rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Fehr, Ernst and Simon Gächter (2000), Fairness and Retaliation: The Economics of Reciprocity,</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>, 14(3), 159-181.</mark> <mark>Ferrando, Mery, Alina Machado, Ivone Perazzo, and Adriana Vernengo. 2011. “Una Primera Evaluación de los Efectos del Plan CEIBAL en Base a Datos de Panel.” Montevideo, Uruguay: Instituto de Economía de la FCEydeA.</mark> Filmer, Deon, Amer Hasan, and Lant Pritchett. 2006. “A Millennium Learning Goal: Measuring Real Progress in Education.” Working Paper 97. Washington, DC: Center for Global Development.

Fredriksen, Birger, Sukhdeep Brar, and Michael Trucano. 2015. _Getting Textbooks to Every Child in Sub-Saharan Africa: Strategies for Addressing the High Cost and Low Availability Problem. Directions in Development--Human Development._ Washington, DC: World Bank. <u>https://openknowledge.worldbank.org/handle/10986/21876</u> <mark>Fredriksen, Birger and Sukhdeep Brar. 2015.</mark> _<mark>Getting textbooks to every child in Sub-Saharan Africa: strategies for addressing the high cost and low availability problem</mark>_ <mark>. Washington, DC: World Bank.</mark> <u><mark>https://doi.org/10.1596/978-1-4648-0540-0</mark></u> Fuchs, Thomas and Ludger Woessmann. 2004. “Computers and Student Learning: Bivariate and Multivariate Evidence on the Availability and Use of Computers at Home and at School.” CESifo Working Paper Series No. 1321. CESifo Group Munich.

<mark>Glewwe, Paul W., Eric A. Hanushek, Sarah D. Humpage, and Renato Ravina. 2011. “School Resources and Educational Outcomes in Developing Countries: A Review of the Literature from 1990 to 2010.” NBER Working Paper No. 17554. National Bureau of Economic Research.</mark> <u><mark>https://doi.org/10.3386/w17554</mark></u> Glewwe, Paul W., Michael Kremer, and Sylvie Moulin. 2009. "Many Children Left Behind? Textbooks and Test Scores in Kenya." _American Economic Journal: Applied Economics_ 1(1): 112-35. <u>http://www.aeaweb.org/articles.php?doi=10.1257/app.1.1.112</u> Gneezy, U. and List, J. A. 2006. “Putting Behavioral Economics to Work: Testing for Gift Exchange in Labor Markets Using Field Experiments”. _Econometrica_ , 74: 1365-1384. doi:10.1111/j.14680262.2006.00707.x Hung, Woei. 2011. “Theory to Reality: A Few Issues in Implementing Problem-based Learning.” - _Educational Technology Research and Development 59_ (4): 529–552. http://dx.doi.org/10.1007/s11423 <u>011-9198-1</u> Kremer, Michael and Alaka Holla. 2009. “Improving Education in the Developing World: What Have We Learned from Randomized Evaluations? _Annual Review of Economics_ 1: 513-542. <u>https://doi.org/10.1146/annurev.economics.050708.143323</u> Lai, Fang, Renfu Luo, Linxiu Zhang, Xinzhe Huang, and Scott Rozelle. 2015. “Does Computer-assisted Learning Improve Learning Outcomes? Evidence from a Randomized Experiment in Migrant Schools in Beijing.” _Economics of Education Review_ 47: 34-48. <u>https://doi.org/10.1016/j.econedurev.2015.03.005</u> 15
Landscan has two key advantages and two key disadvantages over other data sets. First, Oak Ridge National Lab is more explicit in the fact that they are trying to estimate the ambient population with potentially nocturnal and diurnal populations; while in other algorithms this is implicit through the general smearing of the population into built cover, without workplace or residence distinction. The second advantage of Landscan is that Oak Ridge National Lab has access to information which would improve precision over the use of Landsat to just assign people to built cover. Oak Ridge has access to very high-resolution satellite data (under 10-40cm) which _potentially_ allows them to distinguish building types based on what building shapes are likely to house employment versus residents (versus commercial activities like shopping), as well as _potentially_ to distinguish roads for commuting and even infer building heights with digital elevation modeling [DEM]. A key disadvantage in using Landscan is the complete lack of specificity and transparency as to what Oak Ridge researchers actually do; hence, our use of the word ”potentially” in describing what they might do. The second disadvantage, which they acknowledge, is that Landscan data for different time periods are not comparable over time, presumably both because of differential availability of high-resolution data over time and increasingly sophisticated extraction of information from later satellite images.

Hopefully in the future, proposed data sets such as the High Resolution Human Settlement Layer (HRSL)<sup>3</sup> or Modelling and Forecasting African Urban Population Patterns (MAUPP)<sup>4</sup> – which also use very high spatial resolution data – will be able to cover a wider set of time periods and countries consistently with a more explicit methodology. Then one will be able to compare them with Landscan and do more comprehensive ground-truthing exercises. Second while ambient population may be a relevant measure to use in characterizing economic density, one might prefer to know about clustering and density of employment. If the assignment of people to workplace buildings gets very sophisticated, we would be able to explore the use of employment density measures, as well as ambient population ones.

> 3https://www.ciesin.columbia.edu/data/hrsl

> 4http://spell.ulb.be/project/maupp

5
WPS4065

# Beyond Trade

The Impact of Preferential Trade Agreements on Foreign Direct Investment Inflows

## Denis Medvedev*

## Abstract

This paper investigates the effects of preferential trade agreements (PTAs) on the net foreign direct investment (FDI) inflows of member countries using a comprehensive database of PTAs in a panel setting. We find that PTA membership is associated with a positive change in net FDI inflows, and the FDI gains are increasing in the market size of the PTA partners and their proximity to the host country. We identify several different channels through which preferential trade liberalization may affect FDI, and confirm that both threshold effects (signing the agreement) and market size effects (joining a larger and faster-growing common market) are important determinants of net FDI inflows, although the latter seem to dominate. The estimated relationship is largely driven by North-South PTAs, and is most pronounced in the late 1990s and early 2000s, which is a period when the majority of “deep integration” PTAs have been advanced.

JEL classification: F15, F21, F53.

World Bank Policy Research Working Paper 4065, November 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ _*_ Development Prospects Group, World Bank and Department of Economics, American University. I am deeply grateful to Robert Blecker, Jeffrey Lewis, Kara Reynolds, and Hans Timmer for valuable comments and suggestions.
# **Appendix Table A4: Effect of the information intervention on receiving SEBRAE training**

||R|eceived SEBRA|E training with|in|
|---|---|---|---|---|
||3 months<br>(1)|6 months<br>(2)|12 months<br>(3)|18 months<br>(4)|
|_Panel A: Pooled treatment effect_|||||
|Any information sheet|0.019|0.018|0.002|-0.002|
||(0.018)|(0.018)|(0.023)|(0.034)|
|R2|0.235|0.279|0.220|0.150|
|Control mean|0.042|0.048|0.083|0.185|
|_Panel B: Effects for each treatment group_|||||
|(1) No motiv, no pre-enroll|0.025|0.018|0.020|0.027|
||(0.023)|(0.023)|(0.030)|(0.044)|
|(2) No motiv, pre-enroll|0.013|0.009|-0.029|-0.043|
||(0.023)|(0.022)|(0.026)|(0.041)|
|(3) Motiv, no pre-enroll|0.017|0.024|0.010|-0.003|
||(0.022)|(0.024)|(0.029)|(0.042)|
|(4) Motiv, pre-enroll|0.020|0.019|0.005|0.012|
||(0.022)|(0.022)|(0.028)|(0.041)|
|R2|0.236|0.279|0.224|0.153|
|F-test p-value (2=1)|0.606|0.692|0.078|0.096|
|F-test p-value (3=1)|0.732|0.825|0.745|0.492|
|F-test p-value (4=1)|0.814|0.959|0.599|0.721|
|F-test p-value (1=2=3=4)|0.964|0.933|0.249|0.345|
|<br>_Panel C: Heterogenous treatment effects by_|_baseline busin_|_ess practices (BP_|_s)_||
|Any information sheet*Low BPs|0.056***††|0.060***††|0.040|0.025|
||(0.017)|(0.018)|(0.027)|(0.046)|
|Any information sheet*High BPs|-0.025|-0.032|-0.035|-0.028|
||(0.032)|(0.032)|(0.038)|(0.052)|
|R2|0.243|0.289|0.231|0.158|
|F-test p-value (Low BPs=High BPs)|0.024|0.011|0.115|0.454|
|Control mean low BPs|0.000|0.000|0.035|0.128|
|Control mean high BPs|0.089|0.101|0.139|0.253|
|N|866|866|866|866|

Notes: This table shows OLS estimates for the effect of receiving the information sheet on receiving SEBRAE training, including classes, consulting, and networking events. The outcome variables are dummy variables equal to one if the firm received SEBRAE training within the stated number of months after the intervention and zero otherwise. Panel A pools all treatment groups. Panel B shows separate treatment effects for each of the four different versions of the information sheet. Panel C shows heterogenous treatment effects for firms using below and above the median percentage of baseline business practices. BP stands for business practices. Low BP refers to below the median (50%), while high BP refers to above the median. All regressions control for the fraction of firms that received SEBRAE-PR training within 12 months before the intervention and strata fixed effects. The regressions in Panel C additionally control for a dummy variable equal to one if the firm had low BPs at baseline. ***, **, and * indicate significance at the 1, 5, and 10 percent level. To deal with multiple hypothesis testing, †††, ††, and † indicate sharpened q- values statistically significant at the 1, 5, and 10 percent level.

25
Figure 1: Expectation of Losing Job and Income <!-- Start of picture text --> 54% 54% 52%<br>43%<br>40% 40%<br>31% 29% 32%<br>24%<br>Cismen Ciswomen Transgender Non-Binary All<br>Expect to lose job or be unemployed (conditional on had a job on the day of<br>the survey interview)<br>Expect income to reduce at least 30 percent<br><!-- End of picture text --> N=4454 for expect to lose job; and N=7427 for expect income to reduce at least 30 percent.

The disproportionate impact faced by transgender and non-binary people in relation to jobs is also reflected in their expectation of reduced income. More than half, 54 percent of transgender people and 52 percent of non-binary people expect their income to decline by at least 30 percent. However, compared to transgender and non-binary people, a much lower 40 percent of cismen and 29 percent of ciswomen expect the same (Figure 1). The marginal effects of the probit model estimation in Table 3 shows that non-binary people are 7 percentage points more likely to expect an income reduction of at least 30 percent due to the pandemic than cismen.

Nonetheless, one may reason that the expectation of economic loss does not necessarily reflect the actual loss of job and income. Using the COVID-19 Disparity Survey, it is not possible to infer the realized loss of jobs and income for transgender and non-binary people compared to cisgender people. In the absence of global evidence on the impact of COVID-19 on job loss of LGBTI people, we are unable to compare our results widely. The findings in this paper, however, are consistent with the existing empirical evidence on the US economy, suggesting that LGBTI people and their families experienced a higher rate of job and wage loss during the pandemic than non-LGBTI people (Movement Advancement Project 2020). In the US, LGBTI people were more likely to be laid off from their jobs and furloughed than non-LGBTI people (Sears, Conron, and Flores 2021).

12
line in Paraíba, taking into account other characteristics. The analysis reveals (a) a conditional correlation between poverty and characteristics of household heads and (b) information about groups that are particularly vulnerable in 1999. The probability of a household being poor is analyzed based on relevant individual and household characteristics.<sup>12</sup> One of the salient findings is that poverty can be attributed to the lack of income-generating assets, particularly human capital.

Other poverty studies for Brazil as a whole, such as Ferreira, Lanjouw, and Neri (1998), show that in 1996 education was the central personal attribute determining the likelihood that a household would experience poverty. Other factors such as age, family size, race, and rural living are also important in determining the likelihood of poverty. The findings on Paraíba presented in this section are very much in line with those of Ferreira, Lanjouw, and Neri. A discussion of some of the variables explaining poverty follows below.

This section analyzes the impact of the different levels of education on the likelihood of being poor for Paraíba and the Northeast region. The status of the household—poor or non-poor—is regressed on relevant individual and household characteristics using the probit regression technique. To minimize the likelihood of findings being affected by small sample biases, data for the whole Northeast region is used in the analysis. Given the way the regression model is specified, findings reveal when impacts for Paraíba are different from the region as a whole. Findings presented in Table 4.1, show that Paraíba is not statistically very different from the Northeast as a whole. In the following, it will explicitly be mentioned when differences are encountered.

_Education._ All levels of education from primary to tertiary are strongly significant and negatively associated with the probability of being poor (Table 4.1). That is, the more education attained the less likely it is that the person will fall below the poverty line. The impact of having completed grade 1-4 of primary education on the likelihood of being poor is the lowest. For completed high school education, the estimated impact is much larger (4 times) than that of primary education. Furthermore, completed tertiary education reduces poverty more than completed secondary education. The likelihood of falling below the poverty line is 6 times lower when having completed tertiary school than having completed grade 1-4 of primary education.

12 For a detailed analysis of the probit analyses for Northeast Brazil, including Paraíba, see Fiess and Verner (2001).

33
by firm ownership type within each 8-digit HS level. The UN BEC code is used to separate intermediates from final goods in imports at the 6 digit-HS level, which are then aggregated up to 42 product categories in the Chinese IO table. These data are used as import-related constraints and to set initial values for our minimization program.

All initial values 0�� and D0�� in the model, as well as an industry’s total intermediate inputs were set based on official statistics. These values constrain the model solutions to a convex set. To initialize all _z0ij_ ’s, we need to allocate each industry’s total intermediate inputs, both domestic and imported, into different product groups by firm type. To this end, we first use the NBS firm census and the original IO table to compute for each firm type (6 of them), the sectoral (42 sectors) output 0�� and value added D0��. Then we compute total intermediate inputs ( 0�� −D0��) for each sector and firm type, and compute the share of intermediate inputs of each firm type in sector _j_ . Using these shares, we distribute c � ��,�� the numbers �0�� and �0�� from the original IO table into 6 different firm types, e.g., �0�� . Table A5-6 in the appendix shows these shares by firm type in all 42 sectors. The specific procedures to set the initial values for our minimization program are described below.

- �,�

- 1. Setting the initial value for �0�� (the IO coefficients for imports for group g) involves two steps. For sectors that have zero intermediate imports in the trade statistics, but have positive values in the IO table, we simply use the shares of each firm type in the sector’s total intermediate inputs and set �,�

- the initial value for �0 as: ��

On the other hand, for sectors that have positive imported intermediate inputs in the trade statistics, we first compute each firm group’s share in the sector’s imported inputs based on customs statistics, as shown in Table A6.2, to allocate imported inputs into SOEs, FIEs, and others. Using the adjusted �0��� and eq. (23), we further allocate the imported inputs belonging to each ownership type to large and small firms within the same ownership type, respectively.

2. To set the initial value for �0����,�� (the volume of domestic intermediates supplied by group g1 in sector _i_ to group g2 in sector _j_ ), we first assume that the share of intermediate inputs produced by g1 in sector _i_ equals the share of g1’s gross output in sector _i_ . Then on the receiving side, we assume that g2’s share of intermediate input absorption in sector _j_ equals their share of intermediate inputs in total intermediate inputs demanded by the same sector. All this information is available in the firm census 19
age. A second correction is applied only to women with children, as suggested in Munich et al. (2005) and Pastore and Verashchagina (2011). A year per child is subtracted from the mother’s age to calculate work experience. In a few cases, the implied total number of years of potential work experience turned out to be negative; we set these equal to zero.

The other variables used in the estimates are self-explanatory.

Any data source has both advantages and disadvantages. The advantages of the SWT survey are many and they include the following: the availability of a relatively large number of observations for young people rarely available in labor surveys; the availability of information on household characteristics which can be used as instruments in two-step procedures able to control for endogeneity and sample selection bias; very detailed information on demographic and individual characteristics, including aspects of young people’s labor market attitudes, such as aspirations and life goals, job satisfaction, job search methods and the like. Nevertheless, some other important variables, typically used as controls in Mincerian earnings equations, are missing in the SWT survey, thus constraining our ability to explore wage differences more fully. This is the case, for instance with respect to the following variables: the ownership of firms; the sector of employment; the location of respondents, and, except for the distinction between rural and urban residential areas, information on whether they moved to their current place of residence or not.

# **3. Results**

## **3.1. Descriptive Statistics**

### **3.1.1. Demographic factors**

Figure 2 reports the age distribution of first marriage by gender for the entire sample and for employed and non-employed women respectively. The average age at first marriage is different for men and women: 20.3 years for women and 22.6 years for men. It is also different from the official statistics reported in the introduction simply because the data set we use includes only the youngest age segment. Early marriages, namely marriages under the age of 19 years, are much more common among women than men in our sample: in fact, 41.2% of the women who marry do so early, while the comparable figure for men is about a fourth (10.3%). In the case of women, this is a very high share by international standards and several international organizations, including UNICEF point out that this is an important driver of gender differences in the labor market as well as of child health and well-being.

6
MULTILINGUAL CHILD ASSESSMENT

# **References**

Alcock, K. J. (2017). Production is only half the story—First words in two East African languages. _Frontiers in Psychology_ , _8_ , 1898.

- Alcock, K. J., & Alibhai, N. (2013). Language development in sub-Saharan Africa. In M. J. Boivin & B. Giordani (Eds.), _Neuropsychology of Children in Africa_ (pp. 155–180). Chapter 8] New York: Springer New York LLC.

- Alcock, K. J., Holding, P. A., Mung’ala-Odera, V., & Newton, C. (2008). Constructing tests of cognitive abilities for schooled and unschooled children. _Journal of Cross-Cultural Psychology_ , _39_ (5), 529–551.

Altan, A., & Hoff, E. (2018). Complex structures in the child-directed speech of native and

- nonnative speakers. In D. Buğa & M. C. Ögeyik (Eds.), _Psycholinguistics and Cognition in Language Processing_ (pp. 127–139). Hershey, PA: IGI Global.

Armon-Lotem, S., de Jong, J., & Meir, N. (Eds.). (2015). _Assessing multilingual children._

- _disentangling bilingualism from language impairment_ . Bristol, England: Multilingual Matters.

- Baird, S., Hicks, J. H., Kremer, M., & Miguel, E. (2016). Worms at work: Long-run impacts of a child health investment. _Quarterly Journal of Economics_ , _131_ , 1637–1680.

- Ball, J. (2011). _Enhancing learning of children from diverse language backgrounds: Mother tongue based bilingual or multilingual education in the early years_ . Paris, France: United Nations Educational, Scientific and Cultural Organization [UNESCO].

<mark>Barac, R., Bialystok, E., Castro, D. C., & Sanchez, M. (2014). The cognitive development of</mark>

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21
18 Table 4: Median Pass-through Coefficients, January 2009–January 2013

|**Category**|**Gasoline**|**Diesel**|**No. of**<br>**countries**|**Kerosene**|**No. of**<br>**countries**|**LPG**|**No. of**<br>**countries**|
|---|---|---|---|---|---|---|---|
|Income||||||||
|Low|57|57|15|54|9|95|6|
|Lower middle|66|79|24|51|17|30|22|
|Upper middle|116|116|25|108|14|86|20|
|High|157|127|8|a|1|a|2|
|Oil trade||||||||
|Net importers|105|99|53|83|31|74|35|
|Low income|63|60|14|58|8|95|6|
|Lower-middle income|87|96|17|84|12|49|15|
|Upper-middle income|126|121|15|109|10|94|12|
|High income|169|130|7|a|1|a|2|
|Net exporters|57|42|19|17|10|21|15|
|Region (excluding high-income<br>countries)||||||||
|East Asia & Pacific|99|99|9|59|4|57|8|
|Europe & Central Asia|111|145|4|a|1|a|1|
|Latin America & Caribbean|117|113|17|111|10|117|16|
|Middle East & North Africa|22|4|8|22|4|3|7|
|South Asia|65|72|5|88|5|84|5|
|Sub-Saharan Africa|57|49|21|42|16|31|11|

_Sources_ : Author’s calculations using data from sources cited in Table A1.2 in Kojima (2012). _Note_ : Median coefficients are followed by the number of countries in each category. Gasoline and diesel have the same sample countries. Three figures are retained for coefficients exceeding 99% for formatting reasons only and are not intended to signify the number of significant figures. a. Too few countries in the sample.

The foregoing results suggest that domestic prices have been lagging the price movements on the world market for more than half of the countries studied. The next section looks at the pricing policies that have led to this outcome.

# **5. Pricing Policy for Oil Products**

The wide range of prices in section 4 mirrors the diversity of approaches to government pricing policy for oil products. Each has its own history and reflects the state of oil supply and consumption in the country, as well as the political economy of the downstream oil sector and the state of social service delivery by the government. This section reviews different pricing policies followed by governments in developing countries. Unless indicated otherwise, the materials are drawn from Kojima (2013), which contains case studies of 65 countries.

# **Range of pricing policies**

Various options have been adopted by governments to help reduce price volatility, keep prices low for one or more user categories, or do both. Measures include setting prices or price ceilings, providing targeted or universal price subsidies, setting up a fund for smoothing prices across time or freight cost equalization across the country for pan-territorial pricing, reducing