rafmacalaba/datause-encoder-data huggingface dataset:
converge to its steady state and vice versa.<sup>6</sup> More essential for our analysis, the parameter 0 < θ < 1 and the associated persistence term also reflect an echo from past changes in the variables _x_ , consistent with the steady-state dynamics. To see this point, suppose that a change in variable _x1_ (for example, education) happens in period _t_ in country _c_ . This pushes up the corresponding steady-state income level in this country. But it is unlikely that the full effect of this change will materialize immediately in period _t_ . Instead, growth only increases by β _1_ Δ _x1,ct_ . However, this increase in the growth rate will be reflected as persistence θ∙β _1_ Δ _x1,ct_ in period _t+1_ , as θ<sup>2</sup> ∙β _1_ Δ _x1,ct_ in period _t+2,_ and so on. The persistence term hence reflects an “echo” from past structural improvements.

We consider variables that can be viewed as sources of economic growth and that can be influenced by country-specific policies (“structural improvements”). Global trends in those variables are captured by the time-specific shocks Δδ _t_ . We are not interested in “proximate sources” of economic growth, such as capital accumulation or productivity, which are usually explored in growth accounting. Rather, we are interested in variables that represent country- and time-specific factors that in turn will influence those proximate growth drivers, such as financial conditions, FDI, and trade openness. To facilitate the presentation of those changes, we group them into six categories: trade and FDI, finance, macro, infrastructure, demography, and political stability.

# 3.2 Data

We construct a comprehensive data set covering 149 countries and ranging from 1970 to 2019, although the demographic variables that we include in our preferred specification limit the sample to 128 countries. Tables A.1 to A.3 in the appendix summarize all the variables and data sources as well as descriptive statistics. The summary statistics highlight that Bangladesh has an average per capita income level that is about one-fourth of the sample mean, with much lower FDI, infrastructure, and credit/GDP levels than the sample mean.

For GDP per capita, _y_ , we take _rgdpna_ divided by _pop_ from the Penn World Tables (PWT 10.0; see Feenstra, Inklaar, and Timmer 2015), which measures income per capita in constant 2017 US$. Purchasing power parity (PPP) adjustments are not necessary since we only explore variation over time within countries and could even induce unwanted short-term, country-specific fluctuations in the PPP income variable (Inklaar et al. 2021).

> 6 This can easily be seen from rewriting equation (1) as: Δln _yct_ = γln _yc,t-1_ + β _1_ Δ _x1,ct_ +...+β _k_ Δ _xk,ct_ + Δδ _t_ + _uct_ , with γ = (θ-1). For θ < 1, it follows that γ < 0 and a country with a lower income level _y_ in period _t_ will have a higher growth rate in the subsequent period _t + 1_ , ceteris paribus.

-7-
Table A.10: Baseline balance, restricted to sample with endline student assessment data

||(|1)|(|2)||(3)|T|-test|
|---|---|---|---|---|---|---|---|---|
||Co|ntrol|In-|Kind|Reco|gnition|Dif|erence|
|Variable|N|Mean|N|Mean|N|Mean|(1)-(2)|(1)-(3)|
|_Student-level (grade 2)_|||||||||
|Math|6346<br>[109]|0.019<br>(0.076)|6643<br>[109]|0.078<br>(0.067)|6311<br>[112]|-0.109<br>(0.078)|-0.059|0.129|
|Language|6346<br>[109]|0.024<br>(0.070)|6643<br>[109]|0.057<br>(0.067)|6311<br>[112]|-0.030<br>(0.064)|-0.033|0.055|
|Female|6334<br>[109]|0.476<br>(0.008)|6639<br>[109]|0.477<br>(0.009)|6294<br>[112]|0.469<br>(0.008)|-0.001|0.007|
|Age|6153<br>[109]|9.423<br>(0.067)|6398<br>[109]|9.561<br>(0.092)|6101<br>[112]|9.563<br>(0.075)|-0.138*|-0.140**|
|_Student-level (grade 3)_|||||||||
|Math|5846<br>[111]|0.018<br>(0.079)|6129<br>[113]|-0.050<br>(0.068)|5932<br>[107]|-0.117<br>(0.077)|0.069|0.136|
|Language|5846<br>[111]|0.051<br>(0.079)|6129<br>[113]|-0.003<br>(0.063)|5932<br>[107]|-0.106<br>(0.085)|0.054|0.157|
|Female|5840<br>[111]|0.479<br>(0.009)|6116<br>[113]|0.473<br>(0.009)|5928<br>[107]|0.479<br>(0.008)|0.006|0.000|
|Age|5727<br>[110]|10.968<br>(0.071)|5996<br>[113]|11.082<br>(0.079)|5815<br>[107]|11.044<br>(0.067)|-0.115|-0.076|
|_School-level_|||||||||
|No. grade 2 students|109|58.220<br>(3.631)|109|60.945<br>(4.444)|112|56.348<br>(3.563)|-2.725|1.872|
|No. grade 3 students|111|52.667<br>(3.303)|113|54.239<br>(4.396)|107|55.439<br>(3.945)|-1.572|-2.773|
|Headteacher female|129|0.194<br>(0.035)|132|0.174<br>(0.033)|127|0.244<br>(0.038)|0.020|-0.050|
|Headteacher age|123|49.008<br>(0.787)|129|47.961<br>(0.877)|123|46.976<br>(0.922)|1.047|2.033*|
|No. deaths, Ebola|98|15.224<br>(4.347)|101|11.446<br>(3.624)|99|11.606<br>(3.381)|3.779|3.618|

_Notes_ : The value displayed for t-tests are the differences in the means across the groups. Standard errors, in parenthesis are clustered at the school level. Observations with square brackets indicate number of clusters. Strata fixed effects included in all estimation regressions. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

11
-109-

# STATISTICAL APPENDIX (NOTES)

# Introduction

This appendix presents the basic census material we have used and the detailed procedure for matching products. It is intended to be fully transparent in the sense that it gives enough detail for other scholars to replicate or modify our procedures. The tables are arranged by industry, and numbered Al to A17. Their order follows their sequence within the ISIC major division "manufacturing".

# The industries covered are:

|ISIC<br>Code|ISIC Three DigitMajor<br>Industry Groups|Our<br>Code|<br>Our Sample Industries<br>|
|---|---|---|---|
|311/2|Food Products|Al<br>A2|Grain Mill Products<br>Sugar and Sugar Products|
|313|Beverages|A3|Malt and Malt Beverages|
|314|Tobacco|A4|Tobacco and Tobacco Products|
|321/2|Textiles, Wearing Apparel|A5|Textiles|
|323/4<br>331|Leather Products, Footwear<br>Wood Products|A6<br>--|Footwear and Leather Products<br>not represented|
|332|Furniture|--|not represented|
|341|Paper and Paper Products|A7|Pulp and Paper|
|342|Printing and Publishing|--|not represented|
|351/2|Chemicals|A8<br>A9|Soap and Detergents<br>Paints|
|||A10|Agricultural Fertilizers|
|353/4|Petroleum Refining.<br>Petroleum and Coal Products|All|Petroleum Refining and Products|
|355/6<br>361/2/9|Rubber and Plastic Products<br>Stone Clay and Glass|A12<br>A13|Tires and Inner Tubes<br>Hydraulic Cement|
||Products|A14|Bricks, Tiles and Clay<br>Refractories|
|371/2|Iron and Steel, Non Ferrous<br>Metals|A15|Iron and Steel|
|381/2/3|Metal Products, Machinery,|||
||Electrical Machinery|A16|Radio and TV Receivers|
|384|Transport Equipment|A17|Motor Vehicles and Equipment|
|385/90|Miscellaneous|---|not represented|

For each industry there are 13 tables, e.g. for industry Al, Grain Mill Products, the detailed tables are numbered from A1.1 to A1.13. At the end of this introduction we present some specific notes on industries.
behaviors and practices to a set of scores on various scales. Enumerator and raters for SDI, Stallings and _Teach_ each received 4 days of training which included a day at a school implementing the tool. For CLASS, raters received two days of training after which they could practice independently online prior to taking a certification test.

|**Box 1: Summ**<br>|**ary of classroom obser**<br>|**vation instr**<br>|**uments used**<br>|
|---|---|---|---|
|Instrument|Administration|Time-<br>on-task|(Other) Areas of focus|
|SDI|In-person|Yes|Checklist of observed teacher behaviors, availability<br>and use of materials, and classroom infrastructure.|
|Stallings|In-person|Yes|Checklist of observed use of materials.|
|CLASS|Video|No|Rater scoring across various dimensions grouped into<br>3 domains (Emotional support; Classroom<br>organization; Instructional support) plus rating of<br>Student Engagement.|
|Teach|Video|Yes|Rater scoring across various dimensions grouped into<br>3 areas (Classroom culture; Instruction; (promotion<br>of) Socioemotionalskills.|

# **_Service Delivery Indicators instrument_**

The SDI instrument consists of two main parts. In the first part, an enumerator observes a full lesson and records minute-by-minute what the teacher is doing against a set of predefined activities. Activities include descriptors such as “Teacher interacts with all children as a group” or “Teacher supervises pupil(s) writing on the board” or “Teacher in class - not teaching” (see Appendix 1 Table 1 for the full list of recorded activities). Every five minutes, the enumerator also carries out a spot-check and records the number of students who are “off-task.”<sup>12</sup> We use these data to construct two variables. First, the Share of Time Teaching, which is the share of minutes in which the teacher is recorded as engaged in teaching activities. Second, the Share of Time Teaching and Learning which is the share of time in which the teacher is engaged in teaching activities and in which no more than 6 students are “off-task.” This is operationalized by multiplying the Share of Time Teaching by the share of spot-check observations in which no more than 6 students are off-task.

The second part of the SDI instrument is completed by the enumerator after the lesson is complete and consists of a series of questions on teacher behaviors and practices that were observed during the lesson, along with questions about the availability and use of materials as well as classroom infrastructure. We group these into 5 aggregates: Good Teacher Demeanor, Good Pedagogical Practices, Classroom

> 12 Off-task in SDI is defined as: Chatting or interacting with other students about issues not related to the lesson; fighting, playing or having physical interaction unrelated to the lesson with other students; being disciplined; sleeping, day dreaming, or not paying attention; distracted by an activity or event inside or outside the classroom.

8
While documented procedures for borrowing in domestic markets was a dimension of debt management where, on average, small states performed well, very few capitalize on this to prepare, publish and adhere to an annual plan for the aggregate amount of local currency borrowing in the domestic market, divided between wholesale and retail markets. Only 2 small states met the minimum requirement for this dimension of debt management (DP8.1).

# **_Debt management strategy_**

Small states also failed to capitalize on their capacity to coordinate between fiscal and monetary policy to improve monitoring of key fiscal variables and formulate a medium term debt management strategy. Only two of the small states assessed produced a medium term debt management strategy that met the minimum requirements on the DeMPA framework. Of the 17 small states assessed 10 do not formulate and publish a debt management strategy and only four of the seven that do so were found to have proper decision process for developing and updating the debt management strategy regularly(DPI3.2).

# **Debt Management Capacity in Small States Compared to Other Developing Countries**

The DeMPA findings show that, on average, the share of small states that meet the minimum requirement in each dimension of debt management falls well below that of other low and middle income countries (Figure 6).

**Figure 6: Share of Countries Meeting the Minimum Requirements of Debt Management - Comparison of Small States and Other Developing Countries**

_Percent_ <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Non-small states Small states<br>DPI-1-1 DPI-2-1 DPI-2-2 DPI-3-1 DPI-3-2 DPI-4-1 DPI-5-1 DPI-5-2 DPI-6-1 DPI-6-2 DPI-7-1 DPI-7-2 DPI-7-3 DPI-8-1 DPI-8-2 DPI-9-1 DPI-9-2 DPI-9-3 DPI-10-1 DPI-10-2 DPI-10-3 DPI-11-1 DPI-11-2 DPI-12-1 DPI-12-2 DPI-12-3 DPI-12-4 DPI-13-1 DPI-13-2 DPI-13-3 DPI-14-1 DPI-14-2 DPI-15-1 DPI-15-2 DPI-15-3<br><!-- End of picture text --> Source: DeMPA reports

# **Measures to Improve the Debt Management Capacity of Small States**

18
# **3.3.5 Data models, Internet access, and institutions**

Much of digital services trade takes place over the internet. This is especially true for information services, telecommunications, and computer services, because they rely extensively on internet technologies that enable their tradability. Having access to an open internet is therefore an important factor to promote trade in digital services. This importance is substantiated in Figure 9, which shows a positive relationship between countries’ greater access to the internet and per capita digital services trade. The access to an open Internet is measured through the Freedom on the Net indicators of the Freedom House, which cover the extent to which the internet in each country experiences infrastructural and economic barriers to access, government efforts to block specific internet technologies, as well as legal, regulatory and ownership control over internet and mobile phone access providers, and the independence of regulatory bodies.

Figure 9: Digital services trade, data models, and Internet access (2015) <!-- Start of picture text --> Digital services trade and data model<br>By obstacles to internet access<br>100<br>10<br>1<br>.1<br>5 10 15 20 25<br>Free from obstacles to internet access<br>Open Coditional Limited Free Internet No Free Internet<br>Per capita exports in 1000 Mln. USD (log)<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data, World Bank WDI and Freedom House. Countries are categorized on the basis of the data model regarding the cross-border data transfers.

This proxy indicator of internet access from the Freedom House forms part of a wider assessment of whether the Internet is qualified as free, partly free and not free. Thus, besides economic access to the internet, the institute assesses whether the Internet is free on the basis of many other noneconomic criteria, such as content access and free speech, which are then combined.<sup>18</sup> Figure 8 shows that all countries that are classified as having a free internet share either the conditional or 18 Besides economic access to the internet, the other two categories on which this indicator measures whether the Internet is free, partially free or completely free are (a) whether there are limits on Internet content, such as legal regulations, technical filtering or self-censorship; and (b) whether there are violations of user rights, such as privacy, cyberattacked and repercussions for online speech.

22
# **B.1 Computing corporate profits**

We start by creating a dataset of corporate income tax (CIT) declarations for every year in the period 2003 - 2019. We compute firms’ total profit as the difference between total revenue and total allowable costs. We include non-taxable revenue in that computation, so our measure of profit is broader than simply taxable profits. It also includes profits for firms that are exempt from income tax or have tax credits - these are separately documented after firms’ compute their tax liability.

In Table B1 we present some key descriptive statistics of that dataset. The number of corporations filing income taxes increases substantially in the period, from less than 11,000 in 2003 to over 33,000 in 2019. In Column (2), we present the aggregate profits declared by these firms. In the period 2016-2019, aggregate corporate profits fluctuate in the of L45 - L55 billion. in the levels of range Noteworthy changes aggregate profits happen in 2015, when profits increase by 60%, and then in 2018, when they fall by 20% from 2017. These large changes in aggregate declared profits are likely driven by the introduction and then phasing-out of a corporate minimum tax that dampened the incentives of large corporations to report low profit margins (cite minimum tax paper). Note that when we compare these values, computed from CIT declarations, with macroeconomic aggregate of property income flowing to corporations in Table C1, our numbers are much lower - in 2019, for example, corporate income is L113 billion in macro aggregates vs. L56 billion in our dataset. These discrepancies are likely driven both by the precise definition of these aggregates in each dataset, as well as the inherent challenge of estimating these quantities.

We also highlight three other features of our data. First, a non-negligible share of corporations declare losses – 20-30% of total CIT filings every year. That implies that when we assign undistributed corporate profits to individuals, some of them will be assigned negative incomes - in some cases, these losses are large enough to completely compensate for higher incomes and ”exclude” individuals with very large distributed incomes from the top of our distribution. Second, before distributing corporate profits to shareholders, we net out the total amount of dividends distributed by the corporation in the same fiscal period. In Column (4) we report the aggregates of corporate profits net of distributed dividends. On average, corporations distribute 15-25% of declared profits as dividends each year. Note that we do not observe distributed dividends before 2011, so pre- and post-dividend profits are equal. For that reason, macro adjustments for corporate profits will look differently in both periods, but incomes should be consistent over time after matching macroeconomic aggregates.

48
growth advantage is sustained among older girls, suggesting potential inter-generational bene fi ts of averting nutritional and other health shocks for girls in early childhood. These results are further validated by restricting the sample to migrant households and comparing the growth outcomes of siblings before and after migration.

The next section of the paper provides the context for our study. Section 3 presents the estimation and identi fi cation strategy and presents some preliminary evidence on gender differences in the impact of migration on child nutritional status. We test our main proposition in section 4. In 4.1 we use our instrumentation strategy to examine gender differences in the impact of migration in the full sample of households. In section 4.2, we restrict attention to migrant households and examine differences in the nutritional status of children, by gender, born before and after migration within the same household. Conclusions and implications follow in section 5.

# 2 Data and Context

## 2.1 Migration

More than one in four households in rural Pakistan have at least one migrant member. Migrants are typically adult males, who move temporarily to an international or domestic urban destination in search of employment leaving their families in the village.<sup>9</sup> Most maintain very close ties with their origin households and communities, returning frequently and sending substantial remittances.<sup>10</sup> This makes the context particularly useful for examining the impact of migration on outcomes in the sending community.

The study uses data from the Pakistan Rural Household Survey (PRHS) 2001-02, which collected detailed information on migration for each household member.<sup>11</sup> Complete data are available for 2531 rural households in 143 villages in 16 districts across all 4 provinces. The survey contains detailed household and individual characteristics, including demographics, occupation, health, education, investments, assets, household expenditure, and the migration experience of all household members. For migrants, data were also collected on the year and duration of migration, migration destination, remittances, and

> 9 Close to 80% of migrants in our study report having undertaken migration in search of employment.

> 10 See Addleton (1984), Kazi (1989) and Arif (2004) for a review of migration patterns in Pakistan.

> 11 In the PRHS, all individuals who were away from the household at the time of the survey, were classi fi ed as households members, provided they were regarded as members of the household before they left and had not set up a permanant home elsewhere. This enabled collection of all relevant data on current migrants.

5
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_Table 5 Regression of annual consumption on treatment status. The dummy variable for treatment by T2, the diary arm, is the omitted category. All results (except for column 2) are for per capita FAFH. Column 1 shows original results without robustness checks, column 2 shows a regression of per capita FAFH spending (for adults in the household only) on treatment groups, column 3 shows a regression of winsorized (at 1st and 99th percentiles) FAFH spending on treatment groups, column 4 includes various additional covariates,_ _<u>column 5 includes controls for day of the week of the first visit, column 6 drops “incomplete” households, and column 7 excludes replacement households</u>_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Original results|Adults in HH|Winsorized|Including<br>covariates|With day of week FE|Without missing|Excl. replacement<br>households|
|T1: 7-day one-line recall|-2,818***||-2,848***|-2,606***|-2,754***|-2,686***|-2,683***|
||(548)||(482)|(535)|(553)|(556)|(634)|
|T3: 7-day individual recall|-1,847***|-1,866***|-1,681***|-1,710***|-1,665***|-1,377**|-1,917***|
||(549)|(522)|(483)|(536)|(581)|(568)|(646)|
|T4: 7-day HH bounding/salience|-883|-941*|-799*|-976*|-710|-751|-459|
||(549)|(522)|(483)|(535)|(569)|(557)|(635)|
|HH head is male||||-346||||
|||||(399)||||
|HH head is university-level||||3,122***||||
|||||(451)||||
|Number of HH members||||-1,429***||||
|||||(400)||||
|Number of in-HH members||||956**||||
|||||(459)||||
|Number of children in HH||||346||||
|||||(281)||||
|Owns computer||||2,389***||||
|||||(400)||||
|Constant|8,527***|7,555***|8,302***|8,360***|8,422***|8,395***|8,436***|
||(389)|(370)|(342)|(673)|(398)|(400)|(449)|
|Observations|1,904|1,426|1,904|1,888|1,904|1,838|1,491|
|R-squared|0.015|0.009|0.020|0.081|0.017|0.014|0.015|
|T1=T3|0.08||0.01|0.09|0.06|0.02|0.23|
|T1=T4|0.00||0.00|0.00|0.00|0.00|0.00|
|T3=T4|0.08|0.08|0.07|0.17|0.10|0.26|0.02|
|Standard errors in parentheses||||||||

*** p<0.01, ** p<0.05, * p<0.1

22