rafmacalaba/datause-encoder-data huggingface dataset:
assume that the barrier will only be cut by 15 percentage points, since pressure from the Working Party in these sectors is not strong.

# **Share of expatriate labor employed by multinational service providers.** In section 2

we explained that imported foreign primary inputs in services production by multinationals are partial equilibrium substitutes, but could be general equilibrium complements. In our applied model, the impact of liberalization of barriers to foreign direct investment in business services sectors on the demand for labor in these sectors will depend importantly on the share of expatriate labor used by multinational firms. We obtained estimates of the share of expatriate labor or specialized technology that is used by multinational service providers in Russia from Russian research institutes that specialize in these sectors. In general, we found that multinational service providers use mostly Russian primary factor inputs and only small amounts of expatriate labor or specialized technology. In particular, the estimated share of foreign inputs used by multinationals in Russia is: telecommunications, 10% plus or minus 2%; financial services, 3%, plus or minus 2%; maritime transportation, 3%, plus or minus 2%; and air transportation, 12.5%, plus or minus 2.5%.

**Tariff data** .  For about 1700 tariff lines, Russia employs a ―mixed system‖ in which the maximum of the ad valorem or specific tariff applies. Due to acquisition of trade flow data at the tariff line level, the actual tariffs were first calculated precisely by Shepotylo and Tarr (2008),

> Russia of multinationals, 10% is the amount by which telecommunications prices are elevated due to the barriers and if we assume Russian domestic service providers prices are unaffected, then we may solve for x from:  sx + (1s)*0 = .10. That is, x= .10/s  Our data indicate that s = .15, then x = .67 or 67%.

> Barriers to foreign direct investment, however, have an indirect effect on the price of Russian telecommunications services. Consequently, sx + (1-s)*y = .10 may be more appropriate, where y is the amount by which the price of Russian telecommunication services are increased in the benchmark as a result of barriers on multinational telecommunications service providers. The value of y would have to be less than the value of the increase in composite services (0.1). It is likely that the indirect effect of barriers to foreign direct investment on the price of domestic Russian telecommunications services is less than 0.05, since the composite price increased by only 0.1 and lower values of y yield higher estimates of x. But if we take y=.05, then x equals 0.38, which is approximately the value estimated for financial services, of 0.33. We take a conservative estimate here of 0.33 for telecommunications.

46
(compared with non-recipients), and impacts on cognitive outcomes (as measured by a math test and a test of working memory) only for those receiving merit-based scholarship (Barrera-Osorio and Filmer 2016). In this paper we report results from data collected in 2016—nine years after the beginning of the program—from a subsample of the original study participants. We present evidence of the effects of the scholarship on a range of long-term outcomes spanning cognitive skills, socioemotional outcomes, socio-economic and well-being outcomes, and labor market outcomes. The analysis presents causal evidence to address three questions. First, what are the long-term effects of the program on cognitive skills and socioemotional outcomes? Specifically, we investigate the impacts of (exogenously induced) additional exposure to schooling on these outcomes. Heckman and Kautz (2014) show that socioemotional skills (which are also sometimes referred to as “non-cognitive” skills) are important determinants of labor outcomes in the long-term. Second, what are the long-term effects of the scholarships on socioemotional outcomes? In particular, we investigate whether socioemotional outcomes are co-produced with (or are complements to) cognitive outcomes. We can pursue the answer to this question because only the merit-based scholarship induced changes in cognitive skills after the first three years of the intervention; therefore, we can test whether we observe effects on socioemotional outcomes for this group only, for both treatment groups, or for neither group. Third, what are the long-term effects of the scholarships on well-being and labor market outcomes? Given that scholarships induced more schooling for all treated individuals, but only cognitive skills for some, we can investigate the channels through which this additional education might affect these outcomes. Based on an intent-to-treat model, the results show that, despite some catch-up by the control group between 2011 and 2016, scholarship recipients have on average 0.21-0.29 more years of schooling. This is in line with programs that attempt to reduce direct costs (for example scholarships; see Kremer et al. (2009), and Duflo Based on an intent-to-treat model, the results show that, despite some catch-up by the control group between 2011 and 2016, scholarship recipients have on average 0.21-0.29 more years of schooling. This is in line with programs that attempt to reduce direct costs (for example scholarships; see Kremer et al. (2009), and Duflo et al. (2017)) and indirect costs of education (for example conditional cash transfers; see Fiszbein and Schady (2009)).

We find positive effects on measures of cognitive skills, but only for the meritbased approach to targeting. Impacts of the merit scholarships on a “family index”— 3
# **Tables**

Table 1: Decomposing variance in percentage changes in synthetic benefits per population, Farm Bill and ARRA

|||(1)|||(2)||
|---|---|---|---|---|---|---|
|VARIABLES|% change in IV, FB|Covariance share|R2 share|% change in IV, ARRA|Covariance share|R2 share|
|Log average gross income|7.321***<br>(0.954)|0.268|0.166|6.412***<br>(0.785)|-0.0915|0.167|
|Log average rent|-0.925**<br>(0.456)|-0.0459|0.043|-1.409***<br>(0.392)|0.160|0.098|
|Log average household size|4.758<br>(2.925)|-0.0812|0.058|2.666<br>(1.736)|0.0806|0.068|
|Share elderly/disabled|6.222***<br>(1.960)|0.0811|0.052|0.479<br>(1.442)|-0.00265|0.015|
|Share homeless|16.63|-0.00388|0.008|1.655|-0.00183|0.009|
||(10.61)|||(7.429)|||
|Log SUA|-1.849***|-0.0520|0.029|-2.846***|0.712|0.578|
||(0.276)|||(0.247)|||
|% change in SUA|0.179***|0.770|0.644|-0.000765|0.00323|0.065|
||(0.0148)|||(0.00986)|||
|Residual||0.0637|||0.140||
|Observations|48|||48|||
|R-squared|0.936|||0.860|||
|Prob _>_ F|0.000|||0.000|||

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. % change in IV refers to the percentage change in synthetic benefits per population, FB refers to the Farm Bill in 2008m10, ARRA refers to the American Recovery and Reinvestment Act in 2009m4. Covariance share refers to the share of the variance in the outcome explained by each variable. R2 share refers to the share of R-squared explained by each variable following Huettner and Sunder (2012). This measure uses Shapley and Owen values, which calculate the average marginal contribution of each regressor over all possible orderings. All variables are measured in 2006 except for two variables. Log standard utility allowance (Log SUA) is measured the month before the event of interest, and the percentage change in the standard utility allowance (% change in the SUA) is measured at the Farm Bill only because the SUA did not change during the ARRA.

38
_h_ consequence<sup>_s_</sup> 0<sup>will be closer to their individual</sup> _s_ 0 than in the case of poor households. Formally, the difference between the individual and the overall CPI is, from (1) and (2), _h h h_ The larger θ0 , the closer _s_ 0 is to _s_ 0 , and thus the closer the aggregate CPI to _cpi_ In the limit, as θ0 _h_<sup>→</sup> 1 for some _h_ , _s_ 0<sup>→</sup> _s_ 0 _h_ and the overall CPI would be identical to that of household _h_ .

In simpler terms, with heterogeneous consumption baskets the overall CPI tends to mimic the individual price index of the better off. This is what Prais (1958) termed the “plutocratic bias” of the CPI. Moreover, for a given level of total and average consumption expenditure across households, the more disperse their individual consumption levels (i.e. the more unequal society is), the less representative the overall CPI will be of the consumption price index faced by those at the lower end of the distribution – i.e., the larger the plutocratic bias.

Finally notice that the difference between the inflation rate affecting household _h_ and overall CPI inflation is given by This is zero when either (i) all prices change in proportion (i.e., _pt_ ,0 = _k pt_ −0,1 for some scalar _k_ ), or (ii) _s_ 0<sup>_h_</sup> = _s_ 0 , so individual and aggregate CPI shares are the same. As we show below, these two conditions are clearly violated in practice.

# _II.2 Inflation inequality and observed inequality_

As noted earlier, when inflation rates differ across individuals the distributions of nominal and real consumption may follow different paths. Observed changes in standard inequality indices (which are typically computed from cross-sectional nominal income or expenditure data) may offer a misleading picture of the trends in _real_ income or consumption inequality, which are the relevant ones from the welfare perspective. For example, it would be perfectly possible for real inequality increases to be offset by price inequality declines, so that by just observing nominal quantities one may get the mistaken impression that no change in inequality has taken place.

Following Ruiz-Castillo, et al. (2002), let x( _xt_ ) denote some inequality index (which is assumed to be an increasing function in the level of inequality) in period t, where 1 _H_ ' 1 ' _H xt_ = ( _xt_ ,...., _xt_ )'= ( _pt ct_ ,...., _pt ct_ )' . Note that x( _xt_ ) measures the inequality of nominal ' 1 ' _H_ quantities. Further, let _xt_ , _s_ ≡ ( _pt cs_ ,...., _pt cs_ )' , i.e. the vector of household consumptions at _s h h h_ evaluated at the prices of period _t_ (from (1) this implies _xt_ ,0<sup>=</sup> _x_ ,00 _cpit_ ). Then we can write: 4
comprehensive review for modeling money demand in Egypt. These studies are reviewed in detail and summarized in Annex VIII.

Scobie (1983) is one of the early empirical studies on estimating money demand in Egypt. It was conducted to examine the monetary impact of subsidy expenditures on domestic inflation, the balance of payment, and the exchange rate. These variables were determined simultaneously within a framework of the monetary approach of the balance of payment. The demand for real money balances was estimated within a system of four structural equations, including domestic inflation, the black market exchange rate, and the balance of payments equation by adopting a two stage least squares estimation using annual data for the period 1947–1981. The study concluded that the elasticity of real money demand was estimated to be 0.38 in the short run and 1.42 in the long run. With respect to inflation, it was found to be -0.86 in the short run and -2.71 in the long run.

El-Erian (1987) adopted Ordinary Least Squares (OLS) framework to identify the absolute and relative determinants of currency substitution that took place during the period 1980–1986. He used data related to individuals’ expectations regarding foreign balances. A reduced form of the money demand model was estimated using quarterly data of relative holdings of foreign currency deposits and specifications of the associated impact on the portfolio balance decisions of asset holders. Empirical results indicated that agents tend to hold foreign balances for two main reasons: (1) higher financial yields due to expectations of exchange rate induced capital gains; and (2) greater political stability and fewer institutional changes, especially banking regulations. Results also proved that the impact of enhancing these causal factors to influence agents to limit currency substitution will require a time lag to alter the actual holdings of foreign currency balances.

Alami (1999) tested for a long-run equilibrium relationship (co-integration) between the ratio of foreign currency denominated deposits to total liquidity, the expected rate of change of the exchange rate, and an interest rate differential between domestic and foreign currency deposits. He used quarterly data for Egypt covering the period 1981:IV to 1993:IV and tested for cointegration among the different variables utilizing the Engle-Granger methodology (Engle and Granger 1987). The co-integration results were further assessed by implementing the Johansen methodology (Johansen and Juselius 1990) to confirm the uniqueness of the co-integrating vector.

13
_Income and consumption index:_ includes the logarithm of monthly household income and consumption. Because these variables are continuous the index is constructed following the methodology outlined in Kling, Liebman and Katz (2007).

_Labor market index:_ includes a dichotomous variable for employment, and the logarithm of monthly wages and weekly hours worked.

Descriptive statistics for each of the outcomes and their components variables are described in Table 1.

# **III.B Data for the Taliban’s Geographical Presence**

To obtain information on the Taliban’s we online data geographical presence, scraped from the Foundation for Defense of Democracies’ Long War Journal on the districts controlled by Taliban, by U.S. Allies, and contested territories between the two groups.<sup>9</sup> The data includes a yearly evolution of the territorial control in Afghanistan. The classification is based on open-source information from NATO’s data in Afghanistan, press reports, information provided by government agencies, and the Taliban. To categorize the districts, they identify who provides government services, ensures security within the district, administers the district openly, and oversees local courts (see Appendix B.B for a detail description of the variable’s construction).<sup>10</sup> Figures 3 to 5 report province-yearly changes in the share of districts controlled by the Taliban, the U.S. allies, and the contested districts in dispute between the Taliban and the U.S. allies.<sup>11</sup> As seen in the figures, Taliban control was relatively stable from 2017 through 2019, after which drastic shifts took place across regions, in parallel with the U.S. withdrawal from Afghanistan.

> 9The Foundation for Defense of Democracies is Washington, DC-based nonpartisan research institute focusing on national security and foreign policy. It aims to strengthen US national security.

> 10The LJW data aligns with the territorial control analyses conducted by SIGAR and U.S. ForcesAfghanistan, which effectively tracks Taliban-controlled and contested districts by evaluating 5 indicators of stability, including governance, security, infrastructure, economy, and communications (Roggio 2017). 11More granular data is illustrated in Figures A.5 and A.6, which depict the district-yearly geographical distribution of Taliban controlled, U.S. allies controlled, and contested districts in Afghanistan.

17
Wallerstein, Immanuel. The Modem World-System. Vol. 3, The Second Era of Great Expansion of the Capitalist World-Economy 1730-1840s. New York: Academic Press, 1989.

World Bank (200 1a). Global Economic Prospects and the Developing Countries 2001. Washington: IBRD/World Bank, 2001.

World Bank (200 lb). World Development Indicators 2001. Washington, DC: IBRD/World Bank, 2001.

World Bank (2001 c). World Development Report 2000/2001: Attacking Poverty. New York: Oxford Universityt Press, 2001.

World Bank. World Development Indicators 2002. Washington, DC: IBRD/World Bank, 2002.

Yarnamura, Kozo, and T'etsuo Kamiki. "Silver Mines and Sung Coins - A Monetary History of Medieval and Modem Japan in International Perspective." Precious Metals in the Late Medieval and E arly Modem Worlds. Ed. J. F. Richards. Durham, N.C.: Carolina Academic Press, 1983.

Yamazawa, Ippei. "APEC's Economic and Technical Cooperation: Evolution and Tasks Ahead." Whither APEC? The Progress to Date and Agrenda for the Future. Special Report 9. Ed. C. Fred Bergsten. Washington, DC: Institute for International Economics, October 1997. 135-149.

Yamazawa, Ippei and Shujiro Urata. "Trade and investment liberalization and facilitation." Asia Pacific Economic Cooperation [APEC]: Challenges and tasks for the twenty-first century. Ed. Ippei Yamazawa. London: Routledge, 2000. 57-97.

Yamazawa, Ippei and Yozo Yamamoto. Boeki to Kokusai Shoji (Foreign Trade and Balance of Payments). LETS Vol. 14. Tokyo: Toyo Keizai Shimposha, 1979.

Yukizawa, Kenzo and Maeda Shozo. Nihon Boeki no Choki Tokei [Japanese Trade Statistics Rea£gregated by Commodity and by Basic and Major Region.] Kyoto: Dohosha, 1978.

81
adaptive social protection 30 distributed to all Sri Lankan households via a flat tax on income, and we assume that there is no transaction cost to run the program.

<!-- Start of picture text --> 1 month-equivalent Samurdhi benefit<br>payment to Rathnapura Benefit: 0.82M cost = 4.17<br>0.8 after 50-year flood<br>Perfect targeting<br>0.6<br>benefit<br>cost = 3.12 Benefit: 0.48M<br>0.4<br>0.2 Cost: 0.2M<br>Cost: 0.15M<br>0.0<br>Scaleup to affected Scaleout to affected<br>Samurdhi enrollees using PMT<br>(3.6% of district (4.6% of district<br>are beneficiaries) are beneficiaries)<br>Cost, benefit of post-disaster support [mil. USD]<br><!-- End of picture text -->

**Figure 10:** Cost-benefit analysis of scale-up using Samurdhi enrollment, compared to scaleout using PMT (upper threshold = 887) in response to a 50-year precipitation flooding event in Rathnapura. Perfect targeting is assumed in both cases.

In response to the 50-year flood in Rathnapura, the Samurdhi scaleup disburses US$1.2 million to US$150,000 in post-disaster support, depending on how accurately disaster-affected households can be identified among all Samurdhi enrollees in the district (cf. Figure 9). At the high end of this cost range, a payout to every household in the district that is enrolled in Samurdhi (i.e., without any targeting of affected households) reduces wellbeing losses by US$3.2 million, delivering a benefit-to-cost ratio (BCR) of 2.67 on the investment in post-disaster support.<sup>9</sup> At the other end of the estimated range, assuming it would be possible to deliver aid strictly to af-

> 9 Note that the benefits calculated here include the wellbeing benefits of non-affected people who receive post-disaster support; the assessment is not limited to affected populations.
# **VI. College entry and EBTANAS score**

# **Does attendance at SMK affect levels of participation in tertiary education, and how?**

In Table 4 the results of college entry probit were presented. To correct for data censoring, two excluded variables that best predict the likelihood of participation in tertiary education are the level of parent’s educational attainment (or, in the absence of parents in the household, the household head’s level of educational attainment) and the total EBTANAS<sup>4</sup> score achieved at the conclusion of junior secondary education. Household income also has a significant effect in predicting the likelihood of participation in tertiary education of boys, but not of girls.

Some of the results of college entry probit estimate may have potentially important policy implications. One finding is that, controlled for the variables mentioned above, together with the other factors related to household and personal background, attendance at SMK appears to have a significant negative effect on the level of participation in tertiary education.

However, before reaching such conclusions, it is necessary to answer two questions. First, it must be determined whether some unobserved characteristics affect both the choice of attendance at SMK or SMA choice and the level of participation in tertiary education simultaneously. For example, there might be varying innate abilities in terms of obtaining vocational skills compared to academic attainment. These differentiated abilities can only be known to the individual or households. In this case, the estimates of the effect of SMK on participation in tertiary education could be biased. In other words, the results may simply demonstrate that there is a _correlation_ between the choice between the varying types of high school and the level of participation in tertiary education, but not a _causal effect_ .

To explore the net effect of attendance at SMK on levels of participation in tertiary education, the SMK’s effect on the learning process is examined. One unique area of information that IFLS has is the national examination results of individuals. T-test results show that SMK entrants have significantly lower final junior secondary school examination

> 4 National examinations administered at the end of junior and senior secondary cycles.

19
employment questions in the U.S. Current Population Survey (CPS) to investigate the concern that irregular, unpaid, and marginal activities may be underreported partly because people do not think of themselves as working. One of the few studies on this topic has been the Respondent Debriefing Study, carried out by the US Census Bureau in 1988 through the use of vignettes. Respondents were asked to classify hypothetical situations in terms of their own understanding of labor force concepts of “work”, “job”, “business”, and so on. The results were quite revealing. Generally, the majority of respondents were able to classify the situations presented consistently with definitions of the CPS. However, for each vignette, large minorities of respondents gave incorrect answers – for example, 38 percent of the respondents included non-work activities under the “work” classification (Campanelli, Rothgeb, and Martin, 1989).<sup>5</sup> An experiment carried out in 1991 to assess the revision of the CPS questionnaire using vignettes and direct screening questions for unreported work confirmed that questionnaire wording and sequence of questions affect the respondent’s interpretation of work and, therefore, the employment statistics (Martin and Polivka, 1995). The employment categories most at risk of misreporting were work in the family business or farm, casual employment, and work for commission or work compensated by other means than regular salary/wage payments. Moreover, the use of direct screening questions<sup>6</sup> was found particularly useful to detect

> 5 For a discussion of several methodological tools, including hypothetical vignettes and direct screening questions, used to provide diagnostic information to evaluate the effect of questionnaire revisions on reporting of work activities see Esposito et al. (1991).

> 6 In the 1991 CPS experiment direct screening questions were asked about individuals who were reported as non-working in the initial employment questions. With specific reference to the individual originally classified as non-working, the question was “In addition to people who have regular jobs, we are also interested in people who may work only a few hours per week. Last week did (NAME) do any work at all even for as little as one hour?”

6