rafmacalaba/data-use-mentions-extended huggingface dataset:
income bracket as the bracket’s average annual per capita income, computed from Colombia’s household survey data (SEDLAC). Lacking student-level data on tuition expenses, we estimate the tuition paid by students of a given _y_ as the average annual tuition paid by students from the corresponding income bracket at public institutions, calculated from SNIES and SPADIES.<sup>15</sup> Appendix Table A3 shows the resulting income and tuition by family income bracket. Income varies greatly across income brackets but tuition varies less. Although public institutions provide income-based tuition discounts, the highest income individuals do not pay proportionally to their income: their per capita income is about twenty times as large as that of the lowest-income individuals, yet their tuition is only 2.5 times as large.

### **6.2.2 Workers**

We use household surveys to compute the average wages earned by individuals with different educational attainment and experience in 2005. For workers aged 18-65, the average wage of a college graduate, a college dropout with at least one year of complete college, and a college dropout with less than one year of complete college is 160, 58, and 28 percent higher than the average wage of a high school graduate respectively (see Appendix Table A4).<sup>16</sup> Among college (high school) graduates, the average wage of experienced workers is 35 (29) percent higher than the average wage of inexperienced workers. Consistent with the data, we assume that the returns to experience of college dropouts are the same as those of high school graduates.

## **6.3 Computing predicted values**

Since the model does not have a closed-form solution, we use a numerical algorithm to solve students’ dynamic optimization problem for a given value of Θ. Appendix C provides a full description of the algorithm. The estimation of **_δ_** and **_ξ_** is nested within the model solution for a given of value of
Table A2 presents the results of balance checks of baseline characteristics across the different treatment groups and control group. Overall, it shows that all groups are relatively well balanced with respect to observable characteristics: the number of tests that are statistically significant is close to what should be expected due to chance (2 out of 15 tests for the joint tests of all coefficients are equal to zero are significant at the 10% level).

# **3.4 Data**

Three main sources of data are used for this study: administrative data on formalization and program implementation, in-person quantitative surveys with business owners, and qualitative data with study participants and implementing agencies.

Our main measure of formalization is based on monthly administrative data on business registration provided by the GUFE. This database includes the complete list of all newly registered businesses for all legal statuses. Since most businesses in the control group would not have been aware of the new _entreprenant_ status, this measure will capture any alternative legal status they registered under. Appendix 3 describes the matching process used to identify whether firms in the GUFE database came from our sample.

Other main outcomes on business performances (profits and turnover) and intermediate outcomes like business knowledge and practices, taxes and banking were measured through inperson interviews with business owners. The baseline survey of the selected sample of businesses was conducted in March-April 2014 prior to program implementation. Two followup surveys were conducted in April-June 2015, and in May-June 2016. Attrition rates at first and second follow-up surveys were 11.8 percent and 15.9 respectively and were not correlated with treatment status. Two years after the baseline survey, 8.6 percent of the businesses had closed their operations, and business closure was also not correlated with treatment status. Table A3 presents survey rates, closure rates and attrition rates by groups.

Balance checks of baseline characteristics across the different groups on the sample of businesses successfully surveyed at the two years follow-up survey show that, overall, the postattrition sample is relatively well balanced with respect to observable
an increase in international integration with e.g., exports growing much faster than income, making it interesting to analyze the link between international activities and the demand for skilled labor.

Our analysis draws on survey data recently collected by the World Bank from manufacturing establishments in Brazil and China under the Investment Climate Assessment initiative.<sup>19</sup> The surveys applied a (mostly) common questionnaire covering a variety of topics (e.g., infrastructure, fi nance, etc.) to random samples of establishments and were implemented in China in 2001 in partnership with a local statistical institute and in Brazil in 2003 in partnership with a local consulting fi rm.<sup>20</sup> The random samples were drawn from a sampling frame of manufacturing establishments in each country. The sample design varied by country: (i) in Brazil, strati fi ed samples based on industry-state-size were drawn for thirteen states and all the regressions estimated in the paper are weighted accordingly and (ii) in China, the sample was randomly chosen for each of ve cities.<sup>21</sup> Both surveys cover different industries and regions and the entire range of plant sizes, but the exact composition of the samples differs across countries. In Table 2, we show the distribution of plants across industries (Panel A) and across size groups (Panel B) for each country.<sup>22</sup> The apparel, auto-parts and electronics industries are covered in both countries. Large plants represent about a half of the sample for Brazil whereas for China the majority of plants in the sample are small.

> 19More information on the initiative can be found at http://www.worldbank.org/privatesector/ic/.

> 20The survey questionnaires also included some country speci fi c questions. For our purposes, the relevant survey questions relate to the workforce, technology and innovation, production and costs, ownership structure, trade and manager characteristics.

> 21In
## **C. Matching with Administrative Data and Attrition**

The average matching rate of administrative data of enrolled children was 94% at baseline and 97% at follow-up. We present these estimations in Table A2 in the Appendix section. All the matching rates were balanced between T and C groups, except for the matching rate of science scores before the intervention. To account for this difference, we first impute the course average at baseline for those missing observations. Then, we include this variable with imputed values and a dummy of missing values in estimations of academic outcomes. Further details of these estimations are explained below.

As we show in Table A2 in the Appendix section, 968 of 1,056 baseline enrolled children (92%) were surveyed at endline. In the T group, 731 of 798 students (91%) were surveyed at endline, and in the C group, 237 of 258 (92%). This is a low attrition rate compared to those levels found in similar studies of at-risk youth. We regress the attrition dummy on the T dummy and the result shows no difference in the likelihood of attrition between the T and C groups. Therefore, results will not be driven by the absence of follow-up survey data for any group.

Finally, the average matching rate of administrative data of nonenrolled students was 85% at baseline and 98% at follow-up. In estimations using the nonenrolled sample, we follow the same imputation procedure for baseline variables as in the sample of enrolled children.

# **2.5 Summary Statistics**

Descriptive statistics of the full sample and T and C groups are shown in Table 1. Column (1) exhibits statistics for the total sample and columns (2) and (3) are for C and T groups, respectively. Column (4) shows unadjusted _p_ -values for tests of differences in means between T and C means, and column (5) presents adjusted _p_ -values for multiple hypothesis testing of means and FWER
# The Data

Our data set builds on that of Rose (2004)<sup>21</sup> . Unless otherwise stated, the data are from that source; see our Table 1 for a concise description. Most of the variables from that source are standard and do not merit an elaborate description here (see Rose (2004) for complete details), except for the regress and in all our estimations: the log of _average_ bilateral trade flows. The direct source for this variable is the IMF’s “Direction of Trade” dataset. This variable is the average of all four potentially available trade flows between a country-pair (exports from _i_ to _j_ , imports into _j_ from _i_ , etc’). This implicitly imposes symmetry on the effects of the determinants of trade, which means that we are estimating the effect of a change in the regressors both on exports and imports<sup>22</sup> . This seems to be a plausible assumption for some regressors (e.g., distance), but not for all (e.g., tariffs vis-a-vis a trading partner). We deal with this issue below.

We focus our study on the 1984-1999 sample due to restrictions on the availability of data on institutional quality. Our measure of dissimilarity of institutional form is a dummy for different legal origins. This dummy is equal to 1 when a country-pair does not share the same legal origin and zero otherwise. Legal origins are from Djankov et al. (2003) and the CIA Factbook. Two of our measures of institutional quality are Bureaucratic Quality and Control of Corruption; they are taken from the commonly used International Country Risk Guide (ICRG) dataset, are available from 1984 and cover 135 countries for which we have trade data, although coverage is not complete in all years. Our third measure of institutional quality is Protection of Property Rights; it is taken from the Heritage Foundation dataset, is available from 1995 and

> 21 Available at Rose’s web site
Figure A6.2 in the appendix displays maps of conflict events before program initiation and between baseline and endline survey. The difference in conflict exposure between control and treatment group is only significant at the 90 % level. To control for potential selection bias, we include conflict exposure as control variable in our regressions. In addition, we find no evidence that attrition depended on the selection of the original treatment group (Appendix table A1.1), nor differential attrition across covariates between these two groups (Appendix table A1.3).<sup>8</sup> Importantly, there is also no evidence that participants in the control group accessed either the training or the grants. What is more, the low geographic concentration of program participants makes spill‐over effects unlikely. Hence, control group outcomes can plausibly be regarded as counterfactual outcomes for beneficiaries in the absence of the program.

The second stage of the selection process decided which _de facto_ treatment participants of the original treatment group received. Since the cancellation of the program was not planned, this process was not systematically controlled. Among the original treatment group participants reached through the endline survey, we have three _de facto_ groups. The “Training but no grant” group consists of the 408 individuals that had not accessed their grants when erupting violence forced the program to terminate in late 2015, the “Training and grant” group consists of the 210 individuals who successfully accessed the grant, and the “Non‐compliers” group consists of 132 individuals who did not attend the training and therefore could not access the grant.<sup>9</sup> The assignment process to these de facto treatment arms poses some challenges for identification. Participants had no reason to anticipate that the grant disbursement might be frozen in the future, the assignment to “Training and grant” and “Training, but no grant” was partly random. However, participants who tried to access the grant right after the training had a higher chance of receiving it than those who
# **Export Variety and Country Productivity**

Robert Feenstra Hiau Looi Kee

## **Abstract**

This paper provides evidence on monopolistic competition models with endogenous technology by studying the effects of sectoral export variety on country productivity. The effects are estimated in a translog GDP function system based on data for 34 countries from 1982 to 1997. Country productivity is constructed and export variety is shown to be significant. Instruments such as tariffs, transport costs, and distance are shown to affect country productivity through export variety, and only through this channel. Overall, while export variety accounts for only 2% of cross-country productivity differences, it explains 13% of within-country productivity growth. A 10% increase in the export variety of all industries leads to a 1.3% increase in country productivity, while a 10 percentage point increase in tariffs facing an exporting country leads to a 2% fall in country productivity.
, 2014). Following this definition, we include population density (people per sq. km of the district or state area), percentage of cropland, and urbanization of the district or state area. The data source of type of land is from the MODIS 500m Landcover Dataset NASA LP DAAC at the USGS EROS Center, retrieved from Google Earth Engine Data Catalog based on the 2018 Images LC type 1 with 17 classes of land use. We employ class 12 for the cropland area (where at least 60% of the area is cultivated), class 13 for the urban area (at least 30% is impervious surface area, including building materials, asphalt, and vehicles), and the sum of all the classes for the total area of the administrative units at level 2.

Table 3 summarizes the average and standard deviation of the main variables of hazards and exposure at administrative level used in the analysis. The results indicate that Central America and Mexico face an elevated potential damage from cyclones and a higher frequency of occurrences of this hazard. In South America, floods are more prevalent, but countries like Peru and Ecuador evidence on average a higher potential damage from landslides than floods.

10
2036|239|56 %|69 %|62 %|
|4. Travellingand commuting|2036|316|80 %|55 %|65 %|
|5. Eatingand drinking|2036|380|68 %|55 %|61 %|
|6. Resting|2036|234|77 %|75 %|76 %|
|7. Hunt orgather foodstuffs|2036|149|79 %|86 %|82 %|
|8. Personal care|2036|345|91 %|67 %|77 %|
|9. Clean the house,wash,or iron|2036|170|79 %|93 %|86 %|
|10. Fetch water|2036|179|78 %|93 %|85 %|
|11. Work for a wage or salary|2036|37|89 %|99 %|94 %|
|12. Running or working in household<br>business|2036|40|85 %|99 %|92 %|
|Accuracy|||||77 %|
|Macro average F1-score|||||76 %|

The prediction scores demonstrate that our model accurately classifies the reported time use activities in over three-quarters of the cases, using the data from the physical activity sensor signal. To further validate the model's predictive power, we conducted a sensitivity test by varying the dataset size. As the dataset size increases, the prediction scores exhibit a positive trajectory, indicating the model's capacity to improve its performance with growing datasets. This evidence substantiates that time use activities can be reliably predicted using sensor data, effectively addressing our main research question.

For the trained eye **Table 2** also illustrates variations in prediction scores among different activities. Interestingly, despite having balanced train and test sets, the activities less frequently reported prior to the creation of the balanced dataset for analyses (see **Figure 1** ) exhibit higher prediction scores.
indexes. In these two cases, the ranking is consistent across indexes and easier to use for the purpose of aid allocation.

Is this ranking related to the actual distribution of EU social funds? Table 3 shows the indicative allocation of cohesion funds across the eight countries considered and the rank, which can be compared to the rank in Table 2. In first and last position we find Romania and the Czech Republic and this matches perfectly with all indicators in Table 2. The IL index which better approximates the rank of the actual distribution of funds is _IL_<sup>_progA_</sup> , where all ranks are the same, except for Slovakia and Estionia.

Table 3: Indicative allocation of Cohesion Fund, 200713 (2007 prices, in % GDP)

|Country<br>Cohesion|Fund (%GDP)|rank|
|---|---|---|
|Czech Republic|6.69|8|
|Estonia|7.17|5|
|Hungary|8.69|2|
|Latvia|7.32|4|
|Lithuania|8.02|3|
|Poland|7.13|6|
|Romania|18.94|1|
|Slovakia|7.11|7|

_Source: our elaboration on European Union (2007) and Eurostat for GDP_

# **6 Ranking poor countries for North-South transfers**

Table 4 summarizes poverty and income lever indexes together with the country ranks obtained by computing each index for eight African countries based on the Global Income Distribution Database (GIDD). This is a database constructed by the World Bank that homogenized a number of standard variables including income and consumption across household consumption surveys worldwide.<sup>3</sup> The welfare variable used is household equivalent consumption at Purchasing Power Parity (PPP) in USD where the equivalence scale is the square root of household size. To compute the indexes, we define as poverty line _z_ the World Bank official poverty
Policy Research Working Paper 10612

# **Abstract**

This paper examines the effect of exposure to extremist ideologies in the home country on the subsequent integration of refugees in host countries. For this purpose, it combines a rich census of Afghan refugees living in Tajikistan in 2023, following the Taliban’s takeover in Afghanistan, with uniquely scraped district-yearly data on the territories controlled by the Taliban, U.S. allies, and contested territories between the two factions between 2017 and 2021. The empirical strategy compares the integration outcomes of refugees who experienced varying exposure to extremism generated by the exogenous and sudden dramatic shift in Taliban’s territorial control in their province of birth between 2017 and 2021. Findings suggest that refugees who were born in provinces with increased Taliban territorial control between 2017 and 2021, despite having comparable pre-migration characteristics to refugees born elsewhere in Afghanistan, are less integrated into their Tajik host communities than the other refugees. These refugees also show lower educational levels and more mental health problems.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sandrarozo@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of
The last rows of Panel C of Table 5 show the decomposition of long-run export growth of continuing exporters in their continuing destinations across the HS 6-digit product dimension. Continuing products account for 77% of the growth of continuing exporters in their continuing destinations during the whole 2002-2008 period. But the introduction of new products by continuing exporters in their continuing destinations in gross terms plays a meaningful role, accounting for more than a third of that growth. This implies that the new products that survive see their export volumes grow fast subsequently. During the whole 2008-2011 period the thin export growth of continuing exporters in continuing destinations (1.6%) is entirely driven by the positive contribution of net entry of products given that growth in continuing products is actually negative (-1.2%).

Overall, there is a high degree of churning across HS 6-digit products, thus a high degree of reallocation of resources within continuing Turkish exporters in their continuing destinations. The pattern of turnover across HS 6-digit products for Turkish continuing exporters in their continuing destinations shows a similar role for the product extensive margin as what is shown for Portuguese continuing exporters for which Amador and Opromolla (2012) report that exports of continuing products account for about 75% of short-run export growth in continuing destinations.

Finally, to exploit the unique highly disaggregated nature of the product classification in the Turkish dataset, we present in Panel D of Table 5 the decomposition in Eq. (3) focusing on products at the HS 12-digit level. While continuing HS 12-digit products represent the bulk of short-run export growth in continuing destinations by continuing exporters in every pair of consecutive years from 2002 to 2011, the role of net entry is much larger than was the case in Panel C for HS 6-digit products. This can be rationalized by the fact that firms can innovate in their continuing destinations by introducing small variants of their products through a new HS 12-digit variety within the same HS 6-digit and dropping other variants of their products at relatively low cost. Interestingly, the last rows of Panel
2009. Their essential strategy to identify ...nancially fragile households relied on the following question: “How con...dent are you that you could come up with $2,000 if an unexpected need arose within the next month?” The $2,000 ...gure is chosen because it is of the same order of magnitude as the cost of an unanticipated major car repair, a large co-payment on a medical expense, a legal expense, or a home repair. For the survey conducted in the United States, 22 percent of households responded with certainty that they would not be able to come up with the funds and some other 28 percent reported that they would probably not be able to come up with those funds.

# 4.1 Database used

We use individual-level data from the World Bank’s Global Findex which is a comprehensive database on how individuals 15 years old and above save, borrow, make payments, and manage ...nancial risk. The data are collected in partnership with Gallup through nationally-representative surveys of more than 150,000 adults in over 140 economies (i.e., covers about 1,000 individuals per country as in Lusardi et al. 2011). Global Findex has been published every three years since 2011. We use its latest survey for the year 2017 because it allows one to identify all types of social transfers. Previous surveys of 2011 and 2014 did not include public pensions (this is of particular importance for our paper because the empirical evidence shown in Section 2 includes all types of social transfers). The country samples were designed to be nationally representative and were subsequently weighted to re‡ect each country’s population. Given the focus on emerging markets and developed countries, we exclude from our descriptive analysis what the IMF classi...es as “low-income developing countries.” In total, we end up with 99 countries (including those 23 countries of our developed and Latin 16