rafmacalaba/datause-encoder-data huggingface dataset:
may be larger when this relationship is incorporated into the general equilibrium analysis. As a result, using the estimates in columns 2 and 3 leads to more conservative effects.

Table 3: Elasticity of bilateral frictions to transportation time

|Dep Var:|||�<br>_τ_ <sup>1</sup><sup>_−σ_</sup><br>_ijt_|||
|---|---|---|---|---|---|
||(1)|(2)|_,_<br>(3)|(4)|(5)|
|log(Transportation Time)_ij_|-1.572***<br>(0.105)|||||
|log(Transportation Time)_i_=_j_||-2.252***<br>(0.135)|-1.867***<br>(0.115)|-1.836***<br>(0.236)|-1.515***<br>(0.228)|
|log(Transportation Time)_i_=_j_||-0.678***<br>(0.181)|-0.734***<br>(0.189)|-1.024***<br>(0.252)|-1.043***<br>(0.266)|
|Language Proximity_ij_|0.206***|0.171***|0.151**|0.164***|0.151**|
||(0.059)|(0.058)|(0.060)|(0.058)|(0.060)|
|Colonial Ties_ij_|0.795**|0.735**|0.737**|0.690**|0.692**|
||(0.329)|(0.334)|(0.343)|(0.339)|(0.349)|
|PTA_ij_|1.085***|0.777***|0.870***|2.500***|2.447***|
||(0.163)|(0.168)|(0.170)|(0.723)|(0.824)|
|PTA_ij ×_ log(Transportation Time)_i_=_j_||||-0.581**<br>(0.242)|-0.495*<br>(0.256)|
|Observations|2,220|2,220|2,220|2,220|2,220|
|# Origins|20|20|20|20|20|
|# Destinations|110|110|110|110|110|
|Period|2018|2018|2018|2018|2018|
|Transportation|no MAR|no MAR|MAR|no MAR|MAR|
|Estimator|OLS|OLS|OLS|OLS|OLS|

_Note:_ MAR indicates that transportation time is estimated considering maritime preferences. Exporter( _i_ ) and Importer ( _j_ ) fixed effects are included in all regressions. Standard errors in parentheses are clustered by country-pair, *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Colonial Ties _ij_ variable is from Mayer & Zignago (2011); while for Language Proximity _ij_ we the use “lp” variable from Gurevich et al. (2021) (to be more precise we use _log_ ( _lp_ + 0 _._ 01)). The dependent variable _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> is the unexponentiated reciprocal of bilateral trade friction _τij,t_ and indicates trade ease between countries _i_ and _j_ : increasing transport time implies decreasing bilateral trade easiness.

14
# **Analyzing the Dynamics of School Dropout in Upper Secondary Education in Latin America: A Cohort Approach**

**Raja Bentaouet Kattan**

**World Bank**

## **Miguel Székely**

**Center for Education and Social studies, Mexico City**

JEL classification: D1,I2, J21.

Keywords: Dropout rates, Education policy, Public policy, Secondary education, Household surveys, Returns to education, Education, Substitution effect, Income effect, Dropout.

The authors would like to thank Emanuela Di Gropello and Maria Marta Ferreyra for thoughtful comments and suggestions. The inputs and support received from Melissa Merchant, Elizabeth Monroy and Pamela Mendoza are also gratefully acknowledged.
This marginal improvement resulted in a widening educational gender gap with the subset of former British colonies where the gains achieved by women have been more substantial (see Figure 2 below).

**Figure 2: Trend in gap between male and female years of schooling**

<!-- Start of picture text --> 2.5<br>English speaking French speaking<br>2<br>1.5<br>1<br>0.5<br>0<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> The relatively poor performance achieved by French-speaking countries had direct consequences for female labor force participation rates. Although the long-term trend of labor force participation between men and women suggests a narrowing gap across the board, the gap between English and French-speaking countries remains significant and has even widened in recent years (see Figure 2 in the Annex). In part, this probably reflects the persistent effects of educational segregation on employment and its long-term effects for occupational job segregation, assuming that social norms and cultural beliefs are not markedly different across the two subsets of countries.

The widening gender gap between former French and English colonies may also be attributed to potential endogeneity whereby the level of economic development affects the extent of gender inequality. In this regard the rapid reduction of the education gender gap in the former English colonies may be largely attributed to their superior economic performance. Yet, numerous empirical studies which control for endogeneity support evidence of a positive relationship between the reduction of educational gender gap and growth [Klasen (1999), Knowles et al. (2002), BalliamouneLutz and McGillivray (2009)]. A few examples are the work of Klasen (1999), Dollar and Gatti 7
high- and middle-income countries to low-income countries. Benefits-transfer involves adjusting VSL estimates developed in other countries for income differentials between countries. There are disadvantages to using such a benefits-transfer method; the most important one being that individual preferences with respect to risk are influenced strongly by cultural factors and may be quite different in low-income countries. Moreover, differences in the extent of the availability and cost of health services are likely to influence wage-risk tradeoffs in these countries.

The benefits-transfer methods used in this analysis rely on estimates developed by Viscusi and Aldy (Viscusi and Aldy 2003). Based on more than 60 studies of mortalityrisk premiums from 10 countries, they estimate an income elasticity of the value of a statistical life<sup>5</sup> of about 0.5 to 0.6, but their elasticity estimates are influenced downward by three extreme observations for India. Dropping these observations yields an elasticity of roughly one (Becker and Elias 2003) and is the value used in more recent studies (Becker, Philipson et al. 2003). Using the lower elasticity would yield implausibly high estimates of VSL in low-income countries.

Using an income elasticity of VSL of one and starting from the U.S. VSL of $6.1 million (associated with a U.S. real per capita GDP in 2004 of roughly $40,000), we can compute the VSL of India (with real per capita GDP in 2004 of $625) to be $94,721. This translates to $3,162 per year of life saved, at a 3 percent discount rate<sup>6</sup> .

# **Mortality projections**

Data on TB incidence and mortality from 2006–2015 are derived from WHO epidemiological models (WHO 2006). These models project TB incidence (both total and HIV+ cases), prevalence, and mortality for the years 2004–2015 under three scenarios: No DOTS, Sustained DOTS, and Global Plan strategy. The 2004 data from this report

> 5 This refers to the percentage change in VSL for a one percent change in GDP.

> 6 An alternative, although arbitrary approach, adopted by some authors is to assume that VSL is 100 times GDP per capita (Jamison, Sachs et al. 2001; WHO 2005). Since we assume an elasticity of VSL with respect to GDP of one (Becker and Elias 2003), our estimates of VSL are a direct multiple of GDP in the same ratio as for the United States and indicate a VSL to GDP ratio of roughly 150.

12
13 on GDP. Its sign is expected to be negative, also for cost effective reasons; (c) foreign trade share on GDP. Its sign is expected to be positive, since in an open economy it is easier to tax international trade; (d) GDP per capita whose sign is expected to be negative. Moti advanced nations are able to implement more sophisticated and efficient tax systems, and thus will tend to rely less heavily on easy to collect but highly distortive taxes such as trade taxes; and (e) urbanization ratio, whose sign is expected to be negative. The reason is that it is relatively easier to tax the urban population than the rural population.

For a sample of 58 developing nations, CET obtained the following results from an OLS regression (standard errors in parentheses) of seignor12 age on political instability and other structural variables: Seignorage - - 0.020 + 0.0021 Share of Agriculture in GDP (0.032) (0.0005)

- 0.0431 Openness - 0.44E-5 GDP Capita (0.0182) (0.024E-5)

+ 0.0019 Urbanization + 0.1583 Political Instability Index (0.0004) (0.0539) -2 - 0.44 S.E. - 0.049 The CET results are very suggestive. Not only does the regression explain a high percentage of the cross-country variability of seigniorage, 1<sup>2</sup> A1l variables are measured as averages for 1971 - 1982. Seigniorage is the change of high powered money as a percentage of government tax revenue plus increase in high powered money. Openness is measured as Import plus export over GDP. Notice that this equation excludes the mining and manufacturing shares. Including results in an insignificant coefficient, with the expected sign, with no other changes in the regression.
Share of employment in Transport vs. VA per capita 1980, 1990, 2000, 2010 <!-- Start of picture text --> Share of employment in Other services vs. VA per capita<br>1980, 1990, 2000, 2010<br>3»<br>&<br>gocn ° : e<br>® ee<br>3 zee e<br>2 ee e<br>a e e . ‘ 5 e<br>We e ee. / e<br>o= e<br>is) e 80<br>®o e e e e<br>a AG e° e e &&e =<br>® C) ecco See<br>Oo 8.8 © c ? @ ee e id<br>0 2 4 6<br>In(VA per capita), const USD @ PPP<br><!-- End of picture text -->
ability. They also use sibling reports of each other’s educational attainment as an instrumental variable to deal with measurement error. Analyzing data from five Chinese cities in 2002, the estimated returns to a year of schooling are only 3.8 percent, compared to an OLS estimate of 8.4 percent. As has been pointed out by others, schooling differences between twins are unlikely to be random, so that twins-based estimates will still be subject to endogeneity bias (Bound and Solon, 1999; Neumark, 1999). Nonetheless, the results suggest a much greater degree of ability bias in China than has been found in twins studies in the U.S.

Previewing our main findings, taking the means of our preferred IV estimates, we find that the returns to a year of schooling in urban China in 2001 are 7.6 percent compared to 8.3 percent using OLS, the returns to college education versus high school are 34.7 percent compared to 42.0 percent using OLS, and that the returns to high school versus middle school are 27.3 percent compared to 20.9 percent using OLS. Thus, consistent with ability bias, IV estimates of the overall returns and the returns to college are lower than the OLS estimates. The fact that the IV estimate for the returns to high school is higher than the OLS estimate is also consistent with an ability bias story in which selection into college creates a negative ability bias for those who complete only high school. We show that it is unlikely that our results are driven by sample selection bias associated with migration, or by alternative pathways through which the Cultural Revolution could have affected adult productivity.

The rest of the paper is organized as follows. In section 2, we describe the data and key variables used in the estimation. In section 3, we provide evidence on the disruptive effects of the Cultural Revolution on educational attainment. Section 4 presents the estimating equations for the determinants of log wages and discusses our identification 5
# TABLE 7. Cross Country Growth (Developed and Developing)

# DEPENDENT VARIABLE. GR7385

|N of OBS.|<br>23|23|23|23|78|78|78|78|78|.78|78|<br>78|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Const.|1.643<br>0.230|2.712<br>0.371|5.888<br>0.812|5.781<br>0.783|11.797<br>2.38-|11.621<br>2.32-|11.685<br>2.30-|11.895<br>2.30-|11.786<br>2.36-|11.760<br>2.33-|11.711<br>2.37*|11.502<br>2.15'|
|LGDP73|-2.991<br>-0.336|-0.340<br>-0.368|-0.728<br>-0.799|-0.700<br>-0.746|-1.966<br>-2.50'|-1.935<br>-2.43^|-1.938<br>-2.38*|-1.984<br>-2.40^|-1.965<br>-2.49*|-1.959<br>-2.45'|-1.975<br>-2.51*|-1.937<br>-2.23-|
|A173|0.083<br>2.26-|0.069<br>1.88'|-0.047<br>1.367|0.047<br>1.376|0.132<br>3.26-^|0.127<br>3.13--|0.129<br>3.02--|0.133<br>3.07^|0.132<br>3.22^-|0.132<br>3.24-"|0.135<br>3.32--|0.130<br>2.68*|
|BAR75|0.076<br>|0.058<br>|0.116<br>|0.107<br>|0.381<br>|0.380<br>|0.380<br>|0.376|0.381|0.381|0.396|0.391|
||0.572|0.406|0.838|0.758|2.25|2 2.25|2.23-|2.30-|2.22'|2.26*|2.23'|2.34'|
|EEC12||-0.388<br>-1.335||-0.101<br>-0.336|||||||||
|EFrA|||0.727<br>2.11|0.661<br>1.797|||||||||
|SACU||||||0.774<br>0.598||||||0.786<br>0.603|
|CACM|||||||-0.512|||||-0.397|
||||||||-0.623|*||||-0.424|
|LAFrA||||||||0.175||||0.257|
|||||||||0.232||||0.323|
|CEAO|||||||||0.043|||0.148|
||||||||||0.068|||0.213|
|ANDEAN||||||||||-0.167<br>-0.242||-0.250<br>-0.381|
|UDEAC|||||||||||1.619<br>0.869|1.626<br>0.851|
|R2|0.182|0.228|0.290|0.293|0.212|0.216|0.214|0.212|0.212|0.212|0.219|0.224|
|MSE|0.818|0.816|0.734|0.805|2.698|2.711|2.713|2.716|2.716|2.716|2.704|2.792|

Notes: GR7385 = Average rate of growth of the income per capita for the 1973-1985 period (S & H). LGDP73 = Logarithm of real GDP per capita in 1973 (S & H).

A173 = Average investment as a share of GDP 1973-1985 (S & H).

BAR75 = Barro and Lee (1992) average schooling of the population over 25 years old in 1975.

EEC, EFrA. SAClf. CACM, LAFTA. CEAO _=_ dummy variables for each one of the respective integration schemes. t-test significant at the 5% level.

t-test significant at the 1% level.

39
|Home<br>GATS<br>RTACommitments<br>ServicesTrade PolicyDatabase<br>Statistics|User Guide|
|---|---|
|ServicesTrade Policy Database and STRI<br><br><br>||
|Economies:<br>Bangladesh; Hong Kong, China<br>Back t|o selection screen|
|Sectors:<br>Telecommunication Services; Distribution Services||
|NOTE THAT THERE ARE LIMITATIONS AS TO THE NUMBER OF ECONOMIES THAT CAN BE SHOWN ON THE RESULTS PAGE, BUT THE FULL <br>BE DOWNLOADED IN EXCEL OR CSV.|SELECTION CAN|
|O Regulatory Information©sTRI @||
|_>Showfilters||
|<br>X Clearfilters|@|
|Economy||
|Telecommunication Services<br>42.1|18.2|
|Fixed-LineTelecommunication Services<br>42.1|19.2|
|Mode 3<br>438|15.7|
|Mode 4<br>268|50.0|
|MobileTelecommunication Services<br>42.1|19.2|
|Mode 3<br>43.8|15.7|
|Mode 4<br>268|50.0|
|Internet Services<br>42.1|16.3|
|Mode 3<br>43.8|12.5|
|Mode 4<br>268|50.0|
|Distribution Services<br>26.8|27.0|
|Wholesale Trade Services<br>24.7|27.0|
|Mode 1<br>15.7|15.7|
|Mode 3<br>26.9<br>Mode4<br>268|26.9<br>50.0|

<!-- Start of picture text -->
| |Bangiadesh |Hong Kong, China<br>Mode 2016 2016<br>Economy<br>Telecommunication Services ALL 42.1 "18.2<br>Fixed-Line Telecommunication Services 3 43.8 "15.7<br>Fixed-Line Telecommunication Services 4 26.8 50.0<br>Fixed-Line Telecommunication Services ALL "42.1 "19.2<br>Mobile Telecommunication Services 3 43.8 "15.7<br>Mobile Telecommunication Services 4 26.8 50.0<br>Mobile Telecommunication Services ALL "42.1 "19.2<br>Internet Services 3 43.8 12.5<br>Internet Services 4 26.8 50.0<br>Internet Services ALL "42.1 "16.3<br>Distribution Services ALL 26.8 27.0<br>Wholesale Trade Services 1 15.7 15.7<br>Wholesale Trade Services 3 "26.9 6.9<br>Wholesale Trade Services 4 "26.8 "50.0<br>Wholesale Trade Services ALL 4.7 27.0<br>Retailing Services 1 15.7 15.7<br><!-- End of picture text -->

|||||Bangiadesh||HongKong,China|
|---|---|---|---|
||Mode|2016|<br>2016|
|Economy<br>||||
|Telecommunication Services|ALL|42.1|"18.2|
|Fixed-Line Telecommunication Services|3|43.8|"15.7|
|Fixed-LineTelecommunication Services<br>|4<br>|26.8<br>|50.0<br>|
|Fixed-LineTelecommunicationServices|ALL|"42.1|"19.2|
|<br>MobileTelecommunication Services|3|43.8|"15.7|
|MobileTelecommunication Services<br>|4<br>|26.8<br>|50.0<br>|
|MobileTelecommunication Services<br>|ALL<br>|"42.1<br>|"19.2<br>|
|Internet Services<br>|3<br>|43.8<br>|12.5<br>|
|Internet Services<br>|4<br>|26.8<br>|50.0<br>|
|InternetServices<br>|ALL<br>|"42.1<br>|"16.3<br>|
|Distribution Services<br>|ALL<br>|26.8<br>|27.0<br>|
|WholesaleTrade Services<br>|1<br>|15.7<br>|15.7<br>|
|WholesaleTrade Services<br>|3<br>|"26.9<br>|6.9<br>|
|WholesaleTrade Services<br>|4<br>|"26.8<br>|"50.0<br>|
|WholesaleTrade Services<br>|ALL<br>|4.7<br>|27.0<br>|
|RetailingServices|1|15.7|15.7|
utilization of child health care was a key condition to receive cash benefits, the greater exposure to nutrient rich ASFs among children participating in the program would have resulted from either the income effect of the cash transfer (relaxing budget constraints), Pantawid’s family development sessions which emphasized infant and young child feeding and nutrition, or both. In addition research using the 2013 NNS data - which collected detailed data on children’s nutrient intake and food sources through a 24 hour recall module - found that the diets of children aged 6-60 months were concentrated primarily to foods with low nutrient density such as refined rice with little contribution to total energy or nutrients from vegetables, meats, fruits and eggs.<sup>32</sup> A recent observational study looking at the relationship between ASF consumption and stunting globally using DHS data finds that children between the ages of 18 and 23 months who consume 2 or 3 types of ASFs the previous day are around 6 pp less likely to be stunted compared to children who do not consume any ASFs, controlling for other child and household factors.<sup>33</sup> In South, Central and Southeast (SE) Asia, 56.8 percent of children (6-23 months) consumed any ASF the prior day (compared to a high of 83 percent in Latin America & Caribbean and a low of 49.3 percent in Eastern & Southern Africa). Of the children who consumed any ASF, the vast majority (75 percent) only consumed 1 type of ASF - the highest rate across all regions - suggesting relatively low exposure to different kinds of ASFs among children in South, Central and SE Asia. High prices may be a key constraint to relatively low levels of ASF consumption. The prices per-calorie of ASFs relative to the prices per-calorie of the cheapest staple cereal in South, Central and SE Asia, range from a high of 7.8 for fresh cow’s milk to a low of 5.3 for fish; chicken eggs are 6.5 times more expensive, on average, per calorie than the cheapest staple cereal.<sup>34</sup>

# **_4.3 Stunting risk factor trends in the Philippines_**

In the Philippines, population-level trends for key prenatal and postnatal stunting risk factors are positive and creating a more favorable environment for children (Figure 16). The fact that under-5, infant and neonatal mortality have all declined since 2000 at least partly reflects this.

22
- 76 -

# APPENDIX TABLE 6 (COntinue.d

DOMINICA TRANSPORT COST PROFILE FOR EXPORTS TO THE UNITED STATES IN 1986

|||U.S.|DOMINICA NO|miNAL FREIGH|T RATE|DOMI NI CA<br>TARIFF|"OTHERS"|NOMINAL FRE|IGHT RATE|"OTHERS"<br>TARIFF|<br>SHARE IN<br>DOMINICA'S|
|---|---|---|---|---|---|---|---|---|---|---|---|
|OBS|TSUSA|<br>IMPORTSMS|)<br>TOTAL|VESSEL|AIR|RATE|TOTAL|VESSEL|AIR|RATE|EXPORTS|
|131|7463400|<br>11.425|3,27|3.27|0.00|5.00|17.56|16.34|49.50|1.49|<br>0.075|
|132|7603600|<br>14.971|<br>5.24|0.00|5.24|17.76|4.64|1.93|7.66|11.25|<br>0.099|
|133|7604800|<br>41.629|<br>2.25|2.25|0.00|6.29|6.34|5.12|31.68|6.47|0.274|
|134|7714334|<br>36,325|<br>0.13|0.00|1.08|4.44|6.70|6.18|24.08|3.64|0.239|
|135|<br>7721500|<br>13,133|<br>5.15|5.15|0.00|4.19|11.31|10.90|33.79|1.92|<br>0.087|
|136|7729700|<br>13,711|<br>14.22|0.00|0.00|5.70|14.28|13.95|37.99|2.25|0.090|
|137|7733510|<br>14,700|<br>5.44|0.00|5.44|4.69|5.80|4.97|7.38|3.94|0.097|
|138|7911520|<br>76.919|<br>0.86|0.00|0.86|0.00|1.69|0.81|1.70|6.04|0.507|
|139|8000035|<br>389.370|<br>2.63|3.27|2.90|0.00|2.53|4.65|1.97|0.00|2.566|
|140|8065040|<br>22.237|<br>37.55|0.00|37.55|0.00|5.08|6.45|5.75|0.00|0.147|
|141|9999500|3,809,999|<br>0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|25.110|

OEI$ OESCRIPTION

- 131 Ornamental articles of dried or Oleached natural plants

- 132 Refill cartridges for pens ana peAcils

- 133 Cas6o pencils and pencils, nspf

- 134 Film, strips, and sheots of ilekible rubber or plastics nspf, not of cellulosic plastics, over 0.009 Inch In thickness

- 135 Household articles of rubber or plastic

- 136 Religious articles of rubber or plastics, except christmas articles

- 137 Machinery belts and beiting, of rubber or plastics, flat (not contalnin g textile fibers)

- 138 Fur wearing apparel of mink skins

139 U.S. products returned after export without being advanced In value or improved In condition abroad, nos

- 140 Articles exported for repairs or alterations and returned, nes--dutiable value 141 Under $251 formal and informal entries estImated
7

# Table 1. Estimates of the Error Correction Model, 1970-2013

||Pooled w/o dummy<br>variables<sup>1</sup>|Pooled w/ du|mmyvariables for sepa|rateperiods<sup>2</sup>|
|---|---|---|---|---|
||1970-2013<br>(1)|1970-1985<br>(2)|1986-2000<br>(3)|2001-2013<br>(4)|
|α|-0.43**|-0.35|-3.17***|-0.52**|
||(0.17)|(0.53)|(0.64)|(0.19)|
|Short-run elasticity (β)|2.82***|2.13***|2.77***|3.43***|
||(0.36)|(0.60)|(0.35)|(0.21)|
|Speed of adjustment (-γ)|0.12**|0.18|0.58***|0.31**|
||(0.05)|(0.31)|(0.13)|(0.13)|
|δ|0.20**|0.23|1.26***|0.40**|
||(0.09)|(0.39)|(0.26)|(0.17)|
|Long-run elasticity<sup>3</sup>(-δ/γ)|1.70***|1.31***|2.18***|1.31***|
|Stationarity of the residual|yes|yes|yes|yes|
|||(2) vs (3)|(2) vs (4)|(3) vs (4)|
|Test that long-run elasticity differs across periods<sup>3</sup>||8.68***|0.00|291.21***|
|Rsquared|0.740|0.957|0.957|0.957|
|N<br>_Source:_IMF World Economic Outlook (April 2014) and|43<br>authors' calculations|43|43|43|

_Notes:_ Standard errors in paranthesis; *** indicates a significance level of 1%, ** of 5%, and * of 10%. 1dln(imports)t=α + β*dln(gdp)t + γ*ln(imports)t-1 + δ*ln(gdp)t-1+εt, where imports refers to goods and services, and GDP is based on PPP rates. 2dln(imports)t=α1 + β1*dln(gdp)t*DV1 + γ1*ln(imports)t-1*DV1 + δ1*ln(gdp)t-1*DV1 +α2 + β2*dln(gdp)t*DV2 + γ2*ln(imports)t-1*DV2 + δ2*ln(gdp)t-1*DV2 +α3 + β3*dln(gdp)t*DV3 + γ3*ln(imports)t-1*DV3 + δ3*ln(gdp)t-1*DV3 +εt, where imports refers to goods and services and GDP is based on PPP rates. 3 Significance established using non linear Wald test We then use the estimated coefficients to decompose the growth rate of world trade into its short-term and long-term components. Figure 2 reproduces this decomposition for the 2000s. A back of the envelope calculation for the period 2012-2014 indicates that cyclical factors account for 47 percent of the trade slowdown, while long-term factors explain the remaining 53 percent.