rafmacalaba/datause-encoder-data huggingface dataset:
_Yi Chang_ 5 cients ( _β_ 2 _, β_ 3 _, β_ 4 _, β_ 5 and _β_ 6) and 1 constant ( _c_ ) for the polynomial transformation:

- _T_ ( _s_ ) = _β_ 1( _s−c_ )+ _β_ 2( _s−c_ )<sup>2</sup> + _β_ 3( _s−c_ )<sup>3</sup> + _β_ 4( _s−c_ )<sup>4</sup> + _β_ 5( _s−c_ )<sup>5</sup> + _β_ 6( _s−c_ )<sup>6</sup> (2)

- c. Standardize transformed test scores in both periods (i.e. _t_ 0 and _t_ 1) together. The original Bond and Lang code standardizes test scores separately, which could artificially increase or decrease the resulting gap. By jointly standardizing both years, we ensure that they remain vertically equated and that the resulting gap is only due to the transformations.

- d. Run an OLS regression separately for each period using a subgroup dummy and controlling for other desired variables. Given this set-up, the coefficient on the subgroup dummy can be interpreted as the difference between each sub-group.

_T_ ( _s_ ) _t_ = _α_ (0 _,t_ ) + _α_ (1 _,t_ ) _GroupDummy_ + _θtControls_ (3)

- e. Find gap growth between _t_ 0 and _t_ 1, as defined in Step 1.

3. Run Step 2 multiple times from different random starting values, checking that transformations preserve order (monotonic rule).

# **3.2 Improvements**

The process described in section 3.1 is complex and computationally demanding. Most of the original public code is written in Mata, which might restrict wider use among researchers. Furthermore, the public code was written for the specific estimation in their paper. Thus, it makes some assumptions that might not necessarily extrapolate to other data and might affect the speed or magnitude of the results. This left space for two areas of improvement: (a) making the methodology more accessible to Stata users, and (b) improving the precision, flexibility and efficiency of the methodology.

In terms of accessibility, the `scale transformation` command, together with this paper and its documentation will allow any Stata user to perform these robustness checks in one line of code.

Methodologically, the original process also sacrificed precision for efficiency. For instance, the parameter _β_ 1 was not optimized and the monotonicity rule used did not check every single plausible value but rather checked monotonicity in small equallyspaced intervals. The `scale` ~~`t`~~ `ransformation` command improves on these aspects as well as on computational speed.

Specifically, the original methodology has been improved by computing more accurate estimates (in quad precision) using QR decomposition tools in Mata. The monotonicity rule has also been enhanced to be more accurate and flexible with 3 variants: (a) a default rule that checks for monotonicity at every possible score value up to 4 decimals within the plausible range in a transformed scale between 0 and 1; (b) a quick
Allard, C., J. I. Canales-Kriljenko, W. Chen, J. Gonzales-Garcia, E. Kitsios, and J. Trevino. 2016. “Trade Integration and Value Chains in Sub-Saharan Africa: In Pursuit of the Missing Link.” International Monetary Fund, Washington, DC.

Alvarado, R., M. Iniguez, and P. Ponce. 2017. “Foreign Direct Investment and Economic Growth in Latin America.” Economic Analysis and Policy 56 (December): 176-187.

Alvarez, R., and M. Grazzi. 2018. “Innovation and Entrepreneurship in Latin America. What Do We Know? What Would We Like to Know?” Estudios de Economica 45 (2): 157-171.

Amarnath, G., N. Alahacoon, V. Smakhtin, and P. Aggarwal. 2017. “Mapping Multiple Climate-Related Hazards in South Asia.” IWMI Research Report 170, International Water Management Institute, Colombo, Sri Lanka.

Anand, R., M. K. C. Cheng, S. Rehman, and M. L. Zhang. 2014. “Potential Growth in Emerging Asia.” IMF Working Paper 14/2, International Monetary Fund, Washington, DC.

Angrist, N., A. de Barros, R. Bhula, S. Chakera, C. Cummiskey, J. DeStefano, J. Floretta, M. Kaffenberger, B. Piper, and J. Stern. 2021. “Building Back Better to Avert a Learning Catastrophe: Estimating Learning Loss from Covid-19 School Shutdowns in Africa and Facilitating Short-Term and Long-Term Learning Recovery.” International Journal of Educational Development 84 (July): 102397.

Apedo-Amah, M. C., B. Avdiu, X. Cirera, M. Cruz, E. Davies, A. Grover, L. Iacovone, et al. 2020. “Unmasking the Impact of COVID-19 on Businesses: Firm Level Evidence from Across the World.” Policy Research Working Paper 9434, World Bank, Washington, DC.

Aravena, C., J. Friedman, and A. Hofman. 2017. “Sources of Productivity and Economic Growth in Latin America and the Caribbean, 1990-2013.” International Productivity Monitor 33 (Fall): 51-76.

Asian Productivity Organization. 2016. APO Productivity Databook. Tokyo: Asian Productivity Organization.

Aslam, A., S. Beidas-Strom, R. Bems, O. Celasun, S. Kilic Celik, and Z. Kóczán. 2016. “Trading on Their Terms? Commodity Exporters in the Aftermath of the Commodity Boom.” IMF Working Paper 16/127, International Monetary Fund, Washington, DC.

Aturupane, H., P. Glewwe, R. Ravina, U. Sonnadara, and S. Wisniewski. 2014. “An Assessment of the Impacts of Sri Lanka’s Programme for School Improvement and School Report Card Programme on Students’ Academic Progress.” The Journal of Development Studies 50 (12): 1647-1669.

Azevedo, J. P., A. Hasan, D. Goldemberg, K. Geven, and S. A. Iqbal. 2021. “Simulating the Potential Impacts of COVID-19 School Closures on Schooling and Learning Outcomes: A Set of Global Estimates.” The World Bank Research Observer 36 (1): 1- 40.
|Table 4: Year effects<br>Ratio of Car. Price to Average US Price<br>Tariff<br>Total Imports<br>.<br>Observations<br>Difference<br>($ millions)<br>1.052**<br>1992<br>3,084<br>458<br>(0.289)<br>0.787**<br>1993<br>3,808<br>502<br>(0.207)<br>0.812**<br>1994<br>4,367<br>527<br>(0.198)<br>0.686**<br>1995<br>5,322<br>596<br>(0.192)<br>0.728**<br>1996<br>5,927<br>629<br>(0.173)<br>0.658**<br>1997<br>7,529<br>661<br>(0.182)<br>0.685**<br>1998<br>8,219<br>673<br>(0.192)|
|---|
|1.183**<br>1999<br>8,765<br>659<br>(0.203)<br>1.287**<br>2000<br>9,570<br>661<br>(0.202)<br>0.592**<br>2001<br>9,486<br>650<br>(0.156)<br>0.815**<br>2002<br>9,474<br>628<br>(0.163)|

** Statistically significant at the 1% level. Standard errors in parentheses. Each estimation

includes separate product group, country fixed effects and has y? significant at the 1% level.

25
child labor (e.g. Basu and Van 1998), and there is surprisingly little evidence against the assumption (see Levison and others (1998) for an example and discussion). Thus, the discussion in this section is applicable to the more general household composition case in addition to the sibling comparison case that is considered explicitly.

# **3.  Data Description**

The relationship between product prices in the rice sector and the economic activities of household members is investigated using two rounds of the Vietnam Living Standards Survey (VLSS) that span the period of rice market liberalizations. The first round of the VLSS was conducted between September 1992 and October 1993. The second round of the VLSS revisited 147 communes from the first round between December 1997 and December 1998. This study focuses on households in the 115 revisited rural communes and is not limited to panel households that are interviewed in both rounds of the survey. The sample is limited to rural households, because rural households have the potential to be exposed to price changes on both the consumption and the production side. Hence, they should display all of the mechanisms discussed in the previous section. The household survey includes questions on household composition, the labor activities of adults and children, education, expenditure, land holdings, and agricultural activities. The household survey is accompanied by a community questionnaire that includes detailed price information.

# **3.1 Rice Prices**

This paper relates changes in the price of rice to changes in the economic activities children, allowing the association between rice price changes and child labor to vary with sibling and household composition. Thus an overview of the observed rice price changes is important to understand identification. The price of rice is deflated with the monthly consumer price index so 11
# **TABLE A24:  Reporters Without Borders (RSF)**

## **A24: Reporters Without Borders**

http://www.rsf.org Reporters Without Borders - headquartered in Paris - is an international organization dedicated to the protection of reporters and respect of press freedom in the world. In 2002, International Reporters Without Borders published its first worldwide press freedom index, compiled for 139 countries. The organisation's initiatives are being carried out on five continents through its national branches and its offices in Abidjan, Bangkok, Buenos Aires, Istanbul, Montreal, Nairobi, New York, Tokyo and Washington. It also works in close co-operation with local and regional press freedom organisations and with members of the "Reporters without Borders' Network." The index was drawn up by asking journalists, researchers and legal experts worldwide to answer 50 questions about the whole range of press freedom violations (such as murders or arrests of journalists, censorship, pressure, state monopolies in various fields, punishment of press law offences and regulation of the media). In the table below we list the variables included in each of the governance indicators. We use data from 2002 to 2005.

|**Table A24: Reporters Without Borders(16**|**5 developed and d**|**evelo**|**ping c**|**ountr**|**ies)**|||
|---|---|---|---|---|---|---|---|
||2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**||||||||
|Press Freedom Index|X|X|X|X|..|..|..|
|**Political Stability and Lack of Violence**||||||||
|NA|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|NA|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||
|NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|NA|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||
|NA|..|..|..|..|..|..|..|
|Country Coverage|165|165|164|138|..|..|..|

75
_Table 1: AGOA Eligibility_

||**AGOA Eligible**|**Apparel Provision**|**Special Rule**|**Included**|
|---|---|---|---|---|
|**Country**|**Beginning**|**Eligible Beginning **|**for Apparel**|**In Study**|
|Angola|December 2003|||✓|
|Benin|October 2000|January 2004|Yes||
|Botswana|October 2000|August 2001|Yes|✓|
|Burkina Faso|December 2004|August 2006|Yes|✓|
|Burundi|January 2006||||
|Cameroon|October 2000|March 2002|Yes|✓|
|Cabo Verde|October 2000|August 2002|Yes||
|Chad|October 2000|April 2006|Yes||
|Côte d’Ivoire<sup>1</sup>|Restored|||✓|
|Comoros|June 2008||||
|Congo, Rep.|October 2000|||✓|
|Congo, Dem. Rep.<sup>2</sup>|Ineligible-January 2011||||
|Djibouti|October 2000||||
|Ethiopia|October 2000|August 2001|Yes|✓|
|Gabon|October 2000||No||
|Gambia, The|December 2002|April 2008|Yes||
|Ghana<br>|October 2000|March 2002|Yes|✓|
|Guinea<sup>3</sup>|Restored||||
|Guinea-Bissau<sup>4</sup>|Ineligible- January 2013||||
|Kenya|October 2000|January 2001|Yes|✓|
|Lesotho|October 2000|April 2001|Yes|✓|
|Liberia|December 2006|January 2011||✓|
|Malawi|October 2000|August 2001|Yes|✓|
|Madagascar<sup>5</sup>|June 2014|||✓|
|Mali<sup>6</sup>|Restored-December 2013|||✓|
|Mauritania<sup>7</sup>|October 2000||||
|Mauritius|October 2000|January 2001|Yes||
|Mozambique|October 2000|February 2002|Yes|✓|
|Namibia|October 2000|December 2001|Yes|✓|
|Niger<sup>8</sup>|Restored|||✓|
|Nigeria|October 2000|July 14 2004|Yes|✓|
|Rwanda|October 2000|March 2003|Yes|✓|
|São Toméand Príncipe|October 2000||||
|Senegal|October 2000|April 2002|Yes||
|Seychelles|October 2000||No||
|<br>Sierra Leone|October 2002|April 5 2004|Yes||
|South Africa|October 2000|<br>March 2001|No|✓|
|South Sudan<sup>9</sup>|Ineligible 2015||||
|Tanzania|October 2000|February 2002|Yes|✓|
|Togo|April 2008||||
|Uganda|October 2000|October 2001|Yes|✓|
|Zambia|October 2000|December 2001|Yes|✓|

Source: United States Government Accountability Office (2015). ✓ : countries included in study.)<sup>_a_</sup> 7

> _a_ Since 2000, 13 countries have lost eligibility out of which 7 have eventually regained their eligibility. Five including Guinea, Guinea-Bissau, Madagascar, Mali and Mauritania lost eligibility following coups. The Democratic Republic of Congo (DRC) was eligible in 2000, ineligible in 2010 and reinstated in 2011. Madagascar was ineligible between 2010 and 2014 due to a political coup. Among the first entries, Cote d’Ivoire was ineligible between 2005 and 2011 due to political unrest and armed conflict.
<!-- Start of picture text --> Figure 2c. Nicaragua & Costa Rica ‐ Inflation<br>100<br>80<br>60<br>40<br>20<br>0<br>1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983<br>‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5<br>Nicagarua Costa Rica Onset of conflict<br>Annual inflation (%)<br><!-- End of picture text --> Source: Authors’ calculations based on data from Penn World Tables 9.0, International Financial Statistics Database (IMF), and Historical Public Debt Database (IMF)/Abbas et al. (2011).

# **_Example 2. Algeria and Tunisia in the early 1990s_**

The Algerian civil conflict started after controversial parliamentary elections held in 1991. The first year the conflict reached more than 1,000 battle‐related deaths (Definition 3) was 1994.<sup>7</sup> The government subsequently implemented a series of policies aiming to raise public‐sector investment. However, prior to the onset of conflict, a decline of oil prices in the 1980s that lingered well into the 1990s limited the ability of the public sector to finance investment with oil revenues, and thus public debt rose alongside investment (Shabafrouz, 2010).

At that time, Algeria’s non‐conflict neighbor, Tunisia, was in the process of liberalizing its economy. The 1995 association agreement with the European Union and the import‐tariff reforms of early 1990s signaled that Tunisia’s economic management was diverging from Algeria’s. In contrast with Tunisia, public debt in Algeria jumped, reaching its peak of almost 98% of the nation’s GDP in 1992, the same year the conflict saw an upsurge in its levels of violence (Figure 3a). However, it is unclear, given the pre‐ existing oil‐price dynamics, that Algeria’s public debt rose because of the conflict.

In contrast with the increasingly open Tunisian economy, the share of GDP represented by government consumption expanded in Algeria as a result of the growing participation of the state in production (Figure 3b). Shabafrouz (2010) further argues that the low productivity brought by these industrial policies affected the supply of basic staples (including food) during the early stages of the confrontation, which

> 7 See also Gleditsch et al., 2002.

10
The MAPS I remote sensing findings were reported first by Gourlay et al. (2017). MAPS II implemented full‐plot crop cutting for a random sub‐sample of plots, and increased, on each plot, the area for sub‐plot crop cutting (from 4x4m to 8x8m). These decisions were anchored in the concerns around intra‐plot variability of maize yields. Given the enhancements in the scope of crop cutting data in MAPS II and the interest in the validation of satellite‐based approaches to yield estimation, we rely solely on the MAPS II data on 463 households/plots for which sub‐plot crop cutting data are available. The only exception, as explained below, is the plot‐level data on soil fertility, which is sourced from MAPS I. Table 1 provides a breakdown of 463 plots in accordance with pure stand versus (type of) intercropped cultivation status.

**Table 1. Distribution of MAPS II Plots by Cultivation Status**

|**Purestand**|Maize‐Legume|Maize‐Cassava <br>**Int**|Maize‐Legume‐Cassava <br>**ercropped**|Maize‐Other|
|---|---|---|---|---|
|124|119|161|52|7|

# **2.2. Fieldwork**

Three visits were made to each household during MAPS II. During the (first) post‐planting visit, the questionnaire modules included those soliciting information on (1) demographic and socio‐ economic attributes of household members; (2) household dwelling characteristics and ownership of durable assets and agricultural implements; and (3) area, cultivation pattern, management, pre‐harvest labor and seed inputs for all maize plots that were cultivated during the reference rainy season.<sup>6</sup> Following the completion of the household post‐planting interview, each enumerator visited the maize plot that was selected in accordance with the protocol detailed in the previous section. At that time, he/she measured the plot area and saved its boundaries on a Garmin eTrex 30 handheld GPS device, and set up the crop cut sub‐plot for later harvesting and weighing. The crop cut sub‐plot location was chosen at random, in accordance with the protocol that is detailed by Gourlay et al. (2017) and in line with the international best practices.

During the (second) crop cutting visit, the enumerator harvested the crop cut sub‐plots to obtain objectively measured harvest quantities, as detailed in the subsequent section. Finally, during

> 6 A parcel is conceptualized as a continuous piece of land under a common tenure system, while a plot is defined as a continuous piece of land on which a unique crop or a mixture of crops is grown, under a uniform, consistent crop management system, not split by a path of more than one meter in width, and with boundaries defined in accordance with the crops grown and the operator. Therefore, a parcel can be made up of one or more plots. This distinction is key since for the purposes of within‐farm analysis of agricultural productivity, the ideal is to capture within‐parcel, plot area measurements linked with plot‐level measurement of agricultural production

6
municipalities (Table 2, Panel B and bottom panels in Figure 5). However, these findings do not hold when the models are estimated in a narrower sample around the threshold (Columns (3) and (4 )).

Another possibility is that FA could have differential effects among members of the family, especially on those that are the direct recipients of the transfer and more exposed to different sources of information due to the interventions of the program. More specifically, in the case of CCTs, mothers of the beneficiary child are usually designated to receive the transfer and are also required to participate in a number of activities at the community level (e.g. growth monitoring sessions and workshops on good nutrition, health and hygiene practices). This possibly encourages women more than men to interact and exchange information with community leaders and other program participants. We split the sample between women and men to test whether program effects vary with the gender of the parents. Tables 3 and A1 (in the appendix) show the same specifications of Table 2 for these two groups. They indicate that the results for the whole sample are mainly driven by a higher probability to register to vote among women (Table 3). Women are between 1.5 and 3 percentage points more likely to register to vote (panel A “FA”) and 2.6 to 3.5 percentage points more likely to register to vote after the onset of FA in their municipality (panel B “FA”).<sup>16</sup> In contrast, there is no evidence of systematic differences in the intent to vote between FA participant and non-participant men (Appendix Table A1).

Next we look at whether the relatively higher intention to vote attributed to FA translates into actual votes. Considering that voting is not compulsory, this point is of major importance in light of the large historical differences in registration and turnout rates in Colombia. For instance, 44.3% of the population registered in the electoral census casted a ballot in the 2010 Presidential elections<sup>17</sup> . Given the absence of voting records at the individual level, we exploit

> 16The average registration rate for women in the sample of analysis is 95.5 percent.

> 17Source: IDEA, website: `http://www.idea.int/vt/country_view.cfm?id=48#pres`

16
_Model Validation:_ Before using this model for analysis of alternative safeguard measures, we examine its performance relative to historical variation in production and prices. Here, we follow the approach proposed by Valenzuela et al. (2007). Table 1 reports our findings. The first column under “production” is the regional standard deviation of wheat output reported in Valenzuela et al., 2007. Those authors used time series methods to decompose the non-systematic year-to-year variation in wheat output and then used the normalized standard deviation of these residuals to characterize the distribution of supply-side uncertainty in their model. From this column you can see that Australia and Brazil show the most volatile production, followed by Argentina and then Canada.

Since demand for wheat is relatively stable and most shocks to the wheat market come from weather-induced shocks to production, we introduce supply-side shocks as shifts in supply curves for wheat in the model. Specifically, we shock total factor productivity in wheat in each of the model regions. Standard stochastic simulation techniques such as Monte Carlo procedures are cumbersome at best, given the large number of variables in the model so we follow Valenzuela _et al_ . (2007), in approximating the distribution of supply shocks using Gaussian Quadrature. This has been shown to be an efficient means of assessing the consequences of stochastic variation parameters or shocks to CGE models (DeVuyst and Preckel 1997) and its implementation has been automated in the GEMPACK software used to solve our model (Arndt 1996; Pearson and Arndt 2000).

The second column in Table 1 reports the model simulated production volatility, reported as the standard deviation in percentage change of output, from the baseline, assuming symmetric, independent distributions for wheat supply shocks in each of the model regions. By virtue of our sampling strategy, the model results for production are very close to the historical variation<sup>6</sup> . The discrepancy between observed and simulated output volatility is greatest for China and for Canada where the standard deviations in year-to-year output changes differ by 3 percentage points. Because we are mainly aiming

> 6 In fact, we calculate the stochastic shocks to exogenous wheat productivity in each region by computing the shock to TFP required to achieve a given production change, thereupon translating the historical distribution of output shocks accordingly. Since this is a non-linear, general equilibrium model, this approach to calibration is imperfect, and there are some discrepancies between the historical and modelbased standard deviations in production.

12
-7-

within each group.91 However, household surveys often provide a fairly large set of information which can be used to predict income levels. In the next section a method is presented which is based on using predicted values of income given a large number of explanatory variables. Except in very special (and unlikely) cases, it is not exactly equivalent to minimization of expected poverty as given in (4), yet in practice it approximates such a minimization and is both intuitively appealing and computationally simple.

# III. Multivariate Targeting Using Survey Data

Suppose one could predict the incomes of individuals given a set of explanatory variables. If one took these predictions and distributed transfers to the poor under the assumption that these predictions were in fact their true incomes, the more precise one's predictions the more poverty would be alleviated due to better targeting.

Specifically, assume that the income of household i, yi can be predicted by a vector of observable variables **xi** which vary over households: The error term ei accouncs for the error in the prediction. One does not have to assume that zi causes yi, but simply that the variables in the vector xi 9/ In fact, these assumptions allow Ravallion and Chao to calculate actual (as opposed to expected) poverty since all possible outcomes of the joint probability distribution are permutations of each other.