rafmacalaba/datause-encoder-data huggingface dataset:
variable, as in some of the regressions in Helpman et al. (2008). The estimated coefficients for the outcome equation are very close to those from OLS, even though the Heckman model strongly rejects the null hypothesis of independence between the selection and outcome equations (prob. = 0.000). Interestingly, the selection equation suggests that similar dynamics to those from the outcome equation may also be present in relation to trade propensity: exporter and importer logistics both have a positive and significant effect on trade propensity, as well as trade intensity. This result sits well with recent findings indicating that trade facilitation, of which logistics is a key part, can play an important role in expanding trade at the extensive, as well as intensive, margin (Dennis and Shepherd, 2011; and Shepherd, 2010). Again, there is also evidence from both the outcome and selection equations that importer logistics performance has a greater impact on parts and components trade than trade in final goods; however, the exporter interaction has an unexpected negative and statistically significant coefficient.

# **4.3 Results Using Separate Models by Sector**

One possible objection to the interaction term approach used in the two previous sections is that it constrains the other parameters of the model—such as income elasticities and trade cost elasticities—to be uniform across the two sectors we are considering, namely final goods and parts and components. To address this issue, we re-estimate the baseline and alternative models from the previous two sections separately for each of the two sectors (Table 4). A comparison of coefficients across models allows us to draw inferences about the relative importance of logistics in each sector.

In each of the six columns of Table 4, importer and exporter logistics performance both have positive and 1% statistically significant coefficients, which is in line with expectations. As was the case for the interaction term regressions in Table 4, the magnitude of the estimated coefficients tends to be smaller with Poisson than with alternative estimators. In each case, the estimated coefficient on importer 15
15

# Appendix 2. Supplemental Tables

Table 1. Sites and Treatments Administered

|_Site_|_Agency_|_InstructionalTreatment_|_Numberoflearners_|
|---|---|---|---|
|Goey|OSEO|PhonologicalAwareness (PA)|17|
|<br>Sogpeelse|INA|PhonologicalAwareness|66|
|Zaongo|SEO|PhonologicalAwareness|27|
|<br>Godre|OSEO|<br>RapidReading(RR)|25|
|Kamsaooge|OSEO|RapidReading|18|
|<br>Nagreongo|INA|RapidReading|28|
|<br>Samande|INA|RapidReading|22|
|Koaada|OSEO|PA+RR|31|
|Koasenge|INA|PA+RR|28|
|<br>Kulkeka|_INA_|PA+RR|23|
|Waanvuus|OSEO|PA+RR|35|
|Kulkeka/jeunes|[NA|Control|33|
|<br>Nongstenga|OSEO|Control|17|
|<br>Tanlaorgo|OSEO|Control|28|
|<br>Wurgu|INA|Control|27|

Table 2. Effect Sizes for Letter Identification Test

|_Sponsor_|_Phonological_|_Rapid_|_PA+ RR_|
|---|---|---|---|
||_Awareness_|_Reading_||
|OSEO|025|369**|323**|
|INA|879**|439**|.726**|

Table 3. Percent Correct Means and Standard Deviations On the letter Identification Test for Treatment Groups and Sponsors

|_Sponsor_|_Phonological_<br>_Awareness_|_RapidReading_|_PA+ RR_|_Control_|_Totals_|
|---|---|---|---|---|---|
|OSEO|69.43|7976|7840|68.68|74.79|
||(31.55)|(24.03)|(26.56)|(3001)|(2803)|
|INA|7367|5875|6850|4380|60.14|
||(2755)|(3640)|(2945)|(3398)|(33 42)|
|Totals|7070|73 86|74.82|5814||
||(3019)|(29.29)|(2788)|(33.82)||

Table 4. Effect Sizes for Syllable Identification Test

|_Sponsor_|_Phonological_<br>_Awareness_|_RapidPA+R_<br>_Reading_|_PA+ RR_|
|---|---|---|---|
|OSEO|-197|292**|260|
|INA|I04**|851**|912**|
_Gender Equality and Economic Development: The Role for ICTs_ 16 internally customized software, capital depreciation, and the like), and spending on telecommunications and other office equipment. (Source: WDI)

# **Independent Variables – Control Variables**

# <u>Regressions for Gender Inequality in Education</u>

1. _Per Capita Real GDP (Trend Value):_ HP Filtered Chain Weighted Per Capita Real GDP.  This variable serves as a simple proxy for the level of economic development.  It is adjusted for purchasing power parity so that values are comparable across different countries.  The series is also adjusted using a chainweighted index to enable comparison across time periods.  To account for the possibility of endogeneity, this variable was lagged by one year when used as an independent variable in the regressions.  (Source: Penn World Table Version 6.1 website at <u>http://pwt.econ.upenn.edu/)</u><sup>9</sup> To remove cyclical fluctuations, this series is smoothed by apply the Hodrick-Prescott filter<sup>10</sup> .

2. _Youth Sex Ratio:_ The youth sex ratio is defined as ratio of the number of females between the ages 0 to 14 to the number of males between the ages 0 to 14, expressed as a percentage. Population numbers are based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of the country of origin. (Source: WDI) 3. _Average Years of Schooling:_ Average years of schooling of the total population, or equivalently average educational attainment, has been frequently used in the growth literature to proxy for the amount of education received by the population of a country<sup>11</sup> . There are two main global data sets on educational attainment. The first and probably more widely used data set is that of Barro and Lee (1993, 2001)<sup>12</sup> . This data set contains information educational attainment for persons

> 9 See Heston and Summers (1991) and Heston, Summers and Aten (2002) for more details.

> 10 This is a smoothing method that is widely used among macroeconomists to obtain a smooth estimate of the long-term trend component of a series. The method was first used in a working paper (circulated in the early 1980's and published in 1997) by Hodrick and Prescott to analyze postwar U.S. business cycles.  See Hodrick and Prescott (1997).

- 11 For example, see Barro and Sala-i-Martin (1995), Barro (1997, 1999), Krueger and Lindahl (2000) and Temple (1999).

- 12 The Barro-Lee data set is based on school attainment figures obtained from UNESCO and they estimate missing values using data on school enrollment rates and the structure of population by age-groups. More specifically, their fill-in procedure uses the gross enrollment rate, adjusted for repeaters.  Barro and Lee claim that this measure reflects the inflows of new school graduates to existing educational
_9_ income data and concludes that this tax appears far less regressive when cxpenditure rather than income is used as the basis for analysis. Of course, year-to-year fluctuations in income among poor households may exaggerate the regressiveness of taxes. Following Poterba's warning regarding the use of income, we have used in our analysis of **tax** burden uolh income and expenditure approaches.

The income unit which corresponds with the income concept employed in the Bulgarian survey is the _household._ The household concept adopted in the survey includes a one-person household, one family household, and a household of more than one family that makes common provision for food or other essentials for living. This definition corresponds closely to the definition of the 1980 World Population Census Program (United Nations, 1978). To acuount for variation in the household size, we shall use in our analysis the _wumal household income per capita._ Finally, the consideration of household composition requires the use of an adult equivalent scale. The construction of such a scale is fraught with 7 a number of conceptual and practical difficulties. Hence, the calculation of such a scale has not been attempted for this paper.

Our analysis uses a partial equilibrium method for estimating the distribution of tax burdens. That is, taxes on factor income such as the income tax are taken to affect household positions from the sources side only (the burden being distributed in line with earnings subject to tax). Further effects from the uses sides, resulting from changes in relative prices are not taken into account. However, if each income group spends the same proportion of its 7/The literature on the best procedures is controversial see, for example, Deaton and Muellbauer (1980) and Ravallion (1992).
# **BOX** . Is o ,lating L th f., of ji t.1lint . .

Although the "before" and "after" periods can be tailored to the timing of adjustment in each country, change in a performance indicator in the program countries is attributed exclusively to program effects. However, during the 1980s the nonprogram determinants of performance varied widely over time and between countries. Thus the situation prevailing before the program is not a good predictor of what would have happened in the absence of a program.

The control-grcnp approach examines changes in nonadjusting countries to estimate what would have happened in adjusting countries in the absence of programs. Hlowever, it assumes that all countries face the same external enviromnent, and ignores the effects of preprogram and other country-specific characteristics on performance. The results may be biased if the external environment of program countries differs systematically from that of nonprogram countries. Finally, the approach requires a uniform definition of the period before and after adjustment, since the same period must apply to all countries.

The modified control-group approach adopted by the World Bank (1990a) minimizes the sample-selection bias and takes account of the changing external environment of each country. This approach recognizes the nonrandom selection of structural adjustment program countries, identifies the specific differences between adjusting and nonadjusting countries in the preprogram period, and then controls for these differences in comparing subsequent performance. To correct for the self-selection bias, the approach calculates the probability that a country will undertake: an adjustment progr based on lagged values of performance indicators (including external shocks, fiscal deficit, and dummy variables for concurrent IMP programs, world regions, and high debt), world nonprogram variables, and individual country characteristics.

Source: World Bank (1990a).

## B.Data

2.08 Data Qualitv: Crosscountry comparisons between literature sources are difficult because sources do not use the same indicators or the same time period, and it is often difficult to determine which figures are more likely to be correct (box II.2). At times, differences result from discrepancies in definitions: for example, education spending as a percentage of GNP refers alternatively to public and private spending, to public spending, or to central government spending. Sources that provide average data for five-year intervals ignore annual changes in the intervening years that can be significant. Other sources ignore long-term trends dating back to the 1970s. Finally, the age-specific population data used **to** derive gross enrollment rates are based on data for fertility and infant mortality from the last reliable census and extrapolations using a presumed population distribution (Berstecher and Carr-Hill, 1990).

15
Table 9a: Change in the volume of imports in 2030, percent change from baseline

||World|BRI<br>Area|Cambodia|China|Indonesia|Lao<br>PDR|Malaysia|Philippines|Thailand|Vietnam|
|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|4.6|6.2|17.3|4.4|3.8|7.4|19.1|7.3|24.0|6.2|
|Minerals n.e.s.|0.4|0.6|4.9|0.7|0.5|7.8|-1.8|-0.4|4.4|0.7|
|Coal|4.8|7.8|8.2|9.7|2.5|24.2|9.0|-0.4|22.7|9.0|
|Oil|0.0|1.0|-9.5|0.2|-0.6|1.6|19.0|-1.1|10.2|2.9|
|Gas|0.5|1.8|-16.0|0.0|-0.1|22.2|8.2|6.1|6.9||
|Textiles|2.8|3.9|3.2|4.0|1.7|2.3|7.5|1.0|12.1|1.6|
|Wearingapparel|2.4|4.4|10.8|5.6|0.7|14.1|17.6|2.0|30.9|1.5|
|Leathergoods|3.1|5.7|16.4|5.5|4.6|13.6|15.6|-0.2|21.7|5.2|
|Processed foods|3.5|5.6|17.9|5.4|5.5|10.2|18.5|4.7|19.1|6.5|
|Woodproducts|4.1|6.1|26.2|8.3|7.0|8.4|24.5|4.2|13.2|2.2|
|Paper products,<br>publishing|3.7|5.6|4.1|10.3|2.8|6.7|14.3|2.8|14.2|2.7|
|Petroleum, coal<br>products|5.1|7.8|17.7|12.1|2.6|9.8|12.4|5.6|36.9|5.6|
|Chemical, rubber,<br>plastic|3.0|5.0|4.1|6.7|3.6|7.7|12.8|2.1|13.4|2.7|
|Energyintensive man.|2.4|4.0|6.4|5.9|1.5|4.4|9.7|0.0|19.5|2.4|
|Metalproducts|4.0|5.5|6.2|6.7|5.0|3.4|15.8|1.2|13.9|1.8|
|Electronics|6.9|10.5|18.0|7.6|4.1|5.0|34.5|8.5|45.6|2.9|
|Machinery and<br>equipment|4.8|6.7|7.8|7.7|2.9|4.8|18.2|3.2|18.8|1.1|
|Transport equipment|3.8|6.1|13.0|7.7|10.3|3.0|15.6|5.9|20.5|4.0|
|Manufactures,n.e.s.|3.9|6.2|8.3|14.5|1.5|9.9|9.3|1.9|16.0|6.7|
|Electricity|0.3|1.1|-2.2|0.8||2.5|8.0||8.3|1.7|
|Construction|5.9|8.3|18.3|9.1|5.6|19.8|21.3|8.0|20.2|5.1|
|Trade services|4.6|7.2|25.8|5.9|5.6|13.8|30.1|5.9|38.5|9.9|
|Other transport|4.7|7.5|15.5|10.2|6.0|18.4|6.7|7.7|29.3|8.3|
|Water transport|2.9|4.3|14.7|13.2|6.7|14.7|3.6|10.2|2.0|6.6|
|Air transport|4.2|6.9|13.4|9.0|6.6|15.7|8.9|8.0|24.7|8.0|
|Hospitalityservices|0.3|1.8|10.6|0.6|0.7|0.3|14.9|1.0|18.5|1.9|
|Other business services|0.1|1.4|8.6|0.6|0.4|0.6|11.0|1.2|17.8|2.7|
|Other services|0.1|0.7|6.7|0.7|0.5|1.3|10.4|1.1|16.6|2.4|
|**Agriculture**|4.6|6.2|17.3|4.4|3.8|7.4|19.1|7.3|24.0|6.2|
|**Manufacturing**|4.1|6.4|9.2|7.4|3.6|5.4|19.5|4.4|23.4|3.0|
|**Services**|1.9|3.9|12.9|4.5|2.9|7.7|12.8|3.9|22.3|4.4|
|**Other**|0.4|1.4|5.0|0.9|-0.5|7.6|12.2|-0.9|10.0|1.6|
|**Total**|3.4|5.4|9.5|5.6|3.4|5.7|18.5|4.0|21.4|3.2|

- 38 -
|Wane, 2004|Yes (Table<br>A1.2 and<br>Text)|To show that the design of WB projects is a key determinant of<br>its quality and hence effectiveness.|<br>Design effects are a crucial component of quality. The quality of aid is endogenous to the<br>relationship between the donor agency and the recipient government. Highly capable<br>and accountable governments only accept well-designed projects. The stock of ESW<br>deliveries at project entry has a positive impact on the quality of projects.|
|---|---|---|---|
|Winters, 2010|No|Has the World Bank operated in such a way that it favors<br>programmatic lending in well-governed countries and project<br>lending in poorly governed ones? And in the context of project<br>lending, does the World Bank use more targeted projects (for<br>example, subnational projects) in countries that suffer from<br>poor governance?|<br>The author examines the impact of borrower governance characteristics on overall World<br>Bank aid flows and find that, controlling for other country characteristics, the Bank gives<br>more money to well-governed countries. Moving beyond this aggregate focus to look at<br>specific aid project decisions, it reports how the World Bank alters its lending strategies<br>in terms of the modalities through which it gives aid, finding that well-governed<br>countries receive a largerproportion of theirproject lendingas nationwideprojects.|
||Yes (Table|How does the division of financing (number and concentration|<br>World Bank projects with any cofinancing and with less concentrated financing receive|
|Winters, 2019|A1.2 and<br>Text)|of financial collaborators in WB projects) relate to overall<br>project effectiveness?|less positive evaluations. The estimated correlations, however, are generally small in size,<br>conditioning on other project characteristics and using country and year fixed effects.|
|Wood et al., 2020|Yes (Table<br>A1.2 and<br>Text)|To investigate country-level “macro” factors and project-level<br>“micro” factors in driving project-level outcomes for Australian<br>aid and compare it to other donors.|<br>Between 7%-13% of the total variation in Australian aid project outcomes is a result of<br>country-level factors as opposed to attributes associated with projects, in line with other<br>donors. Larger projects tend to be evaluated as more effectiveness. The only country-<br>level significant covariate was GDP per capita, with wealthier countries having more<br>successful projects. Limited project-level characteristics available to test (size, duration<br>and sector only).|

Notes: ‘Question of interest’ and ‘Main findings’ often quoted directly from the abstract. The full reference of studies not included in the main literature review are listed at the end of this Annex.

36
# **_Annex V: Graphical Representation and Diagnostics for Open Economy Formulation_**

## **Graph (11): Plot of Levels and First Differences of Variables for Open Economy VAR**

<!-- Start of picture text --> 2020 2020 2020 2020 2020 2020<br>2000 2000 2000 2000 2000 2000<br>1980 1980 1980 1980 1980 1980<br>1960 1960 1960 1960 1960 1960<br>Drm2 Drgdp DINFL DInterestrt DDISC DDexrtend<br>0.2 0.0 0.2 0.0 10 -10 2 0 5 0 0.5 -0.5<br>2020 2020 2020 2020 2020 2020<br>2000 2000 2000 2000 2000 2000<br>1980 1980 1980 1980 1980 1980<br>1960 1960 1960 1960 1960 1960<br>rm2 rgdp INFL Interestrt DISC Dexrtend<br>6 4 7 5 20 0 10 5 15 5 0.5 0.0<br><!-- End of picture text --> 73
**Table 22: Raw gender gaps % (2007 and 2009)**

|**Occupation**|Gender gap i|n weekly hours|Gender gap i|n monthly pay|
|---|---|---|---|---|
||2007|2009|2007|2009|
|Leaders in all fields|101.0|96.9|120.6|84.3|
|Top-level professionals|96.7|96.8|83.7|82.6|
|Mid-level professionals|98.8|96.6|94.4|94.2|
|Officers|102.4|108.4|103.8|116.1|
|Skilled workers in personal services, security protection and sales|98.5|98.1|77.6|72.5|
|Skilled workers in agriculture, sylviculture, and aquaculture|90.0|90.2|67.7|64.7|
|Skilled handicraftsmen and other relating skilled manual workers|99.5|92.5|69.4|60.4|
|Assemblers and machine operators|102.7|101.7|65.3|65.0|
|Unskilled workers|96.7|93.8|80.0|75.2|
|Total|95.8|94.3|73.7|75.1|

_Source: (LFS, 2007 and 2009) Note: ratio of women’s number of hours by men’s; ratio of women’s monthly wage by men’s._

**Figure 1: Kernel density of wages in main occupation by gender (2008)**

<!-- Start of picture text --> Natural log of hourly wage in main occupation by sex (2008)<br>-2 0 2 4 6<br>Natural log of hourly wage<br>Male Female<br>kernel = epanechnikov, bandwidth = 0.0890<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> _Source: VHLSS 2008_ The gender wage gap is estimated to be similar in 2006 and in 2008 (Table 23). Compared with the results for previous years (Liu, 2004), the gender wage differential is lower than in the nineties (it is now about 65 percent of its value in 1992-93), but it has stopped its decrease in the last two years of VHLSS data (2006 and 2008). While in the estimations for 2006, correcting for selection into wage employment decreased 34
process. Within the provinces in the evaluation, parishes were randomly drawn and, within these parishes, a sample of 1,488 households was randomly selected. (Parishes are the lowest geographic unit in Ecuador, roughly equivalent to counties in the United States.) Households that had previously received transfers from the _Bono Solidario_ were excluded from the sample, as were households whose Selben score made them ineligible fir the BDH. One-half of households in the sample were randomly assigned to a treatment group, and the other half to the control group. We refer to the first group as “lottery winners” and the second group as “lottery losers”. Lottery losers were taken off the roster of households that could be activated for BDH transfers. Note that households in the two groups lived in the same neighborhoods or villages—unlike the evaluation of _PROGRESA_ and most other cash transfer programs in Latin America, where random assignment took place at the community level.

Table 1 uses data from the baseline survey to compare the characteristics of households that were randomly assigned to treatment and control groups (upper panel), and those that received and did not receive BDH transfers (lower panel). The upper panel of the table shows that random assignment was successful: Differences between lottery winners and lottery losers at baseline in a number of characteristics—including expenditure patterns, the food share, household size, and the characteristics of the household head—are small, and are not significant at conventional levels.

Although random assignment was successful, there is unfortunately a very imperfect match between assignment to a study group and receipt of BDH transfers. Program take-up among lottery winners was 78 percent; lack of information, the cost of traveling to a bank, and stigma may all have discouraged some households from receiving transfers. More worryingly, 42 percent of households assigned to the control group received transfers. The precise reasons for this substantial contamination are unclear. Conversations with BDH administrators suggest that the list of households that had been randomized out was not passed on in time to operational staff activating households for transfers. This situation was corrected after a few weeks, but withholding transfers from households that had already begun to receive them was judged to be politically imprudent (see Schady and Araujo 2006).

The lower panel of Table 1 shows that there are clear baseline differences between households that received BDH transfers and those that did not. Households that received transfers had lower food expenditures and lower food shares; they tended to be larger; the heads of households that received transfers had an average of two-thirds of a year of more education, are approximately two years younger, and are somewhat more likely to be male. Many of these differences are significant. Table 1 clearly suggests that the likelihood of receiving BDH cannot be treated as random, and that it is important to use estimation methods that take this into account.

6
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Crafts, Nicholas. 2005. "Interpreting Ireland's Economic Growth." Industrial Development Report 2005: Background Paper Series. Vienna, Austria: UNIDO. de Ferranti, David, Guillermo E. Perry, Indermit S. Gill, J. Luis Guasch, William F. Maloney, Carolina Sanchez-Paramo, and Norbert Schady 2003. _Closing the Gap in Education and Technology_ . Washington, DC: World Bank.

Deichmann, U., Kai Kaiser, Somik Lall, and Zmarak Shalizi. 2005. "Agglomeration, Transport, and Regional Development in Indonesia." World Bank Policy Research Working Paper 3477. Wsahington, D.C.: World Bank.

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19