rafmacalaba/datause-encoder-data huggingface dataset:
(g) "other" (includes broad unspecified evaluation studies of projects,<sup>women</sup> in educadion, subsector linkages in education and intersectoral<sup>linkages, such as</sup> education and rural health).

10. These categories were established on the basis of a<sup>careful review of project</sup> description. Theyv reflect the most salient feature of each study,<sup>and attempt to reflect</sup> mutually exclusive categories. However, classification of<sup>study by type was difficult, and</sup> it is possiDle that a study could fit more than one classification.<sup>**1**Therefore, care must</sup> be taken in interpreting the results of this exercise.

# Resources for Research

11. Budgeted expenditures (loan commitments) on research<sup>components were taken</sup> from cost tables in SARs or were estimated from planned<sup>manmonths of technical</sup> assistance for research. Loan commitments could not<sup>be calculated directly for 10</sup> projects. In these cases, were cstimated as a percentage<sup>of the total cost of the project,</sup> which percentage was derived from a weighted average<sup>of the sub-region percentage</sup> loan commitments on research. All loan commitments<sup>are expressed in current dollar</sup> terms.

# III. Lending for Education Research

# Volume of Research

12. Planned, completed and available studies. Between<sup>1982-1989, a total of 146</sup> education projects were appraised; of these, 116 included<sup>research components, which in</sup> turn included 436 planned studies. Of these, 184 (42%)<sup>;had been -n.mpleted at the time</sup> of this review.<sup>12</sup> Documents were identified for 84 of<sup>the 184 comn. 3d studies. By</sup> comparison, Tan (1982) found 272 planned studies,<sup>1972-825 of which 73 (27%) were</sup> completed and 55 had available documents. Of 104 studies<sup>incomplete at the time of</sup> Tan's study. 59 were completed after 1982, and 30 had<sup>available document.</sup> 13. Table 1 breaks down the number of planned studies into<sup>two periods, from 1972-</sup> 82 and from 1982-89, and indicates the number<sup>of studies completed in those time</sup> periods. There was a rapid absolute increase in the number<sup>of planned studies</sup> incorporated in education projects in the period after<sup>1982. The average number of</sup> studies increased from 1.6 per project in 1972-1982 to a.7<sup>per project in 1982-1989, and</sup> I I Classification **was** comnpleted by one **researcher** only, with<sup>no</sup><sup>**atterpt**to achieve inter-rater reliability</sup><sup>**among**multiple</sup> **raters.**

> 12 Of these 28, **(15%) had** produced first drafts of **reporte**<sup>**only,**but</sup><sup>**were**considered</sup><sup>**complete,**or vere to be pursued undoi</sup><sup>**a**</sup> continuing project. These have been included<sup>in the cost eetirntes for the period 1982-1990,since it</sup><sup>**seems**unlikely that</sup><sup>**further**work</sup> will be undertaken within the assigned project.<sup>See, for exanuple, Guinea CR</sup><sup>**1341; Malawi**CR 1767; Rwanda CR</sup><sup>**1683;**NepalCR</sup> 1198; Jordan LN 2633.

4
# _2. Are the lessons from India and China generalizable?_

Much of the literature focuses on successful examples from China and India. Broader comparative studies, particularly of a quantitative nature, could provide additional insights. For instance, Lewis notes the very different strategies used by India and China. Additional work to better understand which strategies are likely to be most successful are needed. Moreover, understanding where these strategies work is important. China offers multinational investors the opportunity to access a market of one billion people. Thus, firms may be willing to accept restrictions on technology transfer to enter the Chinese market that they would not accept to enter smaller markets. Smaller countries may face additional hurdles when attracting technology transfer. Should such countries focus their attention elsewhere? Could a group of smaller countries form partnerships to increase their bargaining power with multinational firms? Moreover, the earlier concerns about technological distance suggest that further study should address whether policy needs may be different for countries at different stages of development. Whereas countries such as India and China may have the absorptive capacity to benefit from the spillovers provided via technology transfer, countries with a greater technological distance may find using technology transfer and adaptive R&D less valuable, either because they lack the skills necessary to adapt the technology or because even an adapted technology wouldn’t be appropriate for their market. More comparative studies would thus be of great value to policy makers interested in promoting green technology transfer.

# _B. Diffusion within Countries_

The above studies focus on the flow of knowledge across countries. Also important is the flow of knowledge within countries. The diffusion of a new technology is a gradual, dynamic 28
32 sttui undelirAn tho centrality of compttib'e macroeconomic policies, inuding a realistic exchange raze, coensUS building, administrative reform and early attention to the social impact of reform.

Despite many structural limitations and constraints, there now exists growing evidence that even in SSA, trade liberalization and greater openness have had a _significant_ and positive impact on growth and other indicators of performance.<sup>32</sup> This conclusion is reached both by' comparing the performance of the adjusting countries for the period before and after the adjustment process as well as by comparing 'on-adjusting countries with adjusting countries. The cae studies included-here confirm this general concluson- They suggest that even in countries with the worst implementation record some positive results have been obtained.

However, it is essential to go beyond,the usual analysis of the impact of reform in order to .plore further the nature-of the response to policy changes and the likelihood of their future sustalnability. Although conviction and commitment to reform on the part of policy makers always remains a fundamental condition for the continuation of trade reform, positive response to teform and structural changes in other areas of the economy that sustain the positive response are equally essential in order to enhance commitment and preempt trade reform reversal. Therefore, the analysis here is focused on some indicators of structural change and response that guarantees the sustainability and deepening of trade liberalization. Three fundamental indicators are discussed below. Although they are by no means exhaustive, they are deemed the most essential. These include: a. The level and composition of public expenditure and public revenues and the evolution of the government budget deficit.

b. The share of private and public fixed capital fornation in GDP and the sectoral composition of the investment.

> 3 2 Among the most recent literature see Matin (1992a) and EEC (1992).
12 the relative prices in TR prevailing in 1988, i.e., that Hungary would be able to convert its TR surpluses with the Soviet Union into imports from the Soviet Union at the TR prices prevailing in 1988. In that case the TR prices that prevailed in 1988 are the relevant prices for defining the price index in 1988, and the estimated dollar prices are relevant for defining the price indices after the switch-over. We show in the appendix that based on 1988 initial data, the change in the bilateral commodity terms-of-trade is the ratio of the weighted average export relative price (.864) to the weighted average import relative price (1.134) or 0.762. That is, the terms-of-trade would shift adversely for Hungary by almost 24 percent.

The other extreme is to assume that the TR surpluses were valueless. In this case the TR prices are not the relevant prices with which to construct the price indices, because they are not the prices at which Hungary can import. The appropriate TR prices would be the counterfactually created TR price vectors where export prices are reduced and import prices are increased equiproportionately such that there is no trade surplus in TR. This means that the true terms-of-trade under the protocols in 1988 are worse than the termsof-trade based on unadjusted TR prices; consequently the switch-over would cause less of an adverse effect on the terms-of-trade. Making this adjustment, it is necessary to decrease TR export prices and increase import prices in TR by 4.5 percent in order to create a zero trade balance in TR with fixed quantities. Then the change in the terms-of-trade is reduced to 17 percent. This is obtained from the ratio of the counterfactually created export relative price index of .901 to the import relative price index of 1.083, which is .832.

Thus, we estimate that had the switch-over occurred in 1988, the bilateral commodity terms-of-trade with the Soviet Union would have moved adversely against
Table 8: Impact on Schooling Outcomes

||Enr|olled in Sch<br>(Yes = 1)|ool|Hours per<br>studyin<br>(conditi|day spent in<br>g last week<br>onal on enr|school or<br>(0-24)<br>olment)|Aspirat<br>univ|ion to stud<br>ersity (Yes|y up to<br>= 1)|
|---|---|---|---|---|---|---|---|---|---|
||All<br>(1)|Boys<br>(2)|Girls<br>(3)|All<br>(4)|Boys<br>(5)|Girls<br>(6)|All<br>(7)|Boys<br>(8)|Girls<br>(9)|
|Panel A: Endline||||||||||
|CHOICES|-0.004|0.017|-0.024|0.108|0.133|0.054|0.025*|0.021|0.029*|
||(0.02)|(0.02)|(0.03)|(0.14)|(0.18)|(0.24)|(0.01)|(0.02)|(0.02)|
||[1.00]|[0.89]|[0.69]|[0.83]|[0.83]|[1.00]|[0.09]|[0.34]|[0.08]|
|EMB (marginal)|0.008|-0.025|0.042*|0.150|0.157|0.180|-0.004|-0.005|-0.003|
||(0.02)|(0.03)|(0.02)|(0.15)|(0.24)|(0.21)|(0.02)|(0.02)|(0.02)|
||[1.00]|[0.67]|[0.08]|[0.49]|[1.00]|[0.64]|[1.00]|[1.00]|[1.00]|
|Control group mean|0.86|0.88|0.84|6.63|6.92|6.35|0.90|0.91|0.90|
|Adjusted R<sup>2</sup>|0.07|0.03|0.11|0.03|0.03|0.02|0.01|0.02|0.00|
|Observations|<br>2,643|1,285|1,358|2,643|1,285|1,358|2,506|1,229|1,277|
|Panel B: Midline||||||||||
|CHOICES|0.005|-0.016|0.024|-0.069|-0.096|-0.041|-0.014|-0.041|0.010|
||(0.02)|(0.03)|(0.03)|(0.16)|(0.24)|(0.21)|(0.02)|(0.03)|(0.03)|
||[1.00]|[1.00]|[0.71]|[1.00]|[1.00]|[1.00]|[1.00]|[0.15]|[1.00]|
|EMB (marginal)|0.003|0.011|0.003|0.022|-0.056|0.165|0.034|0.041|0.029|
||(0.02)|(0.02)|(0.03)|(0.11)|(0.22)|(0.16)|(0.02)|(0.03)|(0.03)|
||[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|[0.48]|[0.14]|[0.21]|[0.42]|
|Control group mean|0.83|0.87|0.79|6.33|6.81|5.89|0.87|0.89|0.85|
|Adjusted R<sup>2</sup>|0.21|0.14|0.27|0.14|0.10|0.17|0.07|0.07|0.06|
|<br>Observations|<br>1,987|939|1,048|1,987|939|1,048|1,886|908|978|
|ANCOVA|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Notes: * significant at 10% lev|el ** significa|nt at 5% level|*** significan|t at 1% level||||||

(1) CHOICES=1 if adolescent was assigned to CHOICES only or CHOICES+EMB treatment groups. EMB=1 if adolescent was assigned to CHOICES+EMB group, effectively giving the marginal impact of the EMB program in addition to the CHOICES program.

(2) ANCOVA estimation controls for the level of the outcome variable. All regressions include district fixed effects and a set of controls measured at baseline: dummy for whether household has multiple adolescents and age of respondent.

(3) Standard errors are clustered at the community level and are reported under the coefficient in parentheses. Sharpened q-values that correct p- values for the false discovery rate (FDR) are in square brackets. A sharpened q-value of 1.00 indicates the null hypothesis is not rejected at any level of FDR.

37
heterogeneity however emerges across countries. While the gravity results suggest that TAs have partly raised cross-border government procurement, the CHB values remind us that government procurement remains vastly home biased, even if the trends show a slow opening up to trade.

# **6 Concluding remarks**

This paper estimates trade barriers in government procurement. In doing so, it analyses the role of trade agreements to see if and how much they have contributed to reduce those barriers.

Using new Inter-Country Input-Output (ICIO) tables from the TiVA database for 62 countries between 1995 and 2015, we obtain estimates of government purchases across sectors and countries of origin and use those in a standard gravity model. The estimates suggest that governments are significantly more local in their purchasing decisions than private firms. The border effect is large and significantly higher for public markets, especially in services. Yet, we find that trade agreements and the EU single market in particular have contributed to the opening up of government procurement, with the effect being robust in services. Non-discriminatory provisions specific to government procurement are driving the trade-creating effect of TAs in services, whereas the effects are overall weak in goods.

The estimates from the gravity model are then used to estimate indexes of home bias in government procurement and private markets. These measures capture the overall tendency to trade locally rather than internationally, and hence include also factors other than protectionist trade policies. Home bias is larger in government procurement than in private markets. Two mechanisms can account for this difference: (i) governments being on average more home biased than firms in purchases of both services and goods; and (ii) governments spending relatively more on sectors (goods and services) that are more home biased. We find that this difference is however shrinking over time: home bias in government procurement is declining faster than home bias in private markets.

Our results have implications for trade negotiations that touch upon government procurement issues. A strong result of our analysis is that national borders are relatively thicker in services, which absorb the lion share of public spending. Furthermore, we find that policy initiatives targeted to government procurement have been able to reduce trade 33
Also, the fact that we are dealing with count data that might be correlated with previous years (hierarchy at _t_ as function of hierarchy in _t-1_ ) demands additional cautions regarding non-linearity and dynamics.<sup>29</sup> We showed that firms’ organization varies even after controlling for size (firms with the same number of workers have different number of layers).<sup>30</sup> Nonetheless, changes in hierarchy and size (measured by total number of workers) might be simultaneously determined which might result in cov( _Xit_ , _εit_ ) = 0. This might be a second source of endogeneity that may be an issue for identifying _τ_ if cov( _Xit_ , _Disrt_ ) = 0.

Regarding the count data properties of the dependent variable, we applied a logarithmic transformation of Y (number of layers) keeping the full number of layers of the firm from 1 to 5. This transformation allowed us to estimate _τ_ using a fixed-effect instrumental variable approach in order to control for timeinvariant firms’ characteristics. In order to check if the logarithmic transformation provides a reasonable approximation to deal with non-linearities, we compared the coefficients of a Poisson estimator and a standard panel fixed effects.<sup>31</sup> For the second source of endogeneity we instrumented the regressors ( _Xit_ ) that could be simultaneously determined with _Yisrt_ using their own variables with 2 lags.<sup>32</sup> The main assumption is that ( _Xit−_ 2 , _εit_ ) = 0, once we controlled for firms’ time-constant heterogeneity.

We complement the analysis by adopting a panel fixed effects procedure without instrumenting _Dit_ and using a similar procedure to deal with potential endogeneity in _Xit_ . We compared the results controlling for potential regional (state-year) and sectoral-year shocks. In this case the identification is based on a difference-in-difference (DID) approach, which assumes that there is no time-invariant unobservable that determines the access to the program and firms’ organization simultaneously. The specification is the following: Where: _Yisrt_ is the outcome of interest (number of layers based on knowledge); _αi_ is a firm fixed effects intercept; _σst_ is a time-sector fixed effects; _ηrt_ is a time-region fixed effects; _Dit_ is Peiex’s treatment status at the firm-level; _Xit_ is a vector with firms’ control covariates and _εisrt_ is the error term. The next section presents the empirical results.

> 29Observations in count data can take only non-negative integer values and these integers results from counting rather than ranking.

> 30We are interested in analyzing the impact of the program on firms’ organization conditional on firms’ size.

> 31See Cameron and Trivedi (1999).

> 32The covariates include number of employees, average employee’s wage, average years of employee’s schooling, share of engineers and R&D workers, average experience of employees, management quality based on the wage’s distribution for the top layers, new managers hired with former experience in exporting firms and number of subsidiaries.

14
|IntegratingEnvironment and Forest Protection into the Recovery|anTimothyH. Brown|
|---|---|
|IntegratingEnvironment and Forest Protection into the Recovery|anTimothyH. Brown|
|IntegratingEnvironment and Forest Protection into the Recovery|anTimothyH. Brown|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Huai River Basin Flood Management and Drainage Improvement|XimingZhang|
|Productive SafetyNets APL II|Wolter Soer|
|Productive SafetyNets APL II|Wolter Soer|
|Productive SafetyNets APL II|Wolter Soer|
|Productive SafetyNets APL II|Wolter Soer|
|Productive SafetyNets APL II|Wolter Soer|
|Productive SafetyNets APL II|Wolter Soer|
|Productive SafetyNets APL II|Wolter Soer|
|Sustainable Development of Natural Resources|Michael C. Stanley|
|Sustainable Development of Natural Resources|Michael C. Stanley|
|Sustainable Development of Natural Resources|Michael C. Stanley|
|Sustainable Development of Natural Resources|Michael C. Stanley|
|Sustainable Development of Natural Resources|Michael C. Stanley|
|Sustainable Development of Natural Resources|Michael C. Stanley|
|Sustainable Development of Natural Resources|Michael C. Stanley|
|Sustainable Development of Natural Resources|Michael C. Stanley|
|Sustainable Development of Natural Resources|Michael C. Stanley|
|Third PovertyReduction StrategyGrant|Agnes L. B. Soucat|
|Third PovertyReduction StrategyGrant|Agnes L. B. Soucat|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
|Tourism Development|Hannah R. Messerli|
labor force, total number of international students and total skilled immigrants entering the country are available from the _Statistical Abstract of the United States_ published annually by the U.S. Census Bureau. The GDP deflator and unemployment rate were taken from _Economic Report of the President_ . Skilled immigrants are defined to include both those coming under H1-B1 visas (both capped and uncapped) and employmentbased immigration. It should be noted that in our data these categories do not include accompanying family members, but just the workers themselves. Simple correlations among the variables in this study are listed in Table 1.

# **4.  Empirical Results**

Regression results for total patent applications and grants as a proportion of labor force are presented in Table 2 for our basic specifications.<sup>19</sup> Note in the first two columns that the share of total graduate students in the labor force had no detectable impact on lagged patent applications or grants. Thus, we also estimate specifications (3) and (4), which exclude this variable. It may be seen that lagged patent stock as a proportion of labor force had a significant and positive impact on patent applications in regressions (1) and (3). However, its impact on patent grants was insignificant in the full specification in regression (2) and marginally significant in regression (3). The elasticity of patent applications with respect to increases in cumulative knowledge is estimated at around 0.53. This result suggests that, _ceteris paribus_ , there is a dynamic spillover from knowledge to the registration of new ideas, confirming the "standing on shoulders" idea.

> 19 The results reported involve five-year lags for patent applications and seven-year lags for patent awards.  We experimented also with different lag structures, which tended to reduce the significance of some coefficients but did not change the results materially.  Results are available on request.

18
36 Table 3. Average effect of the Venezuelan migration shock on educa– tional outcomes All schools (marginal effects)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dependent Var.|Ln(Enrollment)|Promotion Rate|Dropout Rate|Math Std.<br>Score|Language Std.<br>Score|
||**P**|**anel A: Without**|**Controls**|||
|Foreign Males|0.0043***<br>(0.0010)|-0.0244***<br>(0.0090)|0.0146**<br>(0.0067)|0.0017<br>(0.0012)|0.0040***<br>(0.0012)|
|Native Males|0.0005|-0.0228**|0.0146**|-0.0000|0.0003|
||(0.0009)|(0.0091)|(0.0069)|(0.0006)|(0.0005)|
|Foreign Females|0.0040***|-0.0135|0.0122*|0.0014|0.0016*|
||(0.0010)|(0.0095)|(0.0068)|(0.0013)|(0.0009)|
|Native Females|0.0004|-0.0199**|0.0162**|0.0003|0.0005|
||(0.0009)|(0.0090)|(0.0070)|(0.0004)|(0.0004)|
|R-Squared|0.8739|0.2568|0.2102|0.7949|0.7786|
|||**Panel B: With C**|**ontrols**|||
|Foreign Males|0.0041***|-0.0235***|0.0135**|0.0016|0.0039***|
||(0.0010)|(0.0088)|(0.0066)|(0.0012)|(0.0012)|
|Native Males|0.0003|-0.0219**|0.0135*|-0.0001|0.0002|
||(0.0008)|(0.0090)|(0.0069)|(0.0006)|(0.0005)|
|Foreign Females|0.0037***|-0.0126|0.0111*|0.0013|0.0015|
||(0.0010)|(0.0094)|(0.0067)|(0.0013)|(0.0010)|
|Native Females|0.0002|-0.0190**|0.0150**|0.0002|0.0004|
||(0.0008)|(0.0088)|(0.0070)|(0.0003)|(0.0004)|
|R-Squared|0.8740|0.2573|0.2106|0.7951|0.7788|
|Native Males Mean|4.431|93.64|3.334|0.268|0.0731|
|Native Males SD|1.671|10.71|8.673|1.004|0.977|
|Native Females Mean|4.354|95.25|2.800|-0.102|0.0900|
|Native Females SD|1.707|9.383|8.178|0.907|0.964|
|Foreign Males Mean|1.101|90.79|6.241|0.811|0.511|
|Foreign Males SD|1.120|22.49|19.12|1.852|1.764|
|Foreign Females Mean|1.082|92.10|5.834|0.236|0.406|
|Foreign Females SD|1.104|21.18|18.71|1.697|1.736|
|Observations|334,160|334,160|334,160|120,478|120,478|

Clustered standard errors by municipality in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All columns include School FE, Year FE and Department _×_ Year FE. Selected controls include the following variables interacted with year dummies: N. of Terrorist Attacks _×_ 2012, 2014 and 2016, Informal Labor _×_ 2018, UBN _×_ 2013.