rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Public education in Venezuela has deteriorated steadily over the past 25 years. While the average educational attainment of the labor force increased from 6.1 years to 8.2 years (Ortega and Pritchett, 2006) and the literacy rate for people 15 and older went from 85 percent to 93 percent between 1981 and 2001 (Ortega and Rodriguez, 2008), the government's expenditures on education dropped 36 percent in real terms between 1980 and 2003 (Ortega, 2005). Average aptitude test scores for high school seniors have dropped from 21 to 6 in verbal ability and from 11 to 3 in math reasoning between 1987 and 2003. Possibly as a result of the deterioration in the quality of education, and of other changes in the labor market, the Mincerian returns to education have dropped from 15 percent in 1975 to under 10 percent in 2003.

Working amid this disconcerting evidence is Fe y Alegría, a confederation of Jesuit schools targeting disadvantaged youth. The program's first primary school was established in Catia, a disadvantaged area of Caracas, in a home donated by a local bricklayer. Since then, it has expanded to serve 1.2 million students in 15 Latin American countries (Gonzalez and Arevalo, 2005). The organization has a number of initiatives, including job training, teacher training, adult and radio education, and support for microbusinesses, but the bulk of its efforts are spent in primary and secondary education. Most observers, from community members to academic researchers, consider Fe y Alegría to be quite successful, but no econometrically satisfying program evaluation has been undertaken.

Through an econometric estimation of Average Treatment Effect, we compare Fe y Alegría graduates to a control group of Venezuelan public school students using the results of the Prueba de Aptitud Académica (PAA), a math and verbal test similar to the American SAT. Our results show that Fe y Alegría students perform slightly but significantly better on both parts of the PAA. The difference between two of our estimators suggests a heterogeneous treatment effect, which we then estimate, showing that the program is especially beneficial for the disadvantaged portion of the student body. Finally,
annual deforestation rates for contiguous zones that straddle park boundaries for over 4,000 protected areas in 64 tropical forest countries. We calculate the ratio of deforestation rates outside and inside the boundaries of each park in each year. We adopt this ratio as our measure of protection effectiveness and use panel regression techniques to estimate the protection impacts of many park characteristics, including legal establishment dates.<sup>2</sup> The remainder of the paper is organized as follows. Section 2 describes our database, while Section 3 explains the computation of outside/inside deforestation ratios. We present out results in Section 4, along with a discussion of the potential implications. Section 5 summarizes and concludes the paper.

# **2.  Database Construction**

The database for this exercise has four main components: 30 m forest clearing data from Hansen et al. (2013);<sup>3</sup> the World Database on Protected Areas (WDPA);<sup>4</sup> a shapefile identifying tropical forest areas, drawn from Hansen et al. (2003); and the database of Global Administrative Areas Version 2.0 maintained by GADM.org.<sup>5</sup>

# **2.1 Forest clearing data**

The Hansen 30 m data are downloadable as a large set of tiff panels that cover all forested areas of the globe. This paper uses the panels that cover the pan-tropics. For each 30 m pixel where forest clearing is estimated to have occurred between 2000 and 2012, the pixel entry is the final two digits of the year with the most clearing. To make this exercise tractable (the pixel-level

> 2 This paper focuses on forest cover loss, but we recognize that these are not the only measures for assessing conservation effectiveness. For example, we did not address degradation of forest in this paper.

> 3 Available online at http://www.earthenginepartners.appspot.com/science-2013-global-forest/download.html.

> 4 Available online at http://www.protectedplanet.net/.

> 5 Available online at http://www.gadm.org/. Accessed 2014-03

2
Underlying the most commonly used spatial equilibrium models is the assumption that an area is organized into a discrete number of regions. This assumption is relevant to South Asian countries. As argued above, they do not trade much with each other, except India and Nepal where the former is the latter’s largest trading partner. Thick borders in South Asia result in distinct production and trading regions.

Market integration is how price interdependence can be assessed. In this instance, it can be assessed using a model of oligopsonistic competition - where a large number of producers (farmers) sell to a small number of buyers. In South Asia, the number of farmers is the result of a cascading pattern: farmers can only sell to markets in their own country and within each country they may only sell to a few markets. The model can be formulated as follows: Assume three separate markets X, Y, Z that can buy product from various producers that are evenly distributed between them. Assume supply at each market is of the widely used form for spatial markets: where P is the delivered price offered (i.e. DAP price), _a_ and _b_ are constants and _v_ is a constant parameter >-1.

The FOB price can be assumed to be made up of price p offered by the firm and u being the costs of transporting the product from farm to market. Assuming transport costs are 1 per unit of distance e.g. $1 per tonne per km then: Combining the two equations we obtain: The above result is intuitive, that in integrated spatial markets the catchment area is in part influenced by distance around the market. However, in South Asia the lack of cross-border trade in tea, except for Nepal, reflects the impact of several barriers. The survey data show that no Bangladesh and Indian farmers and traders sell produce across the border while up to a third of Nepalese do so. There are several reasons that are cited for not being able to trade across the border (Table 17). In Bangladesh it is high transaction costs, in India it is largely related to poor market intelligence while in Nepal the main reason is the difficulty in making cross-border transactions.

**Table 17: Barriers to sourcing** **<u>produce across the border</u>**

|**Reason**|**Bangladesh**|**Country (%)**<br>**India**|**Nepal**|
|---|---|---|---|
|High transaction costs|44||3|
|Poor market intelligence|9|43|3|
|Lack of business contacts|9|44|10|
|Border crossing problems|9|12|42|
|Other|29|1|42|

_Source: Field Survey_ 25
**Table 9:  Radio access and Education Knowledge**

|Respondent knows:|Government<br>must provide<br>textbooks|Schools are<br>not allowed<br>to charge<br>fees|Government<br>policy is 40-<br>50<br>students/class|School-leaving<br>exam (CEP)<br>harder to pass in<br>last 3 years|Commune<br>pass rate for<br>CEP|National<br>pass rate for<br>CEP|Literacy<br>center in<br>commune?|
|---|---|---|---|---|---|---|---|
|Number of private|-0.011|-0.009|-0.034|-0.022|0.002|-0.020|0.01|
|non-comm. stations|(0.49)|(0.48)|(0.04)|(0.14)|(0.89)|(0.14)|(0.41)|
|Number of children|-0.005|0.007|-0.002|-0.005|0.002|-0.011|-0.01|
|listed|(0.55)|(0.40)|(0.81)|(0.60)|(0.81)|(0.13)|(0.15)|
|**Radio X children**|**0.006**|**-0.0003**|**0.005**|**0.005**|**0.001**|**0.006**|**0.003**|
||**(0.04)**|**(0.88)**|**(0.05)**|**(0.06)**|**(0.68)**|**(0.01)**|**(0.27)**|
|Health Worker visit for|0.015|0.053|0.10|0.058|0.067|0.051|0.067|
|malaria discussion|(0.54)|(0.02)|(0.00)|(0.02)|(0.001)|(0.02)|(0.00)|
|Percent correct|.66 (Yes/No)|.78<br>(Yes/No)|.52 (5<br>possible<br>answers)|.47 (3 possible<br>answers)|.27 (4<br>possible<br>answers)|.25 (4<br>possible<br>answers)|.77<br>(Yes/No)|
|Observations|3,573|3,650|3,663|3,663|4,200|3,578|3,640|
|R-squared|0.13|0.13|0.17|0.09|0.07|0.10|0.11|

Note: Ordinary least squares with commune fixed effects. P-values based on village-level clustered standard errors reported in parentheses. Regressions include all controls found in the full specifications of tables 4 and 5, plus whether household was visited by a health worker. Percent correct refers to fraction of respondents who answered the question correctly; the number of possible answers is indicated in parentheses (“open” means that the question was open-ended and no answers were prompted).

37
17 ED _i_ /EXP = 1<sup>+</sup> i<sup>/EXP +</sup> 1<sup>(log EXP) +</sup> 2<sup>INTREM +</sup> 3<sup>(INTREM)(log EXP)</sup> + 4<sup>EXTREM +</sup> 5<sup>(EXTREM)(log EXP) +</sup> 1<sup>HS/EXP +</sup> 1<sup>HS</sup> + 2<sup>AGEHD/EXP +</sup> 2<sup>AGEHD +</sup> 3<sup>CHILD5/EXP +</sup> 3<sup>CHILD5</sup> where: ED _i_ = annual per capita household expenditure on one of four levels of education (preparatory, primary, secondary or higher/university) EDHD = years of education of household head FEMHD = female head of household dummy (1 if household head is female, 0 otherwise) and all other variables are defined as in equation (9) Equation (13) can be estimated separately for each of the 3 groups of households: those receiving no remittances, those receiving internal remittances and those receiving international remittances. It can also estimated separately for each of the 4 levels of education: preparatory (1-2 years), primary (grades 1-6), secondary (grades 7-12) and higher/university (4 years).

Parameter results from equation (13) are shown in Tables 9 and 10. Table 9 shows the results without remittance variables, and Table 10 shows the results with both remittance variables.

Since the goal is to understand how remittances affect marginal expenditures on different levels of education, the focus here is on the interactive term: log of total annual per capita household expenditures times remittances dummy. In Table 10 the interactive
# **1. Motivation**

In 1995, Bernard and Jensen published the first of series of papers that use large comprehensive longitudinal data from surveys performed regularly by official statistics in the United States to look at differences between exporters and non-exporters in various dimensions of firm performance, including productivity (see Bernard and Jensen 1995, 1999, 2004). These papers started a new strand of economic literature, as researchers all over the world began to use the rich data sets collected by the statistical offices to study the export activity of firms, its causes, and its consequences. The extent and causes of productivity differentials between exporters and their counterparts which sell on the domestic market only is one of the core topics addressed.

In this literature, two alternative but not mutually exclusive hypotheses about why exporters can be expected to be more productive than non-exporting firms are discussed and investigated empirically (see Bernard and Jensen 1999; Bernard and Wagner 1997). The first hypothesis points to self-selection of the more productive firms into export markets. The reason for this is that there exist additional costs of selling goods in foreign countries. The range of extra costs include transportation costs, distribution or marketing costs, personnel with skills to manage foreign networks, or production costs in modifying current domestic products for foreign consumption. These costs provide an entry barrier that less productive firms cannot overcome. Furthermore, the behaviour of firms might be forward-looking in the sense that the desire to export tomorrow may lead a firm to improve performance today to be competitive in the foreign market. Cross-section differences between exporters and nonexporters, therefore, may in part be explained by ex-ante differences between firms: The more productive firms become exporters. The second hypothesis points to the role of learning-byexporting. Knowledge flows from international buyers and competitors help to improve the post-entry performance of export starters. Furthermore, firms participating in international 2
**Table 7: Elasticities of WTP for own country with respect to average GDP per-capita level in expert’s country, with income as the only variable (raw) and with background variables included , see table 12 (corrected)**

||Rou|nd 1|Roun|d 2|
|---|---|---|---|---|
||Coefficient|t statistic|Coefficient|t statistic|
|WTP plan A (raw variable)|0.587|2.00|0.760|2.82|
|WTP plan A (corrected)|0.453|1.57|0.679|2.65|
|WTP plan B (raw)|0.688|2.35|0.781|2.98|
|WTP plan B (corrected)|0.627|2.08|0.794|3.12|

**Table 8: Elasticities of WTP for own country with respect to average Purchasing Power Parity (PPP) corrected GDP per-capita level in expert’s country; with income as the only variable (raw) and with background variables included , see Table 13 (corrected)**

||Rou|nd 1|Roun|d 2|
|---|---|---|---|---|
||Coefficient|t statistic|Coefficient|t statistic|
|WTP plan A (raw variable)|1.30|2.50|1.54|3.20|
|WTP plan A (corrected)|0.98|1.96|1.28|2.82|
|WTP plan B (raw)|1.44|2.76|1.57|3.37|
|WTP plan B (corrected)|1.28|2.43|1.50|3.35|

The number of data points in our survey (48 experts were included) is too small to yield precisely estimated coefficients for the effects of average national income on average national WTP for Amazon forest preservation. We see, however, that for estimations with respect to noncorrected GDP, in Table 8, virtually all estimated elasticities are in the range 0.5 -0.8.<sup>15</sup> It thus appears as relatively safe to assume this as our main conclusion. Elasticities with respect to PPP-

> 15 Note that we are presenting both “raw” (with only per-capita national income included) and “corrected” calculations (which include a number of relevant background variables some of which are significant; see here also Table 12 below).

16
**_51_**

**Q4** MWXR =L. RF, i= **Q**

# where

MWXR **=** merchandise-weighted conversion factor;

**wj=** merchandise trade weight in Quarter i

**(xi + mi)** 2:(xi **+ in')** x, = merchandise exports in Quarter i; mi = merchandise imports in Quarter i; RFj = official exchange rate in Quarter i; i = Quarter **1,2,3** 4

# Deviations of Merchandise-Weighted Exchange Rates

**72.** The magnitude of the deviations of merchandise-weighted rates (MWXR) from<sup>the</sup> average annual official rates for the period **1980-88** are presented in<sup>Table</sup><sup>**13.A**closer</sup> examination of the percent deviations of MWXR from the official rate reveals that<sup>for most</sup> countries the deviations are relatively minor. Consistently about **_95_** percent of the<sup>countries</sup> exhibit deviations of less than **_5%_** over the period **1980-88,** as shown in Table<sup>**13.**Thus,it</sup> is evident from the results presented in Table **13** that the MWXRs,<sup>ingeneral, donot</sup> reveal anything significant about the preference order between the MWXR and<sup>theofficial</sup> rate for most countries.
## **REGIONAL INTEGRATION AND TECHNOLOGY DIFFUSION: THE CASE OF NAFTA**

# **1.** Introduction

The literature on regional integration agreements (RIAs) is vast and deals with political, economic and political economy issues. A recent overview of that literature is World Bank (2000) and Schiff and Winters (2003). The literature on the economics of RIAs deals mostly with static effects, and concludes that these effects are in general ambiguous. This has led a large number of economists to be skeptical about the benefits of RIAs, particularly for South-South ones. Bhagwati and Panagariya (1996), World Bank (2000) and Schiff and Winters (2003) show that, under homogeneous goods, a South-South RIA is likely to lower bloc welfare. The latter two studies also show that the less developed member country is likely to lose relative to the more developed one.

As for North-South RIAs, the Southern member is likely to lose in the case of homogeneous goods because it typically has higher trade barriers than the Northern member, so that it provides larger transfers to the North than it obtains through its improved access to it.- For instance, Panagariya (1999) finds that NAFTA resulted in a static loss for Mexico in 1996 of US$ 3.26 billion, or 0.98% of GDP.

On the other hand, CGE models using the Armington assumption of products differentiated by country of origin typically generate gains for Mexico from NAFTA. These gains are small under the assumptions of constant returns to scale and perfect

> l A sufficient (though not necessary) condition for this result is an internal solution where the Southern member continues to import from excluded countries after formation of the RIA.

2
|Table B.4: Fiscal (pro)cyclicality of corporate tax|rates, whole sampl|e (2000-2016): Im|pacts of develop|ment level, tax ba|se, credit constra|ints, corruption, r|ule of law and or|der|||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Dependent variable: Individual income tax|||||||||||
|Cyclicality of real GDP|-0.338***<br>(0.043)|-1.221**<br>(0.521)|-4.267***<br>(0.936)|0.085<br>(0.099)|-1.349<br>(0.896)|-1.181***<br>(0.391)|-2.312<br>(3.248)|-0.634<br>(0.773)|-0.534<br>(4.002)|0.389<br>(0.962)|
|**Development level**|||||||||||
|Crgdp*dvpt.level (polytomic)||0.290*<br>(0.169)|||||||||
|**Tax base effect**|||||||||||
|Crgdp*tax|||0.476***<br>(0.113)||||||||
|Crgdp*tax.base.variability||||-0.679***|||||||
|||||(0.153)|||||||
|**Credit constraints**|||||||||||
|Crgdp*credit.ratio|||||0.018<br>(0.018)||||||
|Crgdp*kaopen||||||2.144**|||||
|||||||(0.979)|||||
|**Perceived corruption**|||||||||||
|Crgdp*pol.corruption|||||||3.437<br>(5.331)||||
|Crgdp*pub.service.corruption||||||||0.758<br>|||
|||||||||(1.612)|||
|**Rule of law and order**|||||||||||
|Crgdp*law.order|||||||||0.054<br>(0.991)||
|Crgdp*rule.of.law||||||||||-1.741<br>(2.745)|
|Constant|59.535***|18.554***|59.612***|63.086***|64.350***|60.164***|40.269***|37.649***|60.042***|29.966***|
||(1.136)|(1.251)|(2.950)|(3.590)|(2.151)|(3.093)|(2.894)|(1.805)|(3.233)|(4.539)|
|Adjusted R-squared|0.732|0.732|0.639|0.636|0.741|0.730|0.724|0.723|0.715|0.728|
|<br>Rmse|4.369|4.371|6.055|6.080|4.323|4.388|3.695|3.703|4.468|3.673|
|Joint significance (p-value)|-|0.000|0.000|0.000|0.000|0.000|0.001|0.000|0.000|0.000|
|CD test (p-value)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Order of integration|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|
|Cluster|country|country|country|country|country|country|country|country|country|country|
|Covariates|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|Observations|831|831|291|291|766|730|700|700|751|700|
|i|||||||||||
|# countres|50|50|19|19|50|50|47|47|48|47|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. Coefficients attached to cyclicality of GDP or its interactions are multiplied by 10000. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.3 for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

33
# investment in natural resource sectors and infrastructure.

In contrast to the other EMDE regions, potential growth in SAR was virtually unchanged in 2011-21 and became, together with EAP, the strongest among EMDE regions. All the drivers of growth remained broadly steady, with demographic trends remaining supportive and investment weakness and lower TFP growth in India offset by robust investment growth and solid TFP growth elsewhere.

# 3. Prospects for regional potential growth

In the absence of reforms, potential growth in EMDEs is projected to decline further in the remainder of the 2020s (figure 3). The pandemic-induced shock in 2020 is expected to have lasting effects on long-term growth across EMDEs, and many of these effects will be exacerbated by the fallout from the war in Ukraine. The adverse effects of the two shocks on human capital, investor confidence, fixed capital formation, and supply chains will weigh on long-term growth prospects.

Current projections of the fundamental drivers of potential growth in EMDEs suggest that it will slow by a further 0.9 percentage point a year on average in the remainder of this decade (2022-30) to 4.0 percent a year.<sup>5</sup> The slowdown is expected to be broad-based, reflecting declining contributions from all the fundamental drivers of growth, but especially from capital accumulation, which accounts for more than half of the slowdown. Decelerating TFP growth and slowing labor supply growth are each expected to account for one-quarter of the slowdown.

Of the six EMDE regions, EAP is expected to experience the sharpest decline in potential growth during 2022-30—about 1.6 percentage points a year on average. This is primarily due to reduced capital accumulation and slower TFP growth, especially in China. The country’s policy efforts to rein in credit growth are expected to resume once economic activity recovers from pandemic disruptions. After a decade of resilience, potential growth elsewhere in the region is also expected to moderate somewhat (by 0.1 percentage point a year on average) as labor force growth eases.

In ECA and SSA, potential growth is projected to slow somewhat. Investment weakness and diminishing demographic dividends in the rest of the decade are expected to be only partially offset by a moderate pick-up of TFP growth as the adverse shocks of the past decade subside. In ECA, the slowdown in potential growth also reflects the fallout from the war in Ukraine that will depress investment in the region for several years.

In SAR, LAC, and MNA, potential growth is projected to be broadly unchanged in 202230. SAR benefited from demographic tailwinds over the past decade, but these are expected to fade in the remainder of the 2020s; this is expected to be offset by a recovery in TFP growth. Labor force growth is expected to continue declining in LAC, but this

> 5 Throughout this paper, potential growth projections for 2022-30 are predicated on population size and composition in line with the medium fertility scenario of the UN Population Projections, trend improvements in education and health outcomes, and investment growth constant at its long-term average.