rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7: Mutinomial specification of location choice**

||Specification<br>|(1)<br>|Specification<br>|(2)<br>|
|---|---|---|---|---|
||High FDIStates|Low FDIStates|High FDIStates|Low FDIStates|
|Business age|-0.130|0.034|-0.230|-0.059|
||(0.102)|(0.104)|(0.097)*|(0.098)|
|Startup size|-0.181<br>(0.068)**|-0.357<br>(0.074)**|-0.168<br>(0.063)**|-0.324<br>(0.068)**|
|Entrepreneurial roots located|-0.793<br>(0.338)*|-2.221<br>(0.318)**|-0.738<br>(0.333)*|-2.232<br>(0.310)**|
|Food|0.396<br>(0.323)|1.070<br>(0.323)**|||
|Textiles|-0.172<br>(0.271)|0.411<br>(0.274)|||
|Garments|2.771<br>(0.527)**|2.728<br>(0.535)**|||
|Drugs, pharmaceuticals and chemicals|-0.802<br>(0.196)**|-0.802<br>(0.215)**|||
|Auto parts|-0.028<br>(0.289)|1.255<br>(0.273)**|||
|Constant|2.250<br>(0.458)**|3.202<br>(0.453)**|2.426<br>(0.435)**|3.688<br>(0.426)**|
|Observations|1405||1405||
|Log likelihood|- 1297||-1392||
|Chisquare (k)|349||159||
|PseudoRsquared|0.12||0.05||
|Standard errors in parentheses|||||

* significant at 5%; ** significant at 1%

46
However, on average, women were not successful in finding a job as the increase in labor force participation translated into increases in unemployment, with no statistically significant changes in the employment rate (Column 1 in Panels B and C). The only exception are older and skilled women who did, in fact, witness a significant increase in their employment probability. Women with high school education or more experienced an increase in their employment rate of about 0.49 percentage points for each one percentage point increase in the internet access rate. Nevertheless, given that the impact on labor force participation is higher than on employment, their unemployment rate rises. 6 The absence of significant average employment effects is in line with evidence showing limited job opportunities in the labor market of Jordan (Winkler & Gonzalez (2019)).<sup>7</sup>

# **5.1 Inspecting the mechanisms**

## **5.1.1 Changes in social norms**

In this section we investigate what drives the increase in labor force participation of women. One potential explanatory factor of the increase in female labor force participation as a result of internet adoption is the change in social norms regarding gender roles. The internet, as other communication technologies, may improve access to information contributing to change social norms directly (Arias (2018)), and to shift the bargaining power within the household.

We evaluate this mechanism using information from the JLMPS and constructing a wide array of variables capturing different dimensions of social norms including women’s decision making power, women’s access to money for home expenses, women’s saving and ownership of valuables such as jewelry and land, violent behavior of husbands against their wives, women’s being afraid of disagreeing with their husbands or other males in the households, and women’s agreement with statements about women’s empowerment.<sup>8</sup> We estimate model (1) using as

> 6We also run these models for the sample of men and the results show no statistically significant changes in job search using the internet, employment or unemployment.

> 7 We do not analyze impacts on wage levels due to the very low number of women reporting wages in the sample.

> 8The decision-making power index is the average of indicator variables where 1 means women have

11
<!-- Start of picture text --> <<br>lo 48<br>€oO<br>ao)<br>S<br>fe<br>3 wo<br>oO"<br>-1<br>iQ<br>v<br>FTM<br>lh Universities li Government Agency<br>ha Foreign Company a Business associations<br><!-- End of picture text --> <!-- Start of picture text --> N 15<br>o<br>c<br>2<br>2S<br>‘Gey<br>or<br>(S)<br>S<br>©<br>FTM<br>(HE Firms in the sector [7] Firms another sector [EEE Suppliers or buyers|<br><!-- End of picture text -->
**Table 1**

# **Key Physical Characteristics of Tanzanian Maize Markets**

|||**-------------------**<br>|**---Maize statis**<br>|**tics on ----------**<br>|**--------------**<br>|**----------- Tim**|**e and distance v**<br>|**ia major road**|**s to ------------**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**FEWS NET**<br>**Categorization**|**Production**<br>_(000m MT)_|**Area**<br>_(000 Ha)_|**Yield**<br>_(MT/Ha)_|**Share**<br>_(Percent)_|**Dar**<br>_(Hours)_|**Nairobi**<br>_(Hours)_|**Dar**<br>_(Km)_|**Nairobi**<br>_(Km)_|
|**Central**||||||||||
|_Dodoma_|Minor deficit|351|339|1.0|74|7:33|8:45|502|682|
|_Singida_|Minor deficit|190|150|1.3|53|na|na|na|na|
|_Tabora_|Minor deficit|376|292|1.3|68|12:51|11:35|886|873|
|**Coastal**||||||||||
|_Dar es Salaam_|Deficit|4|6|0.7|55|0:00|11:43|0|913|
|_Lindi_|Minor deficit|63|76|0.8|59|5:57|17:31|457|1,362|
|_Morogoro_|Minor deficit|238|232|1.0|44|2:34|11:22|194|889|
|_Mtwara_|Minor deficit|63|78|0.8|67|7:17|18:51|562|1,467|
|**Lake**||||||||||
|_Bukoba_|Deficit|121|100|1.2|71|18:29|12:30|1,415|960|
|_Musoma_|Minor deficit|117|65|1.8|69|15:25|6:44|1,149|495|
|_Mwanza_|Deficit|250|263|0.9|56|na|na|na|na|
|_Shinyanga_|Deficit|672|516|1.3|65|13:17|10:28|1,017|761|
|**Northern**||||||||||
|_Arusha_|Deficit|210|124|1.7|95|8:25|3:33|647|272|
|_Moshi_|Minor deficit|150|108|1.4|94|7:21|4:32|567|349|
|_Tanga_|Surplus|273|188|1.5|na|4:39|8:32|356|619|
|<br>**Southern Highlands**||||||||||
|_Iringa_|Surplus|384|247|1.6|91|6:29|11:41|503|946|
|_Mbeya_|Surplus|495|271|1.8|72|10:32|15:23|829|1,165|
|_Songea_|Surplus|237|149|1.6|79|11:51|19:10|924|1,374|
|_Sumbawanga _<br>**ources:**FEWS NET categ|Surplus<br>ories from USAID’|351<br>s www.fews.ne|224<br>t.Data for regi|1.6<br>onal maize pro|70<br>duction from T|18:51<br>anzania’s Agri|19:00<br>cultural Sample|1,215<br>Census 2007/0|1,466<br>8 (2012). Road|

**Sources:** FEWS NET categories from USAID’s <u>www.fews.net. Data for regional maize production from Tanzania’s Agricultural Sample Census 2007/08 (2012). Road</u> distances and time from Google Maps (estimated times assume no traffic). Road travel is less relevant for markets in the lake and coastal zones.
contribution rates of the ATP and the collective agreement with the largest coverage (OECD 2007). However, the average contribution rate in Denmark may be significantly lower (see below).

The creation of second pillars has generated considerable flows of long-term savings into pension funds (Figure 2). The level of annual contributions varies from 3.8 percent of GDP in Chile to 7.1 percent in Australia and Switzerland. The higher level in these two countries is explained by the near universal coverage of their second pillars and the fact that covered salaries represent a higher proportion of GDP than in Chile. In the case of Australia, annual contributions also include the co-contributions made by government for low income workers, while in Switzerland they also cover contributions made for superobligatory benefits.

Denmark and Sweden report lower annual contribution flows relative to GDP, despite the near universal coverage of their second pillars. In the case of Sweden this is explained by the lower contribution rate to second pillar schemes. However, including premiums paid on voluntary insurance schemes (third pillar plans), the total annual contributions amount to 6.8 percent of GDP. In the case of Denmark, the statistics imply that the average contribution rate is lower than the combined rate of 12 percent for ATP and occupational schemes indicated above. The combined rate is probably between 8 and 8.5 percent. In Denmark, workers contribute an additional 1.4 percent of GDP to personal pension plans.

**Figure 2: Annual Contributions in Second Pillars (% of GDP)**

<!-- Start of picture text --> 8<br>7<br>6<br>5<br>4<br>7.1 7.1<br>3<br>4.7 4.5<br>2 3.8<br>1<br>0<br>Australia Chile Denmark Sweden Switzerland<br><!-- End of picture text --> Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

All countries have third pillars providing additional benefits and offering provision to the self-employed and other workers that are not covered by the mandatory and quasimandatory second pillars. There are some differences in the organization of voluntary arrangements but available information does not provide a detailed picture of the structure of third pillars. Voluntary schemes benefit from tax incentives that are 9
**The Curious Case of Brazil’s Closedness to Trade**

# **1. Introduction**

As of 2012, Brazil was the country most closed to trade, by measure of trade penetration (combined share of exports and imports in GDP).<sup>1</sup> Although Brazil is a large economy by many measures and it is widely accepted that large economies tend to observe a relatively lower trade penetration, Brazil remains an outlier in this area. This paper shows how Brazil’s closedness cannot be explained by its size or other macro-level characteristics. It further shows important firm-level indicators that help explain Brazil’s closedness. Finally, the paper argues that by increasing dynamism in exporting and importing as well as integrating into global value chains, Brazil could become more productive. The rest of this paper is structured as follows: Part 2 reviews Brazil’s trade aggregates over time and compares then with peer countries. Part 3 shows, through a series of univariate and multivariate regressions, that Brazil’s relative closedness cannot be adequately explained by common macro indicators. Part 4 turns to firm-level data, especially the low propensity of Brazilian firms to export, as an explanation of low trade penetration. Part 5 looks into why few Brazilian firms export and Part 6 lays out how increasing trade penetration through deeper integration in global value chains could lead to increased productivity in the Brazilian economy. Part 7 concludes.

> 1 This is according to WDI data for 179 counties. In 2013 Sudan, newly separated from South-Sudan was recorded with an even lower trade to GDP ratio.  http://economia.estadao.com.br/noticias/geral,brasil-e-o-pais-com- <u>menor-importacao-imp-,984019</u>

2
- Hillson, R., Alejandre, J. D., Jacobsen, K. H., Ansumana, R., Bockarie, A. S., Bangura, U., Lamin, J. M., Malanoski, A. P., and Stenger, D. A. (2014). Methods for determining the uncertainty of population estimates derived from satellite imagery and limited survey data: a case study of bo city, sierra leone. _PloS one_ , 9(11):e112241.

- Horvitz, D. G. and Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. _Journal of the American statistical Association_ , 47(260):663–685.

- Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., and Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. _Science_ , 353(6301):790–794.

- Li, G. and Weng, Q. (2005). Using landsat etm+ imagery to measure population density in indianapolis, indiana, usa. _Photogrammetric Engineering & Remote Sensing_ , 71(8):947–958.

- McBride, L. and Nichols, A. (2016). Retooling poverty targeting using out-of-sample validation and machine learning. _The World Bank Economic Review_ .

- Mullainathan, S. and Spiess, J. (2017). Machine learning: an applied econometric approach. _Journal of Economic Perspectives_ , 31(2):87–106.

- Oakridge National Laboratory (2018). Documentation page of the landscan. Retrieved from https://landscan.ornl.gov/documentation on August 3rd 2018.

- Socioeconomic Data and Applications Center (2018). Gridded Population of the World (GPW). Retrieved from http://sedac.ciesin.columbia.edu/data/collection/gpwv4/methods/method1 on November 19th 2018.

- Stevens, F. R., Gaughan, A. E., Linard, C., and Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. _PloS one_ , 10(2):e0107042.

- Sutton, P., Roberts, D., Elvidge, C., and Baugh, K. (2001). Census from heaven: An estimate of the global human population using night-time satellite imagery. _International Journal of Remote Sensing_ , 22(16):3061–3076.

- Varian, H. R. (2014). Big data: New tricks for econometrics. _Journal of Economic Perspectives_ , 28(2):3–28.

- Vogel, K., Goldblatt, R., Hanson, G., and Khandelwal, A. (2018). Detecting urban markets with satellite imagery. International Growth Centre Working Paper C-89448-INC-1.

- Wardrop, N., Jochem, W., Bird, T., Chamberlain, H., Clarke, D., Kerr, D., Bengtsson, L., Juran, S., Seaman, V., and Tatem, A. (2018). Spatially disaggregated population estimates in the absence of national population and housing census data. _Proceedings of the National Academy of Sciences_ , 115(14):3529–3537.

- Wooldridge, J. M. (2002). Inverse probability weighted m-estimators for sample selection, attrition, and stratification. _Portuguese Economic Journal_ , 1(2):117–139.

- WorldPop (2018). World pop website and methods page. Retrieved from http://www.worldpop.org.uk/data/methods/ on August 4th 2018.

16
research in developing countries—accounts for a substantial share of the association between socioeconomic status and cognitive development.

Our findings are based on a single cross-section of observational data, and interpretation of the associations we report is therefore subject to some uncertainty. To the extent that the relations are causal, they suggest that a three-pronged approach may be useful for improving cognitive development among young children in poor countries. First, programs that directly raise household SES may have high returns in terms of child development. Second, improving children’s health should be given high priority, both for its own sake and because of its association with cognitive outcomes. There is, for example, strong evidence from a randomized experiment in Guatemala that protein supplementation in childhood results in long-run improvements in cognitive outcomes (Brown and Pollitt, 1996), although experimental evidence on the effects of iron supplementation (reviewed in Grantham-McGregor and Ani, 2001) and treatment for helminth infection (reviewed in Dickson et al., 2000) is more mixed. Third, programs and policies that increase the amount of cognitive stimulation children receive and improve the quality of their home environments may also be valuable. Only a small number of randomized control trials have been conducted that examine interventions in developing countries that aim at improving both health and parenting-cognitive stimulation, through homebased or center-based interventions (McKay et al., 1978; Waber et al., 1981; GranthamMcGregor et al., 1991 and 1997; Powell et al., 2004; see also Behrman et al. 2004 for an observational, non-experimental study from Bolivia). Several of these interventions have yielded promising results. For example, the recent study by Powell and colleagues, based on a sample of undernourished pre-school children from Jamaica, concludes that a home visiting intervention that provided children with cognitive stimulation yielded an increase of nearly one standard deviation in a global measure of development. In our sample, only a minority of children were read to, even though the fraction of parents who are illiterate was very small. Controlling for other characteristics, these children had significantly higher performance on the TVIP. These findings suggest there may be large returns to programs which improve parenting quality. More generally, our research underscores the need to understand how both child health and the home environment interact with socioeconomic status in determining cognitive development.

19
Table 7: Output and Conflict Related Aggregate Loss in Human Capital

||(1)<br>log GDP|(2)<br>log GDP|(3)<br>log GDP|(4)<br>log GDP|(5)<br>log GDP|(6)<br>log GDP|(7)<br>log GDP|
|---|---|---|---|---|---|---|---|
|loss|-0.053***|-0.103***|-0.108***|-0.092***|-0.090***|||
||(0.006)|(0.011)|(0.018)|(0.012)|(0.014)|||
|sq-loss|||0.001<br>(0.002)|||||
|loss using per capita battle deaths||||||-0.079***<br>(0.024)||
|battle deaths per capita||||||-0.040***<br>(0.009)||
|loss using refugees|||||||-0.034***<br>(0.004)|
|outfow of refugees|||||||-0.012**<br>(0.005)|
|Intense confict|-0.218***<br>(0.030)|-0.130***<br>(0.022)|-0.130***<br>(0.022)|-0.125***<br>(0.022)|-0.119***<br>(0.024)|||
|Any confict|-0.004|-0.030***|-0.030***|-0.027***|-0.030***|-0.033***|-0.035***|
||(0.013)|(0.009)|(0.009)|(0.009)|(0.010)|(0.009)|(0.009)|
|log population|-0.518***|0.197**|0.194**|0.193**|0.154|0.173**|0.155*|
||(0.029)|(0.081)|(0.081)|(0.084)|(0.094)|(0.084)|(0.083)|
|Country FE|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|NO|NO|YES|YES|
|Country Specifc Time Trend|NO|YES|YES|YES|YES|YES|YES|
|Subregion X Year FE|NO|NO|NO|NO|YES|NO|NO|
|Continent X Year FE|NO|NO|NO|YES|NO|NO|NO|
|Observations|6,962|6,962|6,962|6,962|6,962|6,962|6,962|
|R-squared|0.967|0.991|0.991|0.991|0.992|0.991|0.991|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. GDP data is provided by Penn World Table 7.1. Variable “loss” is the aggregate loss measure built using a dummy equal 1 if the number of battle deaths per 1000 inhabitants exceeds 0.2. We use the same threshold to define “Intense conflict” as a dummy variable. “battle deaths per capita” is the number of battle related deaths per 1000 inhabitants. “Outflow of refugees” is a dummy that takes a value of 1 if there is a net outflow of refugees in a given year. “Any conflict” is also a dummy taking 1 if there is any battle related death in a given year ( in Columns (1) to (6)) or a net outflow of refugees (in column (7)). Columns (6) and (7) use respectively the number of battle deaths per capita and outflows of refugees as measures of conflict intensity to build the corresponding loss measures (respectively “loss using per capita battle deaths” and “loss using refugees”). “Log population” is the logarithm of population size.
8

# **TIME LOSS AT THE SCHOOL LEVEL**

In all countries, the school calendar consists of class days and scheduled holidays. Inevitably, some days will be lost due to unforeseen reasons, such as weather. In higherincome countries, schools are prepared for such events; for example, school districts in the U.S. may build into the school year four days due to snow buildup and extend the school year if snow lasts longer. Aside from using substitute teachers when needed, provisions are made to keep students occupied during periods of in-service training that is conducted during school hours. In lower-income countries, however, money for substitute teachers is rarely available, and school operation may be overridden at the local level. As a result, schools may operate fewer days than expected. Reasons include long matriculation periods, in-service teacher training, climatic conditions, and infrastructure in poor condition (e.g., severely leaking roofs or damaged walls that may make some classrooms unusable). In Mali, for instance, schools were found to function 70% of the official time.<sup>23</sup> In Honduras, schools were open 114 days of the official 200 in 2001.<sup>24</sup> Surveys in Nepal suggest that schools operate on average for three hours per day, a fact that halves the teaching time available from over 1,000 hours to just 540 hours.<sup>25</sup> In Burkina Faso, a minimum of 16% of the official allocated time was lost due to breaks and exam periods.<sup>26</sup>

**Figure 2-2. Argentine students carrying textbooks from the library during class time**

_Source: Author; Iaccarini agricultural school, Mendoza, Argentina_ In poorer countries, instructional time may also be lost also due to the inefficiencies involved in large classes that may exceed 100 students. To deal with large classes, countries like Senegal, Guinea, Bangladesh, Ghana or Niger split a class into two and divide the time. Thus, a school day of five periods may be reduced to three, and the number of instructional hours provided to students is curtailed. This is known as split-shift and results in a 32-42% loss of time (Table 2-1).<sup>27</sup> Some studies have reported that this policy did not significantly impact student performance,<sup>28</sup> but this finding may be due to an already low quality of education.<sup>29</sup> Evidence from Guinea and Burkina Faso suggests that the split-shift arrangement reduces time on task, particularly in the afternoon shift, and ought to have a 23. Kim 1999 24. OED 2004 25. Watkins 2000, p. 112 26. Dia 2003 27. Split-shifting is sometimes called double-shifting, but the interpretation is erroneous. Schools may operate double and even triple shifts and offer students all the expected instructional hours. 28. Bray 2000, World Bank and IADB 2000, p. 46 29. Linden 2001
# **1 Introduction**

Women in the Middle East and North Africa (MENA) exhibit one of the lowest levels of labor force participation in the world (ILO (2017)). On average, only 20 percent of working age women in the region are either working or looking for a job, which is less than half of the levels observed in developing countries, such as those from Latin America, and Europe and Central Asia.<sup>1</sup> There is a large body of literature exploring the drivers of this phenomenon. Typical hypotheses include the role of social norms regarding gender roles, legal differences by gender, the lack of childcare options as well as other factors (see, for example, World Bank (2018 _b_ ), Bursztyn et al. (2018), Esfahani et al. (2015) and Assaad et al. (2014)). Yet, despite the substantial progress achieved in reducing gender gaps in other dimensions—such as educational attainment—the labor market outcomes of women have remained stagnant for decades in MENA.

In this article, we examine the impacts of digital technologies on labor market outcomes of women in Jordan. In particular, we estimate the impacts of increasing internet access on the probability of looking for a job as well as finding one. To our knowledge, the literature on the effects of digital technologies on female labor market outcomes is scarce and focused on countries where labor market segmentation across genders is less pronounced than in MENA (see, for example, Dettling (2017)).

Our main hypothesis is that increased internet access would have a positive effect on the labor market participation of women, especially in MENA countries, due to several factors. First, it lowers barriers to information about labor market opportunities, particularly for women who face mobility restrictions as in some of the countries in the region, where women are still legally required to ask permission to their husbands to travel outside the home (World Bank (2018 _b_ )). Second, it could provide more flexible forms of employment –such

> 1World Development Indicators, https://data.worldbank.org/indicator/SL.TLF.CACT.FE.ZS?locations=ZQ1W-ZJ-7E, accessed on November 7, 2018.

1