rafmacalaba/datause-encoder-data huggingface dataset:
exporting (the number of days to export) – controlling for economic size and level of development. The results are shown in Table 6.

**<u>Table 6: Dependent variable: Percentage change in exports 1995-2004</u>**

||_Coefficients_|_t Stat_|_Coefficients_|_t Stat_|
|---|---|---|---|---|
|Intercept|-0.118|-0.629|3.311|1.681|
|% change in number of<br>goods|1.267|4.891|1.201|4.172|
|% changein IEMP|3.165|7.505|3.128|6.234|
|Ln(GDP)|||-0.012|-0.140|
|Ln(GDPpercapita)|||-0.159|-1.086|
|Ln(Timeforexport (days))|||-0.280|-1.219|
|Ln(1+Tariff)|||-0.458|-2.024|
|Adjusted R Square|0.406||0.421||
|StandardError|1.053||1.040||
|Observations|97||97||

Both the change in number of products and the change in the index of market penetration are significantly related to export performance, with the geographic diversification variable having somewhat greater association with positive performance. Both average tariffs and time to export have the predicted negative association with poor export performance. The control variables of GDP size and level of income are not significant.

These results should be interpreted with caution. The next stage of the research will have to refine the specification of all the variables. We could improve the dependent variable specification by using annual averages rather than the difference in two levels; we could refine the discovery proxies by excluding product deaths; and we could refine the incentive variables by refining tariff measures and perhaps including restrictions on services trade; and finally we could dig deeper into the cost variables.

Are there market imperfections that might justify government intervention at the extensive market in the rapid growth phases, much as Hausmann and Rodrik posit exist in the discovery phase? Indeed it is possible to hypothesize several:

- Factor market imperfections:  Costs of capital may prevent sufficient expansion of supply to reach new export markets, and labor market regulations may prevent flexible deployment of labor.

17
# **2.1 Survey Instrument**

The limited knowledge of youth and the risks faced by this group led us to focus on the 10-24 year olds. The definition of youth varies from country to country, city to city, and maybe even from neighborhood to neighborhood. The United Nations (UN) defines youth as the group of 1524 year olds.<sup>2</sup> We used the UN’s 24 years of age as the upper limit. In Brazil, the official age of entering into the labor force is 10 years of age. Therefore, 10 years of age is set as the lower limit within this group. Moreover, many issues that influence the well being of youth may begin at this age. In Brazil, as elsewhere, the age group of 10-24 year olds is far from a homogeneous group.

The survey instrument was developed with the idea of obtaining information about the livelihood in the very poorest urban neighborhoods and subsequently to analyze issues related to youth-atrisk, social exclusion and risk of intergenerational transmission of poverty. The developed survey instrument contains a total of 61 questions, covering five major areas: i) socio-economic background; ii) education; iii) health and sexuality; iv) social capital and violence; and v) employment and economic activity. The questions were initially drawn and developed from three sources: two available surveys and personal experience in poor urban neighborhoods in Brazil, and then modified throughout the pilot experience. The majority of the questions are developed from qualitative work in poor urban neighborhoods in the Northeast Brazil. The general information questions are based on the Brazilian Living Standards Measurement Survey (LSMS).<sup>3</sup> The questions on social capital are based on a survey instrument developed by Martin Paldam<sup>4</sup> . In addition to the questionnaire, direct dialogue was established with youth, other neighborhood members, and NGOs in the poor neighborhoods, which allowed us to learn about issues that are important in the dwellers’ lives.

The socio-economic section of the survey includes questions about the respondent, the family, the home and people in the household, whether parents of the respondent are alive, and whether they are living in the household. The section on education includes questions on the level of

> 2 Source: http://www.un.org/esa/socdev/unyin/forum/ 3 See http://www.worldbank.org/lsms/ 4

> See for example: http://www.econ.au.dk/Vip_htm/mpaldam/homepage.htm

4
# Openness, Outward Orientation, Trade Liberalization and Economic Performance in Ueveloping Countries

## Sebastian Edwards

## Table of Contents

|I.|Introduction ......................................|4|
|---|---|---|
|11.|The Policy Literature on Trade Regimes:<br>A Review ....................................|6|
|Il|DefiningaTradeRegime ....................................|6|
|11.2|The Need for a Continuum ....................................|12|
|11.3|A Digression on Liberalization, Effective||
||Protection and Domestic Resource Costs .15||
|iln.|The Empirical Evidence on Trade Regimes<br>and Economic Performance in<br>Developing Countries .17||
|IV.|Recent Theoretical Developments on the<br>Relation Between Trade and Growth .34||
|V.|New Evidence on the Relation Between||
||Trade Regine and Economic Performance .39||
|V.1|Trade Orientation, the Absorption of<br>Technological Progress and Growth ............................|39|
|V.2|An Index of Trade Intervention ....................................|45|
|V.3|Trade Restrictions and Cross Country Differences<br>in Growth: Some New Evidence .................................|47|
|VI.|Concluding Remarks .................<br>...................|5 1|
||References ....................................|56|

This paper is part of a project on "Trade Regimes and Development" being carried out at the Trade Policy Division. I have benefited from comments by Bela Balassa, Vinod Thomas, Jim de Melo, Mohsin Khan, John Pencavel, Peter Montiel, John Nash, Ricardo Faini, Ramon Lopez, Gene Grossman and the participants of the CECTP Trade seminar. I am also grateful to Guiliermo Calvo and Ed Leamer for help! ul discussions, and to Miguel Savastano for very helpful assistance.

Sebastian Edwards is of the University of California, Los Angeles, and the National Bureau of Economic Research.
This is an improvement regarding previous techniques such as Machado Mata (2005) and Melly (2005), which only allowed the obtainment of grouped effects. Figure 17 shows the contribution of each of the main explanatory factors to the composition effect. In line with our previous discussion, the increase in the educational attainment of the labor force played an important role in decreasing inequality in the middle and bottom of the distribution. Moreover, the raise of the minimum wage is very important to for those at the very bottom of the distribution. In terms of changes in firm ownership type, the results suggest that an increase in the share of people employed in private organizations contributed to the reduce inequality in wages.

**Figure 17. Composition effects by explanatory factor (2002-2012)**

<!-- Start of picture text --> 0.14<br>0.12<br>0.10<br>Geographic<br>0.08<br>Education<br>0.06<br>Firm Size<br>0.04<br>Firm Ownership<br>0.02<br>Min. Wage<br>0.00<br>Demographic<br>‐0.02<br>Occupation<br>‐0.04<br>‐0.06<br>5 15 25 35 45 55 65 75 85 95<br>2012)<br>Change in log of hourly wages (2002‐<br><!-- End of picture text --> _Source:_ Own elaboration based on RLMS (Rounds XI and XXI). _Notes:_ decomposition results are based on the subsample of currently employed wage workers in the age group 18-60, reporting more than 140 worked hours in the last 30 days and with non-missing data in wages. The distribution of hourly wages was trimmed at the bottom and top 0.25 percent in each round, to remove outliers.

34
the short-run, which attenuates to zero after five years, even though their index includes women’s work itself, which increased due to the public works offer.

Another dimension of women’s agency is their physical mobility. As noted earlier, Djibouti’s restrictive gender norms limit women’s mobility. For this reason, our baseline survey asked respondents about several dimensions of movement, which we subsequently used to construct an index on mobility that was then taken into consideration in our heterogeneity analysis.<sup>14</sup>

# _Attrition_

Table 2 presents the attrition results for men and women and includes regression results (i.e., not being interviewed at midline or endline) as a function of treatment status, controlling for group and strata effects. On average, 7.5 percent of the women in the control group were not interviewed at midline. This fraction increases to 11.4 percent at endline. Given their daily work schedules and temporary absence from the household, it was difficult to interview husbands: about 28 and 22 percent of husbands were not re-interviewed at midline and endline, respectively.

Differential attrition by treatment status is a potential source of bias in program effectiveness, since the balance in observable and unobservable characteristics that ensues from the randomization of treatment status at baseline may be lost. In our study, there is some indication of differential response at midline, with participant women 3.6 percentage points more likely to complete the midline survey than control women, and 4 percentage points less likely to fully complete the weekly survey at endline. This attrition moves in two different directions, suggesting that differential attrition by treatment is not systematic. No differential attrition is observed among women at the endline household survey or in husband’s responding to any of the questionnaires. Lastly, the minor imbalances in baseline that we observe in Table 1 correspond to the imbalances in the post-attrition sample (not shown). As noted earlier, we control for baseline characteristics to partially address potential concerns about non-random attrition.

**4.3. Empirical methods** We use the following reduced-form expression to estimate the effect of being offered the public works program: i i i gg gg gg ii0 gg ss yy = αα+ ββ T + δδ XX + λλ + λλ + uu tt∈{M,E}

> 14 The five domains of mobility are: (1) Going to the grocery store, (2) Going to the market, (3) Going to the health center for consultation for self or child, (4) Visiting friends or family in the neighborhood, and (5) Visiting friends and family outside the neighborhood. For each domain, we classify women by whether they did not go anywhere in the last 12 months (coded as 0); they went out but needed permission and could not go alone (coded as 1); or they went out alone (coded as 2). We conducted a factor analysis and then normalized to 0.

15
Table 5: Heterogeneous migration response to BLAs by governance category

|||Governance catego|ries|
|---|---|---|---|
||Top half|Top two terciles|Top three quartiles|
||(1)|(2)|(3)|
|Bilateral labor agreement (BLA)|1_._281<sup>_∗∗∗_</sup>|2_._004<sup>_∗∗∗_</sup>|1_._979<sup>_∗∗∗_</sup>|
||(0_._263)|(0_._457)|(0_._577)|
|BLA _×_ Africa|_−_1_._194<sup>_∗∗∗_</sup>|_−_1_._985<sup>_∗∗∗_</sup>|_−_2_._283<sup>_∗∗∗_</sup>|
||(0_._325)|(0_._528)|(0_._668)|
|BLA _×_ Top half|_−_0_._821<sup>_∗∗∗_</sup><br>(0_._286)|||
|BLA _×_ Middle tercile||_−_0_._825<sup>_∗_</sup><br>(0_._497)||
|BLA _×_ Top tercile||_−_1_._924<sup>_∗∗∗_</sup><br>(0_._469)||
|BLA _×_ Third quartile|||_−_1_._009<br>(0_._639)|
|BLA _×_ Second quartile|||_−_0_._869<br>(0_._609)|
|BLA _×_ First quartile|||_−_1_._947<sup>_∗∗∗_</sup><br>(0_._590)|
|BLA _×_ Africa _×_ Top half|0_._983<sup>_∗∗∗_</sup><br>(0_._377)|||
|BLA _×_ Africa _×_ Middle tercile||1_._066<sup>_∗_</sup><br>(0_._579)||
|BLA _×_ Africa _×_ Top tercile||1_._624<sup>_∗∗_</sup><br>(0_._657)||
|BLA _×_ Africa _×_Third quartile|||1_._684<sup>_∗∗_</sup><br>(0_._748)|
|BLA _×_ Africa _×_ Second quartile|||1_._462<sup>_∗∗_</sup><br>(0_._714)|
|BLA _×_ Africa _×_ First quartile|||1_._869<sup>_∗∗_</sup><br>(0_._873)|
|Observations|268_,_905|268_,_905|268_,_905|
|R-sq|0_._875|0_._876|0_._875|
|Origin country x decade FE|_Y es_|_Y es_|_Y es_|
|Destination country x decade FE|_Y es_|_Y es_|_Y es_|
|Origin x destination countries FE|_Y es_|_Y es_|_Y es_|

Notes: Standard errors in parentheses are clustered at the origin-destination level. BLA (dummy) in decade N takes the value 1 if a BLA was signed in the last 10 years and zero otherwise. The dependent variable is the inverse hyperbolic ssine of the number of dyadic migrations in protocols. For each of the highlighted categories, we consider consider the specification with the most stringent set of fixed effects.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

In summary, our results show that Bilateral Labor Agreements (BLAs) significantly increase migration flows worldwide. This positive effect is particularly pronounced for destinations within the Gulf Cooperation Council (GCC) countries. While LLMIC origin countries experience the most significant positive impact of BLAs on migration flows, African countries do not seem to be significantly affected, leading to what we refer to as the “ _African puzzle_ ”. Further investigation 19
Lagos, & Deming, 2020; Bergman, 2016; Dizon-Ross, 2019).<sup>1</sup> The information intervention studied in this paper has been implemented at scale by the national government. This is an important feature given that studies from efficacy field trials do not always yield similar results when implemented at scale (Al-Ubaydli, List, & Suskind, 2020; Banerjee et al., 2017). The scaling up of the information intervention was made possible by delivering information to parents through a group that exists in all schools: parent associations. Groups have been demonstrated to be efficient platforms for information delivery in other settings, such as women’s groups in developing countries (Dìaz-Martin, Gopalan, Guarnieri, & Jayachandran, 2020). Parent associations are also useful because they create opportunity for social interaction among members, resulting in positive externalities (Small & Gose, 2020). As the information intervention in the first experiment included time for group discussion, we view the social interaction in parent associations as an important feature of the intervention.

Second, the double grant experiment is closely related to a broader set of school-based management (SBM) reforms. Since the 1990s, SBM has been a popular policy strategy to improve the quality of education in developing countries. By decentralizing decision-making authority of school operations and management from the federal government to local stakeholders, SBM allows for schools to directly respond to local needs and priorities. Despite the widespread adoption of SBM, the effectiveness of these programs in raising educational outcomes have been mixed. Experiments in India (Banerjee et al., 2010), the Gambia (Blimpo, Evans, & Lahire, 2011), and Niger (Beasley & Huillery, 2017) show no improvement on student learning outcomes. In contrast, experimental evidence from Indonesia (Pradhan et al., 2014) and Kenya (Duflo, Dupas, & Kremer, 2015) finds significant, positive effects on student test scores. The effective intervention in Indonesia provided information to parents about ways to become involved in their children’s school along with “linkage” meetings between parent committees and the village council (Pradhan et al., 2014). In Kenya, Duflo et al. (2015) provided parents with information about how to evaluate contract teachers and involved parents in the formal review process of contract renewals for teachers.

In particular, our study builds upon the numerous evaluations of Mexico’s longstanding schoolbased management program, which consists of a package of education reforms including infras-

> 1The information intervention studied in this paper is similar to a parental outreach program in France, which gave parents information about the functioning of schools and advice on how to support children with school work (Avvisati et al., 2014). The French program increased parental involvement and improved student behaviors, but did not raise student achievement.

4
**<u>Table 2: Community and household variables in estimation of consumption</u>**

||(1)|(2)||(1)|(2)|
|---|---|---|---|---|---|
|**Variables Description**|**Type**|**Unit**|**Variables Description**|**Type**|**Unit**|
|**Community Level**<br>**Variables:**|||_Number of children_in the age<br>groups of|||
|_Biomass_|C|m3/Ha|0-4|C|Number|
|_Biomass Squared_|C|[m3/Ha]<sup>2</sup>|5-10|C|Number|
|_Regular bus service in the_|D|0, 1|11-14|C|Number|
|_community_|||_Highest level of education in_|||
|_Health clinic in community_|D|0,1|_the household:_|||
||||_No education_|(Default)||
|_EA is a Boma or a trading_|D|0, 1|_Someprimary_|D|0,1|
|_center_|||_Completedprimary_|D|0,1|
|_Travel time to the nearest_|||_Postprimary_|D|0,1|
|_Boma_:|||_The_ _chronic illness in the_|D|0, 1|
|_Less than 20 minutes_|(Default)||_household_|||
|_20 to 30 minutes_|D|0,1|_Religion:_|||
|_30 to 45 minutes_|D|0,1|_None/ Traditional_|(Default)||
|_45 to 60 minutes_|D|0,1|_Islam_|D|0,1|
|_More than 60 minutes_|D|0,1|_Catholic_|D|0,1|
|The_ADMARK market in_<br>|D|0, 1|_CCAP_|D|0,1|
|_the community_|||_Other Christian religion_|D|0,1|
|_Bank in the community_|D|0,1|_Household has wage or salary_|D|0, 1|
|_Daily market in the_|D|0, 1|_income_|||
|_community_|||_Household has non-farm_|D|0, 1|
|_Tarmac/asphalt road in_|D|0, 1|_enterprise_|||
|_the community_|||_Eight Agricultural_|||
|**Household Level**|||_Development Districts(ADD): _|||
|**Variables:**|||_Karonga_|D|0,1|
|_Female-headed_|D|0, 1|_Mzuzu_|D|0,1|
|_households_|||_Kasungu_|D|0,1|
|_Head of the household is a_|D|0, 1|_Salima_|D|0,1|
|_widow or a widower_<br>|||_Lilongwe_|D|0,1|
|_Age of the head of the_<br>|||_Machinga_|D|0,1|
|_household_|||_Blanyre_|D|0,1|
|_Less than 26_|(Default)||_Ngabu_|_(Default)_||
|_26 to 35_|D|0,1|_The land holdings of a_|||
|_36 to 45_|D|0,1|<br>_household:_|||
|_46 to 55_|D|0,1|_Area under rain-fed_|C|Ha|
|_56 to 65_|D|0,1|<br>_Cultivation_|||
|_66 and above_|D|0,1|_Area under dimba_|C|Ha|
|_Household size_|C|Number<br>|_Cultivation_|||
|_Household size squared_|C|[Number<sup>2</sup>]<br>/100|_Household cultivated tobacco_|D|0,1|

Type: C continuous variable. D: Indicator variable, takes value 1 if a condition is satisfied and 0 otherwise.

37
<!-- Start of picture text --> Figure 7a: Number of firms that signed up for the Figure 7b: Number of firms that signed up for the<br>small firm tax regime each quarter micro firm tax regime each quarter<br>2011q1 2011q3 2012q1 2012q3 2013q1 2011q1 2011q3 2012q1 2012q3 2013q1<br>Year and quarter Year and quarter<br>8000 3000<br>6000<br>2000<br>4000<br>Number of firms Number of firms<br>1000<br>2000<br>0 0<br><!-- End of picture text --> The low take-up of the new regimes in Georgia is not uncommon and likely linked to a number of related factors. First, to limit the room for abuse and strategic behavior by firms, the Georgian Authorities restricted the regimes to individual entrepreneurs and, in the case of the micro regime, also prevented businesses with employees from participating. In addition, special regimes tend to be unattractive for businesses with major supplies to (or from) VAT registered entities, as presumptive taxpayers cannot voluntarily register for the VAT. Finally, though the micro regime is highly advantageous in exempting businesses from income tax obligations, uncertainty regarding the long term stability of this policy choice may keep businesses from registering.<sup>24</sup>

# **4. Estimation Approach**

In order to measure the effects of the tax reform on formal firm creation and tax compliance in a rigorous way, we need an adequate comparison group for firms affected by the reform. Given that eligibility for the reform was based on thresholds, we use a regression discontinuity design (RDD) based on the GEL 30,000 and GEL 100,000 cutoffs in annual turnover to estimate the effects of the reform. The RDD approach takes advantage of the fact that only firms with annual turnover below GEL 30,000 (GEL 100,000) are eligible to file under the new micro (small) firm tax regime. If we compare firms just below each cutoff to firms just above the cutoff, as the RDD does econometrically, we have two groups that should be very similar to each other. However, the patterns of bunching around the GEL 100,000 threshold shown in Section 3 that existed even before the reform can invalidate the RDD. The following paragraphs first explain the simple RDD approach and the assumptions needed for this methodology to be valid and then discuss how our analysis can adjust the design to account for strategic under-reporting around the GEL 100,000 threshold, while leaving the design intact to study the group around the GEL 30,000 threshold where the distribution is smooth.

> 24 In a number of Eastern European countries, the lack of predictability of MSME tax policy is a major concern for the business community (Engelschalk and Loeprick 2014).

15
41 Figure 3: The Estimated Total Effect o' Tariff and Nontariff Protection for Textiles and Clothing in the United States <!-- Start of picture text --> W&ovn oMUoees t_t*:I<br>MB awean_I I._<br>MBITCWUI&trs I - __-:_:_-_--<br>he Njt. type Co w .. _I ... _.<br>Kn ft & bpwum _ __<br>WCmh xltn k3P_I mjt_ _ . _.<br>Cotton tweah I I<br>MB Se coa_~~I _ ............. . .. ..... - ..............<br>kV go-type c I w<br>j MB s*Ch-,p<br>M p o il _- _ w _ - .- _ _ _ _ _ _ _ _ _ _ _ _ _<br>YVGI I I .:<br>_ __ -~~~~~~~,,-~ ~ .. ~.., -' .<br>oo swate MB i -<br>hLnot krWI MMP f~~~~lcs__L..r~~~~..riw..,.. J ..<br>MMF n j -k-ghtwsa .- . Tariff equivalent of NTBs minuis<br>_ . 0 Tariff rate<br>WGI wool swe I _twm<br>we roerI . -...<br>londk&estAfsm<br>0 20 40 60 ~~~~80 100 120<br>Not_: M.me1O nvnrne f__.. b:WGI rmeans wmensgis.n iIfwrtl., MB mea meris aid boy.<br>S"O..c: USLTC 1989<br><!-- End of picture text -->