-46.77 (6.44)***|-47.10 (6.46)***|
|Student likes learning mathematics/science|-4.30 (1.57)**|-4.35 (1.57)**|-0.85 (1.17)|-0.79 (1.18)|
|Student confident in mathematics/science|15.24 (1.27)***|15.26 (1.27)***|11.35 (1.54)***|14.09 (2.02)***|
|Student sense of school belonging|-1.03 (1.12)|-1.00 (1.12)|-1.20 (1.45)|-1.15 (1.45)|
|Bullying<sup>a</sup>|-0.42 (0.91)|-0.43 (0.91)|1.49 (1.21)|1.32(1.21)|
|Time spent on mathematics/science homework(15 minutes or less)<sup>b</sup>|-11.35(3.98)**|-11.40(4.01)**|-12.37(3.99)**|-11.94(3.99)**|
|**Class/school-level variables(reference category)**|||||
|School mean of home educational resources|25.97 (5.32)***|26.91 (5.29)***|20.23 (4.66)***|18.59 (4.37)***|
|School location (urban)|||||
|suburban/medium size city or large town|6.64 (6.19)|9.63 (6.22)|-6.53 (5.96)|-7.84 (5.70)|
|small town or village/remote rural|2.05 (11.06)|4.58 (11.13)|3.12 (8.44)|-3.79 (8.53)|
|Teacher age (40 years or older)|||||
|29 years or younger|-20.08 (19.13)|-23.36 (19.05)|9.78 (13.38)|3.56 (12.88)|
|30–39 years|-5.54 (11.15)|-6.51 (11.35)|-2.14 (6.47)|-20.34 (7.79)**|
|Safe and orderly schools|0.87 (1.68)|0.87 (1.62)|2.29 (1.40)|1.96 (1.44)|
|School emphasis on academic success|1.80 (1.70)|5.76 (2.48)*|0.07 (1.24)|0.46 (1.29)|
|School discipline|-2.60 (1.92)|-3.11 (1.93)|0.64 (1.14)|0.13 (1.13)|
|Teacheryears of experience|-0.49(0.88)|-0.60(0.88)|0.54(0.48)|0.54(0.51)|

27
46 <!-- Start of picture text --> Firave 3<br><!-- End of picture text --> sm **tATt** Of _cG_ U Of NDU8L AoDucno <!-- Start of picture text --> 39 - S.~~~~~~~~~~~~.<br>-16<br>-so - , ,,,,, ................., ,, , ,, ,r,,,, ,,, , T,,,, ,,,. ,..........<br>80/1 81/1 82/1 8311 84/1 85/7 86/1 87/1 88/1<br>THE RATE Of GROM 0o EfnLOTmI<br>(2 Per Month)<br>4<br>-4 _ ................ W ... .. ...<br>80/1 81/1 82/1 83/1 84/1 85/1 86/1 87/1 88/1<br>THE TRADe oDcBfcT<br>(m illion)<br>U./1 61/1 62/1 83/1 84/1 85/7 8611 87/1 Boll<br>80/1 81/1 82/1 83/1 84/1 85/7 88/1 87/1 88/1<br><!-- End of picture text -->
- - 48

that are likely to accompany the proposed reforms would also minimize some of the adjustment costs. Therefore, the net effects are likely to be less negative than outlined above.

# E. CONCLUSIONS

85. The policy of high taxes and tariffs on capital goods and key intermediates have escalated the costs of production in India across a wide spectrum of industries. At the same time, this structure of protection has led to much lower net average protection to the industrial sector as a whole. However, it has created tremendous unplanned variance in net EPRs where half the industry receives highly positive while the other half receives highly negative net EPRs. The policy of keeping landed price of imports (especially for intermediates) higher than domestic prices also has induce **d** omestic firms to enter these areas irrespective of considerations of comparative advantage and international competitiveness. These uneconomic investments in turn generate further tariff escalation5 and higher costs of production for all downstream Industries. On the other hand, the deregulati **o** n f industry through excess entry and creation of supply surpluses has tended to lower the EPRs. The deregulation seems to have greater impact primarily on some consumer and capital goods. However, in intermediates and other capital goods, domesti **c** ompetition does not seem to have achieved the same results.24/ First, India is still a large net importer of these products and will be so for some time to come. Second, rapid growth in manufacturing (due to deregulation and excessive domestic demand) have led to growing imports despite rapid growth of domestic supply of intermediates. Third, these sectors are highly capital intensive which makes entry more difficult and many of these products are licenced tightly and/or reserved for public sector. What is needed in these sectors is more import competition and across the board tariff and tax reductions; which would lower the costs of production for all downstream industries.

24/ Recent productivity growth studies have corroborated this observation. According to these estimates, total factor productivity (TFP) growth has increased dramatically during the 1980s. However, the TFP growth has been lowest for intermediates; 1.4X p.a. for intermediate goods versus about 6X p.a. for consumer goods and 3.4X for capital goods (Ahluwalia, 1991).
# **2. Data**

## **2.1 Data on health at birth**

We use administrative data from the Colombian national registry of live births over 1999-2008, which are available from the vital statistics, collected by the Administrative Department of Statistics (DANE). In contrast with the data used in previous studies on the effects of shocks _in utero_ on birth outcomes, which tend to be limited to a geographic area within a country, the data we use have universal coverage of all birth records in Colombia. The data contain several measures of health at birth, including weight, length, gestational age, and the Apgar score, which is measured 1 and 5 minutes after birth. The existing literature tends to focus on a very limited set of birth outcomes, most notably birth weight and gestation. This study breaks new ground by also looking at height at birth and the Apgar score carried out 5 minutes after the full birth of the baby. We use the 5 minute Apgar score because it is regarded as more reflective of the baby’s overall health and more predictive of survival and neurologic abnormalities at one year of age (Chase and Greenberg, 1965; James, 1960). Birth records also include other important information about the child (sex, birth order, and multiple births), the mother (age and educational attainment), the method of delivery (vaginal, caesarean section or instrumented), and birth location (health institute, home, other).

We analyze both weight at birth (measured in grams) and an indicator for normal weight, which takes the value of one if the weight of the liveborn infant is more than 2,500 grams. We use the 2,500 cutoff because the medical literature has identified it as the threshold over which the probability of perinatal mortality and morbidity, inhibited growth and c <mark>ognitive development, and chronic diseases later in life drops substantially. We also construct and indicator for full term baby taking the value of one if the baby is born at 38 weeks of gestation or more and is zero otherwise. Our fourth health outcome is length at birth (in centimeters). Lastly, we use a dummy variable that indicates overall health of the newborn based on an Apgar score of at least 8 points, which is the cutoff point used in the medical literature.</mark> The Colombian registry is also unique in that it can be merged with the temperature and rainfall data based on the mother's municipality of residence. We use information on the children’s date of birth and municipality of residence of the mother to calculate whether a child was exposed to a weather 7
Demand for children’s health care is likely distorted by market imperfections such as credit constraints. Parents may not fully internalize their children’s welfare or externalities due to contagion. Moreover, households may not be able to interpret symptoms and assess the value of visiting the doctor in different health states. All of these imply that parents’ health care demand is not socially (or even privately) optimal. At the same time, solving the social planner’s problem of optimal resource allocation requires too many parameters for which there are no good estimates, from the production-side trade-off between supplying health care and other goods, to the true preferences of the household.

Given the correlation of the IMCI diagnostic algorithms with serious illness, they provide some guidance for assessing welfare, even if imperfectly. First, they constitute an approximate solution to an optimization problem: conditional on the limited information an outside observer can use to make a recommendation, for an organization that is prepared to devote resources to children’s health, which mapping from health states to care utilization has the greatest effects on expected mortality and morbidity? At a minimum, this provides a _ranking_ ; from a medical standpoint, care in the “care required” state is more valuable than otherwise, even if the total amount of care is not optimal.

Many policymakers may indeed agree that all children should receive care when the IMCI recommends it. For example, a child with malaria symptoms should be seen by a doctor regardless of parental preferences or wealth. Moreover, it is unlikely that parents have private information that indicates that no care is needed even though the IMCI recommends care.<sup>17</sup> The “care required” designation may thus be considered a lower bound for underuse.

Last but not least, comparing parents’ behavior to the IMCI recommendations indicates to what degree they follow the CHW’s care seeking advice. Thus, even if they do not characterize the social optimum, the WHO guidelines capture important information, giving them a role in evaluating the effects of demand-side health care policies. Studying demand in detail in this manner complements studies that measure mortality or health outcomes.

> 17The reverse seems more plausible, i.e. parents may observe symptoms that require medical care but are not covered by the IMCi, such as injuries.

17
Third, implementation problems, particularly the failure of the government to issue the promised recognition bonds, reduced the confidence of workers **in** the new system. The regulations for the issue of recognition bonds did not come out until December 1994.

<!-- Start of picture text --> Monthly Rate of Affiliation<br>140,000<br>120,000<br>100,000<br>80,000<br>60,000<br>40,000<br>20,000<br>0<br>J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J<br>1993 1994 1995 1996<br><!-- End of picture text --> Source: SAFP Fourth, until April 1994, the government did not pay the contributions for those public sector employees who had joined the SPP. The still outstanding debt, including interest and penalties, amounts to about S.400 million or US$ 162 million.

Fifth, affiliates were not properly informed about the implications of the pension reform; many workers thought, for example, that they would lose health insurance coverage if they switched from the public to the private pension system.

After contribution rates between the SPP and SNP were equalized at 11 percent and a uniform retirement age of 65 years was set for both systems in July 1995, monthly affiliation increased by a factor of 5. In the last quarter of 1996, about 40,000 workers on average joined the SPP every month. It should be noted that the July 1995 changes also removed the "solidarity" fee of 1% that affiliates to the private pillar were required to pay.

As observed in the case of Chile and other countries which operate AFP systems, there is a large discrepancy between the number of affiliates and those who actually regularly contribute to their accounts. In Peru, the share of affiliates who are regularly contributing to their accounts is estimated to be around 45 percent.

There are various reasons for this discrepancy. In the case of dependent workers, employers may default on their payment obligations and mistakes are made in the contribution data submitted to 10