rafmacalaba/datause-encoder-data huggingface dataset:
# Other data

Data on human capital are based on Barro and Lee (2001). Data on GDP per capita and population data are taken from the PWT and from the World Development Indicators. Data on legal origins are taken from La Porta et al. (1999).

# 2.2.3 Regression results

Tables 1, 2, 3, 4 present our main general results from estimating equation 1 and using the Freedom House PR and CL indicators, the Polity2 measure from the Polity IV data set, and the Economic Freedom Indicator (EFW). We start by considering as variables of interest the lagged dependent, the total emigration rate, the share of tertiary educated workers over the total resident labor force, and the log of population size.

Column 1 of each table shows the pooled OLS relationship between the total emigration rate and democracy by estimating equation 1. The results show a positive correlation between openness to migration and democracy, statistically signi...cant, however, only when considering the Polity2 and EFW indexes (all standard errors are robust and clustered by country group). In column 2, when we control for ...xed e¤ect, the coe¢ cient related to the total emigration rate becomes negative (except for EFW), and statistically not signi...cant. We know that in a dynamic panel data model, the standard ...xed e¤ect estimator is biased and inconsistent in panels with a short time dimension (the so called Nickell bias (Nickell, 1981)). Moreover, both in our ...xed e¤ect and pooled OLS estimations, explanatory variables are considered as exogenous. To deal with these problems we use the system GMM estimator that is consistent in dynamic panel estimations and rely on "internal instruments" to control for a weak form of exogeneity of all explanatory variables. We consider the explanatory variables of interest as predetermined, i.e. instrumented using their own one-period and further lags, in order to use a relevant number of instruments for e¢ ciency reasons and at the same time keeping the number of instruments lower than or equal to the number of country groups in all speci...cations.<sup>18</sup> In column (3) example, Surinamese emigrants mainly live in the Netherlands, with just 3 percent of Surinamese emigrants living in the six receiving countries in Defoort’s sample. We will therefore conduct a sensitivity analysis to check the robustness of the results to the exclusion of low-coverage countries in the Defoort dataset.

> 18A problem of the GMM estimator is that too many instruments can over ...t the endogenous variable. As rule of thumb, the number of instruments should be less or at least equal to the number

12
thus reduce the magnitude of those fixed costs and stimulate the exports of smaller firms relatively more than in other sectors.

Firm exports of products with heavier regulatory requirements in terms of SPS and TBT are likely to respond differently than those of products with lighter regulatory requirements. To address this possibility, we consider the specific trade concerns raised by WTO members in the WTO Committees on SPS and on TBT measures, which are viewed as capturing de facto trade barriers (Fontagné et al., 2015).<sup>34</sup> We aggregate our firm-destination-HS 6-digit-year level data to the firm-destination-industry-year level in order to estimate Equation (2) separately for industries subject to at least one specific trade concern versus industries subject to no such concerns. Both panels in Table 6 shows that the effects of SPS and TBT provisions on smaller firms’ exports are entirely driven by industries subject to at least one specific trade concern. This constitutes useful evidence that the mechanism that our results identify is an integration and liberalization of SPS and TBT regulations across partner countries via the inclusion of related provisions in PTAs.

# **5.2 Impacts on Firm Export Market Entry**

Since SPS and TBT requirements may act as a fixed entry cost, regulating SPS and TBT by their inclusion in PTAs would decrease such cost and a boost to firm export market entry could be expected, especially the entry of smaller firms. Due to the fill-in of our database to account for zeros in trade, our baseline results capture a combination of impacts at the intensive and extensive margins. But we explicitly examine firm export market entry defining it as a dummy variable equal to one if a firm-destination-HS 6-digit triplet export a positive trade flow in year _t_ , while exporting a zero trade flow in _t_ -1. We estimate Equation (2) using firm entry as the dependent variable and provide the results in Table 7. The estimates show that including SPS provisions in PTAs significantly increases the entry of smaller firms into new product-markets. PTAs with mutual recognition or harmonization provisions for

> 34We consider all specific trade concerns raised between 1995 and 2010 by all WTO member countries separately in the SPS or TBT committee.

21
Table S.21: Impacts on children’s educational achievement - Bengali: complementarity <u>(OLS)</u>

|Dependent variable:|||Bengali: GPA|z-scores|||
|---|---|---|---|---|---|---|
|_X_:|No. of||School|-level ratio|of||
||students<br>in each|no. of<br>teachers|no. of<br>blackboards|no. of<br>benches|no. of<br>fans|no. of<br>toilets|
||school-|to|to|to|to|to|
||grade|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|
||(a)|(b)|(c)|(d)|(e)|(f)|
|**December in 2013**|||||||
|All products|-0.019|-0.609**|-0.366|-0.406|-0.077|-0.087|
||(0.198)|(0.248)|(0.383)|(0.299)|(0.089)|(0.105)|
|One product|0.472**|-0.314|-0.069|0.430|0.046|-0.098|
||(0.175)|(0.323)|(0.294)|(0.268)|(0.082)|(0.126)|
|All products _× X_|-0.001|23.416**|16.736|0.819|7.229*|10.307|
||(0.003)|(10.051)|(17.402)|(0.606)|(4.210)|(24.454)|
|One product _× X_|-0.009***|14.169|4.937|-0.922|-4.466|32.113|
||(0.003)|(14.041)|(14.672)|(0.612)|(10.172)|(26.922)|
|No. of students|-0.008|-|-|-|-|-|
|in each school-grade|(0.005)||||||
|R-squared|0.268|0.261|0.259|0.263|0.260|0.258|
|No. of obs.|849|850|850|850|850|850|
||(g)|(h)|(i)|(j)|(k)|(l)|
|**December in 2014**|||||||
|All products|-0.318*|0.485|0.015|-0.403|-0.052|-0.185|
||(0.181)|(0.294)|(0.211)|(0.245)|(0.087)|(0.136)|
|One product|-0.181|0.554|0.166|-0.120|0.089|-0.147|
||(0.254)|(0.377)|(0.267)|(0.365)|(0.092)|(0.153)|
|All products _× X_|0.005*|-23.724*|-5.259|0.725|-7.669|25.971|
||(0.003)|(11.772)|(11.933)|(0.577)|(6.253)|(26.620)|
|One product _× X_|0.005|-20.832|-5.769|0.402|-7.942|53.571|
||(0.004)|(14.643)|(15.206)|(0.843)|(10.880)|(32.760)|
|No. of students|0.002|-|-|-|-|-|
|in each school-grade|(0.003)||||||
|R-squared|0.117|0.119|0.114|0.115|0.116|0.118|
|No. of obs.|845|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

66
children in primary school – is not possible. Such a measure would require baseline and followup data on school enrollment in local communities where qualifying schools are located, and these data were not collected. In addition, the approach of selecting at least one school per commune to provide access to the program across a wider geographic area also ensures that program schools are not closely clustered, making it more difficult to measure effects on area enrollment rates.

In order to assess the success of randomization, and to estimate the causal impacts of the program on schools, the 1,034 qualifying schools that applied were identified first in the 201112 school census, and then matched back to their entries in the 2002-3 school census. Given the time lapse between the census waves and program rollout, the rapid turnover of non-public schools in Haiti, and the lack of census identifiers in the data collected from qualifying schools, only 64% of the schools were identified in the 2011-12 census.<sup>8</sup> In the next step, 55% of these schools were then matched back to the 2002-3 census. In other words, 35% of the 1,034 qualifying schools are observed across the two census waves (Table 1).

Randomization was successful, as treatment and control schools were equally likely to be identified in the 2002-3 school census and had similar characteristics. Since baseline data were not collected in 2008, we use the 2002-3 school census data as a proxy for baseline, or pretreatment, data. Following randomization in 2008, an initial test for balance was conducted, in which 64% of treatment and 68% of control schools were successfully identified in the 20023 school census. These results, presented in the Appendix, also found that treatment and control groups were observationally equivalent, suggesting that randomization was successful.

Treatment schools appear more likely than control schools to have remained open through the 2011-12 school year. As shown in Table 1, schools participating in the TWP were much more likely than control schools to be identified in the 2011-12 census. 408 of the 539 (76%) participating schools were identified, compared to 244 of the 485 (50%) control schools. However, we cannot say with certainty that the schools not identified in the census had closed down. In Haiti, non-public schools can often change names or move locations. Without unique identifiers, tracking schools over time becomes very difficult, and even small changes in name

> 8 Identification is done manually using commune, school name, and school address.  Spelling variations and slight differences in information recorded in the TWP application data and across census years required that each school be manually identified in the 2011-12 census and then matched back to the 2002-3 census.

7
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_Table_ _<u>6: Program impact on health seeking behavior of nonbeneficiary households</u>_

||_Pr_<br>|_egnant wome_<br>|_n/last pregnancy_|_in the last 36 mo_<br>|_nths_|_Children _|_younger than 60_|_months old_|
|---|---|---|---|---|---|---|---|---|
||Any|Timely|C f|Postnatal care|Iiil||Gh|T|
||antenatal<br>care|initiation<br>of ANC|ontent o<br>care in ANC|within 24<br>hours|nsttutona<br>Delivery|MMR|rowt<br>Monitoring|reatment<br>seeking|
|_Panel A: Program impact_|||||||||
|Program village|-0.039<br>(0.029)|0.004<br>(0.059)|0.000<br>(0.202)|0.000<br>(0.054)|-0.061<br>(0.060)|0.032<br>(0.048)|0.020<br>(0.032)|0.001<br>(0.044)|
|_Panel B: Interaction with sa_|_turation_||||||||
|Program village|-0.074|0.022|-0.819|-0.087|-0.204|-0.170|0.144|0.117|
||(0.060)|(0.229)|(0.592)|(0.207)|(0.195)|(0.143)|(0.110)|(0.161)|
|Saturation|-0.168|-0.227|-2.577***|-0.285|-0.631***|-0.125|-0.088|-0.168|
||(0.123)|(0.301)|(0.860)|(0.262)|(0.231)|(0.220)|(0.133)|(0.224)|
|Program|0.066|-0.018|1.498|0.159|0.267|0.345|-0.208|-0.191|
|village*saturation|(0.110)|(0.363)|(0.994)|(0.333)|(0.312)|(0.249)|(0.172)|(0.273)|
|_Panel C: Interaction with ab_|_ove median s_|_aturation_|||||||
|Program village|-0.043|-0.061|-0.154|-0.018|-0.068|-0.029|0.056|0.015|
||(0.031)|(0.082)|(0.228)|(0.076)|(0.080)|(0.063)|(0.043)|(0.052)|
|Above median saturation|-0.075|-0.192**|-0.791**|-0.029|-0.164*|-0.137*|-0.014|-0.088|
||(0.049)|(0.093)|(0.338)|(0.076)|(0.090)|(0.083)|(0.046)|(0.072)|
|Program village*above|0.011|0.146|0.347|0.042|0.021|0.142|-0.083|-0.031|
|median saturation|(0.044)|(0.115)|(0.381)|(0.119)|(0.115)|(0.102)|(0.063)|(0.094)|
|_Panel D: Interaction with to_|_p quartile of s_|_aturation_|||||||
|Program village|-0.030|0.037|0.114|-0.010|-0.048|0.046|0.031|0.019|
||(0.024)|(0.063)|(0.216)|(0.062)|(0.069)|(0.055)|(0.037)|(0.046)|
|Fourth quartile of|-0.107|0.032|-0.380|0.092|-0.079|0.022|-0.039|0.144|
|saturation|(0.082)|(0.143)|(0.482)|(0.118)|(0.087)|(0.079)|(0.051)|(0.108)|
|Program village*fourth|0.019|-0.153|-0.329|-0.011|-0.012|-0.078|-0.038|-0.135|
|quartile of saturation|(0.091)|(0.160)|(0.544)|(0.142)|(0.121)|(0.108)|(0.065)|(0.137)|
|Control observations|172|180|169|178|180|239|268|258|
|Treated observations|202|203|184|198|203|267|299|291|
|Control mean|0.965|0.472|5.533|0.371|0.439|0.473|0.209|0.508|

note: *** p<0.01, ** p<0.05, * p<0.1 Child level outcomes include quadratic terms for child age (in months), and all specifications include municipality fixed effects. Standard errors are clustered at village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey 49
<!-- Start of picture text --> Policy Reserchl<br>Population, Health, and Nutirtion<br>WPS 1033<br><!-- End of picture text --> This paper is a product of the Population, Health, and Nutrition Division and the Population Policy and Advisory Service of the Population and Human Resources Department. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Otilia Nadora, room S6-065, extension 31091 (November 1992).

Population projections for all countries are prepared annually by the World Bank's Population and Human Resources Department. They are published first in summary forn in the Bank's _World Development Report_ and later in greater detail as technical notes or working papers and, in alternate years, as a book.

Separate papers cover the six Bank regions: (I) Africa (Sub-Saharan), (2) Latin America and the Caribbean (and Northern America), (3) East Asia and Pacific region and South Asia region combined, and (4) Europe and Central Asia region and Middle East and North Africa region combined.

# Among trends observed:

All of the Bank's regions have positive population growth rates, but a few countries are losing people. These countries are in one of two regions: in Latin America and the Caribbean, where several small island countries (Dominica, Grenada, and St. Kitts and Nevis) have recently had negative growth rates, and in Europe (Bulgaria, Hungary, and Ireland). In the case of the Caribbean islands, the cause of population decline is outmigration, whereas in Eastem Europe it results from a combination of belowreplacement fertility and outmigration.

> * On the American continent, fertility in all countries has dropped to below 6 children per woman, but some countries have gone much further than others. The total fertility rate for Latin America is intermediate at 3.1, and replacement fertility is projected to be reached in every country no later than 2035. In Northern America, fertility is at 2 children per womanthat is, just below replacement level.

- At the regional level, international net migration has a trivial effect on population growth. The most significant flow in the world occurs between Latin America and the Caribbean and Northern America, which amounts to just over one-half million persons per year. Migration often occurs for unpredictable reasons, and is often reversed in a short time. The projections of future net migration assume a gradual decline to zero from current levels.

The Policy Research Working PaperSeries disseninates the flndings of work under way in the Bank. Anobjective of the series is to get these findings out quickly, even if presentations are less than fully polished. The fndings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
Boyd and Prescott 1986). These market frictions, however, can also limit the extent to which financial institutions can reach out to clients. Transaction costs that to a large extent are independent of the size of the financial transaction – deposit, loan or payment – make outreach to clients with demand for small transactions costly. High information asymmetries and the resulting agency problems make outreach to opaque clients more difficult and costly. Barriers such as high minimum account balances and fees, multiple documentation requirements and high payment fees might reflect high transaction costs and the contractual and business environment in which banks operate. However, they might also reflect the competitive framework and the availability of physical infrastructure in the market where banks offer their services.

Financial exclusion can retard economic growth and increase poverty and inequality. Theoretical models have shown that financial market frictions that prevent broad access can be the critical mechanism for generating persistent income inequality or poverty traps (Banerjee and Newman, 1993; Galor and Zeira, 1993). A large empirical literature has established the importance of banking sector depth for economic development and poverty alleviation. Based on extensive cross-country databases, researchers have explored the relation between indicators of financial sector depth and GDP per capita growth, productivity growth, poverty, firm growth and entry rates (see King and Levine, 1993; Beck, Levine and Loayza, 2000; Demirguc-Kunt and Maksimovic, 1998; Rajan and Zingales, 1998; Beck, Demirguc-Kunt and Levine, 2004; Klapper, Laeven and Rajan, 2006). Much less is known, however, about the determinants and implications of access to financial services by individuals and firms. This is because data on who has access to which financial services remain thin and inadequate. This paper contributes to closing this gap in the literature.

Our data show substantial cross-bank and cross-country variation in barriers to banking. While banks in 19 countries do not impose any minimum balances for checking accounts, such 2
- **Oceans Beyond Piracy** , “The Economic Cost of Somali Piracy 2012,” Technical Report, One Earth Future Foundation 2013.

- , “The Economic Cost of Somali Piracy 2013,” Technical Report, One Earth Future Foundation 2014.

- , “The Economic Cost of Somali Piracy 2014,” Technical Report, One Earth Future Foundation 2015.

- **Rubinstein, Ariel** , “Perfect Equilibrium in a Bargaining Model,” _Econometrica_ , January 1982, _50_ (1), 97–109.

- **Rust, John** , “Stationary Equilibrium in a Market for Durable Assets,” _Econometrica_ , July 1985, _53_ (4), 783–805.

- **van Ours, Jan C. and Ben Vollaard** , “The Engine Immobiliser: A Non-starter for Car Thieves,” _The Economic Journal_ , 2015, pp. 1264–1291.

- **Vollaard, Ben and Jan C. van Ours** , “Does Regulation of Built-in Security Reduce Crime? Evidence from a Natural Experiment*,” _The Economic Journal_ , 2011, _121_ (552), 485– 504.

- **World Bank** , _The Pirates of Somalia: Ending the Threat, Rebuilding a Nation_ , Washington DC, USA: The World Bank, April 2013.

47
12

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