rafmacalaba/datause-encoder-data huggingface dataset:
are at best quasi-voluntary; many<sup>schoolsas many children were punishedand humiliatedwhen</sup> their families do not pay for these.<sup>Total privateexpensesper primaryschool child amountedto</sup> 4.4 percent of a typical poor<sup>household'snon-food expenditure(Table10). Privatecosts at public</sup> lower and upper secondary<sup>levelsaccountedfor9and21percentof totalhouseholdnon-food</sup> expenditure in 1998 compared<sup>to11.4and 21percent in1993,respectively.</sup>

**Table 10: Per student household**<sup>**expenditureonschooling,1993and1998**</sup> (as % of total household non-food<sup>expenditure)</sup>

|||Poorest quintile<br>|Richestquintile<br>|Vietnam|
|---|---|---|---|---|
|_1993_|||||
||Primary<br>|4.4<br>|3.0<br>|3.1<br>|
||Lowersecondary<br>|11.4<br>|5.1<br>|7.9<br>|
||Uppersecondary|20.9|7.6|15.8|
|_1998_|||||
||Primary<br>|4.9<br>|3.8<br>|3.4<br>|
||Lowersecondary<br>|8.9<br>|5.2<br>|6.4<br>|
||Uppersecondary|21.1|7.9|13.8|

_Source:_ Author's estirates based<sup>on 1992-93and1997-98 VLSS</sup> As a result, if a typical poor<sup>household(fromthebottom20%)hadtwochildrenin</sup> school: one at a primary and<sup>the otherat a lowersecondary school,it wouldcost this household</sup> 14 percent of its total non-food<sup>expenditurein1998comparedto16 percent in 1993.It should be</sup> noted that 75 percent of the<sup>poorest householdswere "foodpoor"(Table11).This meansthese</sup> poor households do not have<sup>enoughmoneytospendonfood to gettheminimumstandardof</sup> calories necessary for them,<sup>so 3 percentof householdspendingoneducationisconsiderableand</sup> much more difficult for them,<sup>comparedto better-off households.</sup>

**Table 11: Composition of consumption**<sup>**by the poorest,1993and 1998**</sup>

||Poores<br>|tgroup<br>|Richest<br>|group<br>|Viet<br>|nam<br>|
|---|---|---|---|---|---|---|
||||||||
||1993<br>|1998<br>|<br>|<br>|<br>||
|_Foodpoor(%)_<br>|100<br>|75<br>|0<br>|0<br>|25<br>|15<br>|
|_Totalconsumption_<br>|100|100|100|100|100|100|
|_expenditure_<br>|||||||
|Onfood<br>|70<br>|68<br>|44<br>|38<br>|55<br>|48<br>|
|non-food<br>|30<br>|32<br>|56<br>|62<br>|45<br>|52<br>|
|education|2|4|4|8|3|6|

Source: Author's estimates based<sup>on 1992-93and1997-98 VLSS</sup>

# _Composition ofprivate spending_<sup>_on primary education_</sup>

Table 12 shows composition of<sup>privatespending onprimary educationby the poorestand</sup> richest quintiles in 1992-93<sup>and1997-98.In1993,PTAandschoolcontribution(19.4%)and</sup> text books and school supplies<sup>(50%)werethelargest itemsintotal privatespending bythe</sup> poorest quintile. In contrast,<sup>better-offhouseholdsspentmostoftheirprimaryeducation</sup> expenditure on food and lodging<sup>(45%)and booksandschoolsupplies(15%)whilePTAand</sup> school contribution accounted<sup>foronly8%.In1998,PTAandschoolcontributioneven</sup> accounted for a larger share<sup>(22%)oftotaleducationspending perprimarychildfor poor</sup> 12
10 Tabte 3. Population In 2000 and Stationary Population by Geographic Region and for Selected Countries (mifllins)

||Projection|<br>of 1980|Projection|of 1987-88|Current|projection|
|---|---|---|---|---|---|---|
|Geographic region<br>or country|Population<br>in 2000|Stationary<br>population|Population<br>in 2000|Stationary<br>population|Population<br>in 2000|Stationary<br>population|
|World|6.075|9,867|6,176|10,650|6,205|10,806|
|Africa|805|2,071|875|2,531|**872**|2,506|
|America|847|1,267|824|1,216|825|1,224|
|Asia|3,561|5,569|3,634|5,974|3,657|6,087|
|**Europe and**U.S.S.R.|833|**922**|**818**|**888**|**821**|882|
|**Oceania**|28|38|29|41|29|40|
|Selected countries|||||||
|China|1,251|1,555|1,274|1,683|1,279|1,695|
|India|973|1,644|996|1,678|1.002|1,698|
|**Indonesia**|**203**|350|212|363|211|356|
|Brazil|201|345|178|292|**180**|306|
|Nigeria|153|425|163|529|163|529|

These revisions have affected the projected stationary populations dramatically. The eventual stationary world population now appears almost 10 percent greater than it did in 1980. For Africa in particular, the projected stationary population is now 21 percent larger than in the 1980 projections.

Aggregate changes since last year's projections, on the other hand, are minor. Projected world population in the year 2000 is up half of 1 percent this year as compared with last year. The largest revision for a geographic region is for Asia, but is barely above half of 1 percent.

Projections for practically all countries have changed, but in the majority of cases only minimally, and about as often upward as downward. The larger changes have been due more often to revisions in current demographic indicators rather than to revisions in assumed future trends. Current indicators may have changed, for instance, because of new data or new analysis of census or survey data. Revised current population estimates account for new projections for countries lilSe Swaziland, Costa Rica, Jamaica, Peru, and Papua New Guinea. Revised fertility estimates, often from Demographic and Health Surveys, account for new projections for countries like Nepal, Pakistan, Bolivia, Guatemala, and Nicaragua.

# ComRarisons among World Bank regions

The four World Bank regions, comprising all the borrower countries
in values:<sup>21</sup> _Aj/γj_ , the preference-adjusted technology, is calibrated using Equations (11) and (12). As the value of domestic income and expenditure calculated from the trade data do not perfectly match up, we dene _ψj ≡ Ej/_ � _Yj_ + _ηj_ � _i∈_ N _ji,t_<sup>_φiYi−φjYj_</sup> � _i∈_ N _ij,t_<sup>_ηi_</sup> � as an exogenous country-specic parameter that accounts for these trade imbalances. In the spirit of Dekle et al. (2007, 2008), we rst eliminate all exogenous trade imbalances and take the equilibrium without trade imbalances as baseline.

In order to highlight the alternative channels through which DTAs aect FDI, and also to capitalize on the full set of our partial estimates, we perform two sets of experiments. First, we rely on our estimates of the dummy variables for DTAs and DTAs with investment provisions from column (2) of Table 1 and column (3) of Table 2, respectively. Then, we also obtain corresponding eects based on the estimates of the continuous depth variables from column (3) of Table 1 and column (4) of Table 2. Consistent with the discussion in Section 2.2, we perform each of the two experiments in two steps. First, we change the vector of FDI frictions. Then, in addition, we change the vector of trade costs. As the DTAs are already in place, we perform an ex-post evaluation, i.e., we assume that in the baseline the agreement is in place and simulate the eect without DTAs as counterfactual. We then report the change from the baseline to the counterfactual, i.e., baseline value minus counterfactual value relative to the counterfactual value.

> 21 The values of _ωij_ are restricted to be between zero and one. Hence, we normalize each row by the maximum element. Further, all zero FDI ows are leading to zero _ωij_ 's by construction. To avoid this, we set _ωijMi_ = 1 _._ 00001 for those observations.

35
P( _y_ = j ) = F( -Xβ (j+1) ) - F( -Xβj ) j = 1, ..., m - 1 P( _y_ = m ) = 1 - F( -Xβm ) In specifications which include the innovating history of the firm, it can be argued that this variable is likely to be endogenous as the perceptions that employers have of labor market regulations may influence their propensity to innovate. This problem can only be solved if we are able to find a satisfactory instrument for the innovating history (i.e. a variable that is correlated with the innovating history but not with perceptions). The ICS provide information regarding the sources of the firm’s working capital. We measure the proportion of capital that comes from formal credit institutions<sup>23</sup> as an indicator for access to credit. The instrumental variable estimation is done in two steps, we first estimate a first-stage logit regression of the innovating history on firms’ characteristics and the instrument. We then include the fitted values of the first stage regression in the second stage model (described above) in place of the endogenous variable. To accommodate for the fact that fitted values are used, standard errors are calculated through bootstrapping.

# _Modeling firms’ behavior_

Having shown the extent to which perceived and _de jure_ labor regulations are correlated, in a second stage, we model the behavior of firms as a function of their perception of labor regulations, actual regulations and firm characteristics. We are interested in the way in which firms adapt their behavior to the constraints of labor regulations. In particular we look at two choices of firms:

- _Provision of training._ Since regulations make it harder for firms to fire workers, they may have an incentive to increase the human capital of their workers by offering them training.

- _Use of temporary employment_ . As discussed above, the extent to which firms rely on temporary workers is likely to depend on the combination of regulations of regular employment on the one hand and temporary employment on the other hand.

The independent variable of interest is the degree to which firms find actual regulations to be an obstacle to their business. We recognize however that employers’ perceptions of labor market regulations are likely to be endogenous to firms’ behavior. We therefore use in subsequent specifications our measures of _de jure_ regulations instead of perceptions; through the analysis in our first stage described above, we provide an assessment the quality of this proxy. We add combinations of variables measuring the strictness of regular and temporary employment regulations. We include these two types of

> 23 Formal credit institutions are the following: local commercial banks, foreign owned commercial banks, leasing arrangement, investment funds, special development financing, other state services, trade credit, credit cards, equity, sales of stock. Other sources of capital include: internal funds or retained earnings, family, friends, informal sources (e.g. money lenders).

17
**Figure 1: Exports by destination country or region**

<!-- Start of picture text --> Italy<br>Bosnia H.<br>Germany<br>Slovenia<br>Austria<br>Serbia<br>US<br>Hungary<br>Netherlands<br>UK<br>E. Europe<br>France<br>Czech R.<br>Poland<br>Macedonia<br>Romania<br>LAC<br>MENA<br>Switzerland<br>N. Europe<br>Africa<br>Spain<br>E. Asia<br>Russia<br>W. Asia<br>0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0<br>2012 2007<br><!-- End of picture text --> _Source: Staff elaborations based on data from the CBS. All numbers are in billion HRK_ Exports by county are presented in Table 5 while Table 6 shows the number of export destinations by county. Recent indicators show that Zagreb, Varadzin, Istria, and SisakMoslavina are currently the most important exporting counties in terms of volume (Figure 2). Over the six year period for which data is available, exports originating from Zagreb (city and county combined) account for approximately one third (35%) of total exports from Croatia. The second biggest exporter is Istria with only 10% of the total exports, while the smallest exporter in terms of volume is Lika-Senj with about 0.1% of the total. When it comes to the number of destinations, Zagreb exports to all 25 possible locations, followed by Primorje, Split and Istria, which export to about 20 countries and country groups. Lika shows the smallest value in terms of the number of destinations, only 5.

9
# 5. Robustness Checks

We compare Harmonized Learning Outcomes (HLOs) for primary reading scores with scores generated using IRT linking by the Linking International Comparative Student Assessment (LINCS) project. The LINCS project leverages overlap in items for a subset of ISATs focused on reading at primary school from 1970 onwards (Strietholt, 2014; Strietholt and Rosén, 2016).

Figures 9.0-9.6 below show our results. The HLO and IRT scores have large overlap with a correlation coefficient consistently above 0.95 for scores as well as ranks. This indicates we are able to produce similar results to an IRT methodology where there is overlap. This increases our confidence in the robustness of our estimates.

We further compare ranks derived from HLO scores versus raw scores and verify the original regional rankings are preserved. We are able to fully preserve underlying regional ranks within a year subject and schooling level. This is true by design due to a fixed exchange rate applied at this level of disaggregation. We do not show these results since they are true mechanically.

We conduct two additional robustness tests, shown in Annex 5. The first explores HLOs derived from multiple possible source tests and how this affects international and regional rankings. The second is a comparison of test scores by data availability of each test. These robustness tests confirm that even when HLO scores come from different tests they are broadly consistent.

A future robustness check could be the inclusion of psychometric adjustments. Jerrim et al. (2017) highlight a few features of ISATs that are important to consider when conducting aggregate test score comparisons. Jerrim et al. (2017) conclude that results of a paper they re-analyze by Lavy (2015) are robust to inclusion of these elements. This enhances our confidence that such adjustments are not strictly necessary, but we hope to include them as a robustness test in the future. For example, if we want a regional average we can weight countries to construct a reliable ‘regional average’ that is not distorted by artificially weighting each country equally since the true region consists of larger countries.

19
Levine, D.I. (1993), “Fairness, Markets and Ability to Pay: Evidence from Compensation Executives”, _American Economic Review,_ Vol.83, pp. 1241-1259.

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34
# 1. INTRODUCTION AND KEY FINDINGS

As formal trade barriers have declined within Central America over the past five decades, high logistics and transport costs have emerged as a key impediment to trade. The nations of the region have signed a series of agreements aimed at regional economic integration and both multi-lateral and bilateral treaties with countries outside the region. Most Central American countries rate relatively highly on international indicators of openness to trade, with Nicaragua, Guatemala, and Costa Rica ranking in the top 50 countries out of 180. Only Panama has a lower ranking on trade freedom than on economic freedom overall (see Table 1). Trade has expanded commensurately, with merchandise exports alone having attained 25 percent of GDP for the region over the past decade (See Figure 1) and total merchandise trade as a percent of GDP having substantially exceeded levels for other lower middle income countries (Table 2). Yet country rankings on the International Logistics Performance Index are relatively poor. Rankings out of 155 countries in 2012 were 61<sup>st</sup> for Panama, for Guatemala 74<sup>th</sup> , Costa Rica 82<sup>nd</sup> , El Salvador 93<sup>rd</sup> , Honduras 105<sup>th</sup> , and Nicaragua 107<sup>th</sup> (2010.)<sup>1</sup> Although in most countries of the region insecurity – crime, theft, and disorder – are viewed by businesses as a more severe constraint to doing business than transport costs, a substantial percentage of firms in Costa Rica (54 percent), El Salvador (32 percent), Guatemala (25), and Nicaragua (24) also rated the high cost of transportation as a major constraint<sup>2</sup> (see Table 3).

**TABLE 1:  SCORES ON ECONOMIC AND TRADE FREEDOM, 2013**

|**Country Name**|World<br>Rank<br>Economic<br>Freedom<br>Overall<br>(out of 180)|World<br>Rank<br>Trade Freedom<br>(out of 180)|Region Rank<br>Economic<br>Freedom<br>Overall|2013<br>Economic<br>Freedom|Score|2013<br>Trade Fr|Score<br>eedom|Tariff Rate (%)|
|---|---|---|---|---|---|---|---|---|
|**Costa Rica**|49|43|8|67.0||85.1||2.4|
|**El Salvador**|53|74|10|66.7||79.0||5.5|
|**Guatemala**|85|41|16|60.0||85.2||2.4|
|**Honduras**|96|87|18|58.4||77.1||6.5|
|**Nicaragua**|110|39|21|56.6||85.4||2.3|
|**Panama**|71|99|13|62.5||74.8||7.6|

Source: Heritage Foundation (2013)<sup>3</sup> Inefficiencies in the Central American road freight sector render international and domestic trade significantly more costly to exporters, importers, and consumers. High transport costs can take a variety of forms: long lead or delivery times, breakage, and spillage, all of which cause cargo to depreciate; uncertain timing of delivery, which impedes market access; and/or high transport and logistics prices, which reduce or “tax” the gains from trade directly.

> 12012 rankings were not available for Nicaragua.

> 2Source:  World Bank Enterprise Surveys 2010.

> 3Trade freedom is a composite measure of the absence of tariff and non-tariff barriers that affect imports and exports of goods and services. The trade freedom score is based on two inputs:  (a) The trade-weighted average tariff rate and (b) non-tariff barriers.

2
integration on child labor may be unique to the present case where most households are wellpositioned to benefit from an increase in the relative price of rice. Second, it could be the case that ethnic traditions and social norms are weaker in Vietnam than in other countries where they are believed to be important. Thus, economic factors have a greater ability to influence sibling differences in child labor in Vietnam than elsewhere.

# **References**

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- **Benjamin, D., Brandt, L.  (2004):** "Agriculture and Income Distribution in Rural Vietnam under Economic Reforms: A Tale of Two Regions," ."  in P. Glewwe, N. Agrawal and D. Dollar (eds), _Economic Growth, Poverty and Household Welfare: Policy Lessons from Vietnam_ .  Washington DC: World Bank.

- **Birdsall, N. (1991):** “Birth Order Effects and Time Allocation,” _Research in Population Economics_ , T. P. Shultz, ed., Volume 7, 191-213.

- **Butcher, K. and A. Case (1994):** “The Effect of Sibling Sex Composition on Women’s Education and Earnings,” _Quarterly Journal of Economics_ , 109(3), 531-563.

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44
According to the data, agriculture is the least productive sector, and mining is the highest productive sector in Guinea. Productivity in agriculture represents only the equivalent of 29.6 percent of the economy-wide productivity. Another key characteristic of the Guinean economy, highlighted, is the large productivity gaps between different sectors. Productivity in the mining sector is 54 times larger than that of agriculture. In fact, if all Guinea’s workers could be employed

**Table 1.** Guinea: Sectoral labor productivity and sectoral share of total employment in 2015

|Sector|Labor<br>Productivity<br>(millions of<br>GNF)|Ranking by<br>level of<br>sectoral labor<br>productivity|Sectoral share<br>of total<br>employment|Ranking<br>by<br>sectoral share<br>of total<br>employment|
|---|---|---|---|---|
|Agriculture, hunting, forestry<br>and fishing|2.75|<br>10|67.14|<br>1|
|Mining and quarrying|149.47|1|0.71|9|
|Manufacturing|20.67|6|4.95|3|
|Public utilities|63.52|2|0.13|10|
|Construction|28.79|4|1.62|8|
|Wholesale and retail trade, hotel<br>and restaurants|15.13|<br>8|13.72|<br>2|
|Transport and communication|20.21|7|2.79|6|
|Finance, insurance, real estate<br>and services to business|23.31|<br>5|3.60|<br>4|
|Community, social and|||||
|personnel services|14.07|9|3.30|5|
|Government services|47.85|3|2.02|7|
|**Economy-wide**|**9.40**|**-**|**100**|**-**|

Source: Authors’ calculations based on data from the INS.

in the mining sector, the country’s overall labor productivity would be multiplied by 2,230 times. Unfortunately, mining cannot absorb such large number of workers. Comparing sectors that have similar potential to absorb labor also reveals large gaps between sectors. For instance, productivity in construction is more than 10 times larger, and productivity in manufactures is 7.5 times larger than productivity in agriculture.<sup>5</sup> The productivity gap between agriculture and other sectors is also confirmed when comparing the situation in 2006 with that of 2015. The data also depict declining

> 5 For a discussion on the reasons for the productivity difference between agriculture and other sectors, see Mundlak _et al_ . (2012), and Gollin _et al_ . (2014a, b). In the specific case of Guinea, that difference could be due to weak and less efficient investment in the agriculture sector and the slow path of adoption of new technologies by that sector.

10