rafmacalaba/datause-encoder-data huggingface dataset:
_Intraclass Correlation Coefficient_

**Table B.3: Intraclass Correlation Coefficient**

||**Guangdong**|**Guangdong***|**S-MTP**|**MET**|
|---|---|---|---|---|
|**Positive Climate**|0.58|0.60|0.43|0.42|
|**Teacher Sensitivity**|0.28|0.30|0.35|0.33|
|**Regard for Student Perspectives**|0.39|0.43|0.39|0.38|
|**Behavior Management**|-0.01|0.05|0.44|0.41|
|**Productivity**|0.30|0.34|0.38|0.28|
|**Negative Climate**|0.06|0.07|0.51|0.49|
|**Instructional Learning Formats**|0.38|0.38|0.39|0.35|
|**Content Understanding**|0.41|0.47|0.38|0.31|
|**Analysis and Inquiry**|0.33|0.34|0.33|0.26|
|**Quality of Feedback**|0.38|0.46|0.42|0.37|
|**Instructional Dialogue**|0.36|0.42||0.37|
|**Student Engagement**|0.36|0.39|0.41|0.27|
|**_Domains_**|||||
|**Emotional Support**|0.67|0.70|||
|**Classroom Organization**|0.23|0.41|||
|**Instructional Support**|0.64|0.70|||

Source: Authors’ calculations; Teachstone. Note: The Guangdong* column removes the two rater pairs least in agreement.

The two-way mixed-effect model for absolute agreement is a more stringent measure of inter-rater reliability, measuring not only differences in absolute scores, but also in ranking. Scores for the Guangdong assessment range from 0.28 to 0.58 across the dimensions, while domain scores range from 0.23 to 0.64 (table B.3). These ratings are in line with comparable studies, although none of the values across all studies would be considered as indicating extremely high consistency or agreement between raters per the most widely accepted standards of ICC tests.

Additionally, these ratings are averages for all rater pairs. Much as there were weaknesses in the percent agreement, there were also weaker pairs pulling down the averages. For example, if the two weakest pairs were removed from the Classroom Organization ICC score, the average would rise to 0.41, almost doubling the observed ICC.

Other CLASS studies indicate that more experience with the CLASS instrument may improve inter-rater reliability. In a paper on the applicability of CLASS in Chinese preschools, Hu et al. (2016) achieved average ICC scores of 0.82 to 0.92, indicating very high levels of consistency between raters. The authors, however, have been using CLASS for many years and have had more opportunity to discuss the measures.

Finally, given the range of subjects (math, English, Chinese), the difference in grades (4<sup>th</sup> and 8<sup>th</sup> ), settings (urban and rural), and rating methods (in-person, videotaped), it is worth further investigation to see if any of these factors play a role in consistency and agreement between raters.

35
> <sup>10</sup>

objecting to it on the grounds that MPLADS would be used by national parties to encroach upon the policy domain of lower tier governments ( _Inter Press Service Global Information Network_ , February 1, 1994). The timing and manner of program initiation, and these remarks from a political party with predominantly regional strength, suggests that MPLADS may have been conceived as a vehicle for the dominant national party to channel funds to its MPs in the growing number of states controlled by the opposition.<sup>11</sup> In sum, when MPLADS was initiated there appears to have been no political incentive to inform voters about the program as a means of constituency service by legislators. Correspondingly, there was also no coverage of the program in the media. A search on the media database News Plus/Factiva for newspaper coverage of the MPLADS program yields only 6 articles in the four and a half year period between October 1993, just before MPLADS was introduced in the parliament, and June 1998. Only one article, written in February 1994, discussed the program itself in any significant detail, with the others mentioning it only in passing as part of other stories. Even this single article was published in a less visible media, the Inter Press Service Global Information Network, rather than in a leading newspaper. In sum, the program was not widely politically salient for the first years after its initiation.

The scenario changed suddenly in 1999 after the Comptroller and Auditor General (CAG) of India published a pilot audit of the MPLADS program in a few states (Government of India, 1998). The CAG report revealed both lack of utilization of funds and some inconsistencies in the way funds were used. It concluded that guidelines needed to be

> 11 The MPLADS program appears to have been modeled after a similar program initiated by various state governments to provide constituency-specific allocations to state legislative assembly members (MLAs). The MLALADS can similarly have been a political response to declining control of political parties in state elections—coalition governments of multiple political parties had become a constant feature of the state-level political landscape.
# **Measuring Firm-Level Innovation Using Short Questionnaires: Evidence from an Experiment**<sup>1</sup>

Xavier Cirera Silvia Muzi Keywords: Innovation, Measurement, Surveys, Firms, Development JEL codes: 010, 030, 050 1 Contacts: Xavier Cirera (Innovation and Entrepreneurship, Trade and Competitiveness, World Bank, <u>xcirera@worldbank.org); Silvia Muzi: (Development Economics, Enterprise Analysis Unit, World Bank, smuzi@worldbank.org).</u> We would to thank Jorge Luis Rodriguez Meza and the participants in seminars at the World Bank and OECD for their suggestions and comments. We would also like to thank Xie Huang for excellent research assistance, Leonard Sabetti for his valuable and detailed work with the verification of the innovation descriptions, and Amanda Franziska Zarka for her comments. We thank the UK Department for International Development (DFID) for the financial support provided to DECEA for collecting data on innovation in developing countries.
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For the third group of endogeneity tests, we argue that for a given increase in informal competition, certain types of firms are likely to suffer more from informal competition than the others. Thus, we check that a unit increase in informal competition has a much smaller negative impact on large firms vs. SMEs, exporting firms vs. non-exporting firms, and financially unconstrained vs. financially constrained firms. The rationale for these tests is as follows. It is well-known that large firms operate in very different product markets and therefore unlikely to be much affected by informal competition. The logic can be extended to exporting firms that also operate in very different (international) markets than the non-exporting and informal firms. Regarding financial constraints, our endogeneity check is based on the findings of Distinguin et al. (2016). As mentioned above, the study by Distinguin et al. (2016) shows that informal competition worsens access to external finance for SMEs in the formal sector. In other words, one of the ways in which informal competition can hurt SMEs is by reducing their access to finance. Thus, SME firms that do not have proper access to finance are likely to suffer more from a given increase in informal competition than the rest. We expect the stated predictions to hold if informal competition has a causal impact on employment growth but there is no strong reason for it to hold otherwise.

# **3. Base Regression Results**

For all the regressions discussed below, we control for initial employment as this is perhaps one of the most important predictors of employment growth. The remaining baseline controls are added sequentially.

The base regression results are provided in Table 4. These results reveal a large, negative and statistically significant relationship between the growth rate of employment and informal 20
# Innocent Bystanders:

How Foreign Uncertainty Shocks Harm Exporters<sup>_∗_</sup> Daria Taglioni<sup>_†_</sup> Veronika Zavacka<sup>_‡_</sup> <mark>October 1, 2012</mark>

## **JEL Classification** : F02, F10, G01

**Keywords** : uncertainty shocks, international trade, exporters

**Sector Board** : <mark>Economic Policy (EPOL)</mark>

> _∗_ This paper represents the views of the authors and should not be interpreted as reflecting those of the European Bank for Reconstruction and Development or 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. We acknowledge valuable comments by George Alessandria, Jean-Louis Arcand, Richard Baldwin, Nicolas Berman, Matthieu Bussiere, Giancarlo Corsetti, Thomas Farole, Bernard Hoekman, Daniel Lederman and Ugo Panizza.

> _†_ The World Bank. Email: dtaglioni@worldbank.org

> _‡_ Graduate Institute, Geneva and European Bank for Reconstruction and Development. Email: veronika.zavacka@graduateinstitute.ch, zavackav@ebrd.com

1
**Table 3. Estimation of well‐being controlling for shocks and interactions**

|||**Co**|**nsumption**|**Foo**|**d Consumption**||**Income**|**M**|**aize harvest**|
|---|---|---|---|---|---|---|---|---|---|
|||Variance|Level|Varianc|e<br>Level|Variance|<br>Level|Variance|Level|
||Rainfall shock||‐0.459|**|‐0.603|***|‐0.275||‐0.476|
|**es)**|||(.014)||(.003)||(.358)||(.257)|
|**alu**|Maize price shock||‐0.204||‐0.275||0.065||‐0.149|
|**13 v**|||(.237)||(.155)||(.812)||(.604)|
|**(20**|Malaria shock||‐0.013||‐0.005||‐0.017||‐0.062|
|**ck**|||(.568)||(.831)||(.727)||(.164)|
|**Sho**|Off‐farm employment loss shock||‐0.077|**|‐0.046||‐0.642|***|0.114 **|
||||(.011)||(.150)||(.000)||(.019)|
||CoV ‐ Dec‐Jan Rainfall, 1983/84 ‐ 2014/15|0.379|‐0.239|0.726|‐0.829|‐2.020|‐0.550|<br>1.466|‐2.558 **|
|||(.366)|(.673)|(.215)|(.221)|(.286)|(.539)|(.314)|(.047)|
|**ure**|CoV ‐ Jan‐Mar Real Maize Price, 2005‐2013|0.021|‐0.060|0.040|‐0.076|0.043|0.156|‐0.116|0.202|
|**pos**||(.668)|(.300)|(.470)|(.254)|(.845)|(.227)|(.442)|(.128)|
|**Ex**|CoV ‐ HH Incidence of Malaria|‐0.036|**<br>0.018|‐0.053|***<br>‐0.004|‐0.193|***<br>0.061|‐0.070|0.031|
|**Risk**||(.020)|(.430)|(.009)|(.881)|(.005)|(.107)|(.226)|(.577)|
||CoV ‐ HH Incidence of off‐farm employment|0.004|‐0.015|*<br>0.006|‐0.018|*<br>‐0.017|0.000|0.058|**<br>0.018|
|||(.447)|(.095)|(.466)|(.078)|(.487)|(.999)|(.017)|(.337)|
|<br>**es)**|HH Wealth + Education Index|‐0.031|1.085|***<br>‐0.143|***<br>0.918|***<br>‐0.243|1.646|***<br>‐0.006|1.359 ***|
|**lth**<br>**alu**||(.418)|(.000)|(.003)|(.000)|(.140)|(.000)|(.970)|(.000)|
|**Wea**<br>**10 v**|HH Ag Assets + Land Holdings Index|‐0.100|*<br>0.097|‐0.089|0.116|*<br>‐0.899|***<br>0.440|***<br>‐0.386|*<br>1.055 ***|
|<br>**(20**||(.057)|(.131)|(.119)|(.087)|(.000)|(.000)|(.058)|(.000)|
||Number of observations|2789|2789|2789|2789|2770|2770|2432|2432|
||R‐squared|0.019|0.277|0.025|0.219|0.02|0.223|0.022|0.226|

_Note_ : Table reports coefficients and p‐values in parentheses. Asterisks denote significance levels: * p<0.10, ** p<0.05, *** p<0.01.

17
Based on findings from the literature on substantial differences in gender wage gaps along the wage distribution, we also adopt a quantile decomposition framework. Quantile regression provides a richer characterization of the data, allowing us to consider the impact of a covariate on the entire distribution of hourly earnings, not merely its conditional mean, and thus provides a deeper understanding of existing wage differentials. For decompositions based on quantile regression methods, we use the method proposed by Machado and Mata (2005), whereby the conditional earnings distribution<sup>6</sup> is estimated using quantile regression separately for men and women at specific quantiles. For each gender group, the quantile function is expressed using the following linear specification: where, _w_ denotes log earnings and the group subscript _g_ includes both genders ( _m_ , _f_ ). _X_ is a vector of covariates (including observed characteristics and skills measures) for individual _i_ and � is a vector of coefficients estimated at different quantiles �. The quantile regression coefficients are interpreted as the returns to different observed characteristics and skills at specific quantiles of the earnings distribution. It is assumed that all quantiles of _w_ , conditional on _X_ , are linear in _X_ Instead of using the simulation-based technique proposed by Machado and Mata (2005) to estimate the unconditional earnings distribution function, we use the procedure from Melly (2006), which is computationally less intense but is shown to be numerically identical when the number of simulations goes to infinity. Melly’s procedure<sup>7</sup> integrates the conditional distribution function over a range of covariates, then inverts the unconditional distribution function to obtain unconditional quantiles. The counterfactual for women using observed characteristics and the skills profile of women and the male wage structure is expressed as: � ��� ���,�′��,� .

The decomposition of earnings gaps of the unconditional quantile functions between men and women is then given by: � � ������,� ���� ������ ���,�� , where the first term on the right-hand side represents the quantile endowment effects or the effect of characteristics and skills, and the second term represents the effects of coefficients.

The issue of self-selection or general endogeneity leading to biased results is a concern in this and most decomposition analysis. Men and women could follow different decision-making

> 6 The conditional quantile distribution is the distribution of an outcome Y at specific quantiles conditional on a set of covariates X.

> 7 Melly’s (2006) procedure has been implemented using the “cdeco” program in Stata.

9
percent aspired to it; 21 percent expected their children to do so while 40 percent aspired to it (Garcia, Harker, and Cuartas 2019). Similarly, when asked about educational options they were aware of and felt academically able to follow, French junior-high-school students reported that they felt able to follow only a subset of options of the ones they were aware of (Guyon and Huillery 2021). In El Salvador, Mexico, and Uruguay, a housing program for slum-dwellers had a positive impact on participants’ aspirations but not their expectations, and their expectations were found to be unrelated to the evolution of their housing aspirations over time (Galiani, Gertler, and Undurraga 2021).

# **3. Data**

## _Data set_

We use a large cross-sectional survey that is representative of students in public, secular, middle schools on the Indonesian islands of Java and Sumatera, which are home to 70 percent of the Indonesian population. The survey, collected in February-March 2018, covers 55,964 8<sup>th</sup> grade students studying in 2,095 public schools.<sup>3</sup> The data was collected as the baseline of an impact evaluation of a pilot intervention aimed at improving students’ aspirations, mindsets, socioemotional skills, studying practices, and learning outcomes (Johnson et al. 2020).

## _Measures of outcomes and correlates_

**Schooling outcomes** . We measure two current schooling outcomes: absenteeism and grades. Absenteeism (the opposite of school attendance) is the number of missed days of school in the month preceding the survey, which is self-reported by the students. Students who did not miss any day—51 percent of the sample—are marked with a zero. Eighty percent of students who missed at least one day report they missed school because of sickness. Students who mention other factors for missing schools, such as family’s demands, school and weather factors, and issues related to their behaviors, miss more days per month on average. Grades are the average of four subjects—Math, Science, English, and Bahasa Indonesia—of the last semester before the survey. Information on grades was collected from the schools’ administrative data and has been standardized at the school level with a mean of zero and a standard deviation of one. School attendance and grades correlate highly with college attendance, future wages, and future arrests better than standardized achievement test scores (Jackson 2018).

> 3 The survey lasted 40 minutes on average. It was paper-based and self-administered during school time under the guidance of a trained enumerator.

7
**Figure 10. Incidence of cash and in-kind benefits by deciles**

<!-- Start of picture text --> 3.5 USD PPP % 50 0.30 USD PPP % 3.0<br>45<br>3.0<br>0.25 2.5<br>40<br>2.5 35<br>0.20 2.0<br>30<br>2.0<br>25 0.15 1.5<br>1.5<br>20<br>0.10 1.0<br>1.0 15<br>10<br>0.05 0.5<br>0.5<br>5<br>0.0 0 0.00 0.0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>% of disposable income net of intervention (right axis) % of disposable income net of intervention (right axis)<br>USD PPP per day USD PPP per day<br>(a) benefits (b) privileges<br>20 USD PPP % 250<br>18<br>16 200<br>14<br>12 150<br>10<br>8 100<br>6<br>4 50<br>2<br>0 0<br>1 2 3 4 5 6 7 8 9 10<br>% of disposable income net of intervention (right axis)<br>USD PPP per day<br><!-- End of picture text --> <!-- Start of picture text --> (c) pensions<br><!-- End of picture text --> _Note._ The ratio of pensions to the disposable income net of pensions for the first decile is not calculated as related net income for this decile is close to 0.

_Source_ : own estimates based on HBS data.

# **3.5. Indirect subsidies**

Subsidies for utilities and urban public transportation are two major types of indirect subsidies in Belarus. Utilities subsidies amount to 2% of GDP, with around 1% covered by the cross-subsidization by the enterprises, and the other 1% coming directly from the budget (IMF, 2016). As of 2015, subsidies on utilities were available to everyone automatically in the form of subsidized tariffs in the utility bill (since September 2016 direct subsidies to the utilities are also available). Some households and apartments, however, are not eligible for the subsidy. These are the households where the household head owns more than one apartment or house, or if no one is registered in the apartment (usually the case when the apartment is rented out). According to IMF estimates, households with access to the subsidy covered 48.5% of the actual costs.

Expenditures on utilities are reported in the HBS, but the households do not report if they get the subsidy or not. To identify the households without access to subsidized tariffs we establish a cut-off in utilities cost per square meter. If the household is paying above the cut-off of BYR 15,000 per m<sup>2</sup> (two times higher than the average), or above BYR 1,000,000 in total per month (three time higher than the average), we assume that the household does not obtain the subsidy. The rest of the households are assigned a 51.2% subsidy on top of their actual utilities expenditure.

To check if we have allocated the utilities subsidy correctly, we gross up the allocated utilities, and find that they sum up to 1.96% of GDP, which coincides with the IMF estimate of 2% of GDP.

20
P 2 1 1) (6) ii2 ii1 = PP(yy ≤zz |zz < yy < vv

In this study we specify the vulnerability index P to be 20% and we calculate the corresponding vulnerability line based on the 2004/5-2011/12 synthetic panel interval. This is associated with a vulnerability line of Rs. 770 in 2004/5 prices and can be compared to the all-India rural poverty line in 2004/5 of Rs.447. Note that P is bounded below by the percentage of the nonpoor population in period 1 falling into poverty in the second period. Note further that 1 provides the solution to expression (6), which in turn can be used to obtain 2 with an appropriate price deflator. Given vv , we can extend expression (2) to analyze the dynamics for vv ii these three categories: poor, vulnerable, and secure. For example, the percentage of poor vv households in the first period that escape poverty but still remain vulnerable in the second period is<sup>11</sup>

# **IV. Validating the Synthetic Panel Method**

As described above, the methodology we follow to produce synthetic panels with the NSS data is predicated on various assumptions. While these assumptions have been validated against actual panels in other contexts, they cannot all be readily tested empirically using crosssectional data alone. Hence we attempt to gauge the likely reliability of our mobility estimates by validating the methodology with the IHDS data. Despite some limitations with the IHDS data themselves, our strategy here is to consider this data source as the benchmark to validate the synthetic panels.<sup>12</sup> To the extent that we find that the method appears to work well, we then

> 11 See Dang and Lanjouw (2017) for further discussion, including another definition of the vulnerability line, as well their full derivations and properties.

> 12 As noted above, the IHDS data have far fewer consumption items than the NSS, which can result in concerns about a less comprehensive consumption aggregate. Furthermore, the IHDS is also affected by an attrition rate of around 17 percent between 2004/05 and 2011/12 (IHDS 2015). Clearly, these issues may affect the validity of the IHDS as the benchmark data source for validation.

16
of this spell presents some difficulties. For those who marry young, the “correct” starting point is first ovulation, when the woman becomes “at risk” for a birth, but often menarche is used instead. Information on age of menarche is only available in NFHS-1. Instead, for women that began living with their husband before age 12, the staring point is the month they turned 12 years old.

The second spell is from nine months after the birth of the first child until the second child is born or censoring occurs. The third and fourth spells follow the same definition. Beginning the spell nine months after the previous birth allows the next pregnancy to be carried to term. In other words, there is no variation in the dependent variable for the nine months following a prior birth.<sup>20</sup> Because the method requires births to occur, spells are counted as censored once data become too sparse for the method to work. For the first and second spell an observation is counted as censored if a woman has not had child within 21 quarters of the beginning of the spell and whereas the cut-off is 19 quarters for the third and fourth spell. This is equivalent to 6 years from last birth or marriage for the first and second spells and 5.5 years from last birth for the third and fourth spells. The shorter spell length for the third and fourth spells is dictated by the low number of births after 19 quarters. Sterilization is also treated as censored.

The explanatory variables are divided into two groups. The first group consists of the main explanatory variables that are expected to affect the shape of the hazard function and includes the mother’s education, the sex composition of previous children and the area of residence. The second group consists of the variables that are expected to have a proportional effect on the hazard and includes the age of the mother at the beginning of the spell, the length of her first spell (for second spell and above), whether the household owns land, and whether the household belongs to a scheduled tribe or caste. There is clearly a trade-off between increasing the number of variables that are expected to affect the shape of the hazard and the precision of the results. The more variables are interacted with the baseline hazard the more data are required to precisely estimate the effects. The choice of the variables to interact with the baseline hazard is based on which variables are likely to be most important for fertility decisions and the use of sex selection.

> 20 There are a few women that report births that occur less than nine months after the previous birth. Those women are dropped from the sample.

18