rafmacalaba/datause-encoder-data huggingface dataset:
4 anticipated marriage play in family decisions to remove girls from schooling, particularly in traditional cultures that question the effect of education on the subsequent roles and behaviors of females.W/ Governments in such countries are less willing to invest in educational establishments that are exclusively for young women, except in countries such as Saudi Arabia, where coeducation is culturally unacceptable.!/ Moreover, because of ambivalence about the importance of educating girls, the actual treatment of females in schools in some developing countries has been found to be inferior.!/ While single-sex secondary education could be seen as facilitating female achievement and promoting greater female participation in schooling where the physical safety of adolescents is an issue, the evaluation of the relative effectiveness of coeducation compared to single-sex schooling may be confounded by the fact that more males than fox les are in school in the first place in developing countries.

Single-sex education may be seen both as an economic and a social issue, in addition to the relatively straightforward question of differential educational effectiveness. A movement away from single-sex education at both the secondary and post-secondary levels, motivated by both social and economic reasons, was experienced in the 1960's and 1970's in the United States. Single-sex education was viewed as a barrier to successful adolescent cross-sex socialization, and the declining demand for single-sex education led to institutions either closing or converting to coeducation in order to stabilize enrollments.2/ This trend occurred at precisely the time that research on American institutions was beginning to document positive effects -- especially for young women -- for single-sex education on students' academic and occupational achievement patterns, self- image, ano career
been partly offset by the simultaneous increase in the oil price. As a result, poverty increased especially for extremely poor people. The reason, the author suggested, is that the increase of the price of the households’ consumption basket overcomes the income rise experienced by the poorest households.

By simulating the impact of the oil price reduction at both the macro and micro levels, this paper will allow us to map precisely the impact of a terms of trade shock – in this case the drop of the oil price in an oil exporter country – on income distribution and thus on poverty, inequality and shared prosperity.

# Methodology

Evaluating the distributive impact of the oil price shock requires the use of a simulation model for at least two reasons. First, being a recent phenomenon, there are still no micro data to compare the distributions of welfare across households before and after the reduction in oil price. Second, even if data were available, they might include other shocks<sup>4</sup> ; hence, to identify the distributive effect of the terms of trade loss, net of (possible) additional shocks, a counterfactual welfare distribution needs to be generated.<sup>5</sup> To obtain the counterfactual distribution the effect of the shock needs to be analyzed under two different perspectives: the macro‐economic perspective, which provides the impact of the terms of trade variation at the aggregate level; and the micro‐economic perspective, which is required to take into account the heterogeneous effect across households. The macro‐micro simulation model used here follows a top‐ down (from macro to micro‐level) approach, where the macro and micro part of the model are handled separately and then linked through changes in prices and quantity computed with the CGE (Lokshin and Ravallion, 2008; Chen and Ravallion, 2004). The CGE (macro) and the microsimulation modules are briefly described below.

## _The macro CGE model_

The CGE model used in this exercise is fairly standard and only a summary of its main features is described here, as its full detailed documentation can be found in van der Mensbrugghe (2014). Production is modeled using nested CES (Constant Elasticity of Substitution) functions that combine at various levels, with different substitution elasticities, intermediates and primary factors. Households’ consumption demand is derived from maximization of household utility producing a constant‐differences‐in‐elasticity

> 4 The labor market performances and the income distributions of Russian households, for example, are likely to be affected also by the economic sanctions.

> 5 This section relies mostly on previous works from Bourguignon and Bussolo (2013) and Bussolo, De Hoyos and Medvedev (2013) and Bussolo et al. (2013).

6
population have risen together until the 1980s when the proportion of the secondary education population appears to be inversely related to the private rate of return to secondary education.

Estimates such as these have been used extensively in policy fora to argue that more needed to be invested in education, especially basic education. For example, in the campaigns for “Education for All,” analysts have used these rates of return as a call to action. While education is seen by many as a basic human right, these economic arguments are seen as essential to releasing the national resources to invest in schools and universities. In the 2002 Education for All Global Monitoring Report it is stated that “available estimates of rates of return for developing countries consistently show that both private and social returns to primary schooling are higher than at secondary and tertiary levels. Their magnitudes are generally greater than typical returns to capital in other economic sectors” (UNESCO 2002: 34). The Global Campaign for Education (2005: 3) argues that: “education, especially for girls, empowers families to break the cycle of poverty for good. Young women with a primary education are twice as likely to stay safe from AIDS, and their earnings will be 10-20 percent higher for every year of schooling completed. Evidence gathered over 30 years shows that educating women is the single most powerful weapon against malnutrition – more effective even than improving food supply. Without universal primary education, the other Millennium Development Goals – stopping AIDS, halving the number of people living in poverty, ending unnecessary hunger and child death, amongst others – are not going to be achieved.”

# **3. Limits to Applying Cost-Benefit Analysis to Guide Public Investments**

While compelling for advocacy nationally and even globally, the rate of return estimates have proven to be less useful as a guide for setting government spending priorities at the 10
* *

<!-- Start of picture text --> Pollcy Research<br><!-- End of picture text --> <!-- Start of picture text --> Population, Health, and Nutrillon<br><!-- End of picture text --> WPS 1042 This paper-a product of the Population Policy and Advisory Service, Population and Human Resources Department-was prepared for a review of effective family plan ing programs. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Otilia Nadora, room S6065, extension 31091 (November 1992, 36 pages).

Both projects described here, Matadi and Kananga, helped health providers in those two cities offer clinica~ family planning services. But their approaches differed markedly. PRODEF/ Matadi concentrated on pioneering communitybased distribution of contraceptives, with carefully supervised distributors. The Kananga Project emphasized clinical supervision and pleasing the clients; introduced social marketing with loose supervision of retailers; and provided an information team skilled in face-to-face group meetings, plus a weekly radio program.

Four factors common to both projects seemed to contribute to their success:

* The single-minded dedication of 0.aff members to making family planning work.

**o** An uninterrupted supply of affordable contraceptive methods available through outlets at many locations.

* Enough organizational autonomy to be able to respond to problems as they arose. Such autonomy made project personnel identify more with project goals and feel responsible for achieving project objectives.

- Regular and supportive supervision of those responsible for service delivery. Both projects emphasized regular contact with clinic personnel - Matadi also included distributors. These contacts bolstered morale by showing that the project administration was closely following service providers' activities and by transmitting to providers the staff's enthusiasm for project activities. Supervisory visits included administrative functions such as collecting service statistics and controlling inventory, but these activities were handled in a friendly, nonthreatening manner that encouraged service providers to perform their tasks well.

* Adequate funding. Both projects had special funding that allowed them to experiment with approaches for increasing contraceptive prevalence. That fiunding may partly explain their organizaticnial autonomy and may have contributed to the sense of purpose and esprit de corps that developed among project staff. Larger-scale programs in Zaire have operated with significant financial constraints, so it would be unfair to compare them with these more successful projects. Special funding does not guarantee project success but may make it far more likely, conclude Berand and Brown.

'Me Policy ResearchWorking PaperSeries disseoinates the frdings of work under way in theBankr Anobjeiveof the series is to get these findings out quickldy, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent of ficial Bank policy.

Produced by the Policy Research Disserniination Center
considerably less dispersion around this mean during the earlier three crashes (Table 1). The standard deviation of returns during the recent crash was 2.6 percent, similar to the historical average of 2.4 percent, but about half the magnitude of the earlier crashes. In contrast to the other crashes, the inter‐quartile range (2.9 percent) was also much closer to the historical average of 2.3 percent. The proportion of days when prices fell by more than 2 percent was less than the other crashes as well (though greater than the historical average); the number of days when prices rose by more than 2 percent was also less than all three previous crashes and the historical average. Last, a measure of the proportion of ‘stable’ days (i.e. days in which absolute log returns did not exceed 2 percent) was much closer to the historical average than to values associated with the three previous oil price crashes.

To confirm these observations and also identify drivers of volatility during each crash period, we estimate a GARCH specification as discussed earlier, the results of which are reported in Tables 2 and 3. The specification is applied to seven samples: 1985‐ 2015 (full sample), 1985‐2003 (pre‐boom period), 2004‐2015 (post‐boom period), and four 250 day periods ending with the end of each crash.

We draw three conclusions. First, while shocks to volatility have a smaller half‐life during oil price crashes, of the four crashes, the half‐lives are larger (greater than 12 days) for the crashes involving a loss in price support from OPEC (1985/86 and 2014/15) com‐ pared to the crashes engendered by the first Gulf War (1985/86) and financial crisis of 2008/09 (which each exhibited a half‐life of just 2 days).

Second, positive equity market shocks during the three previous crashes were as‐ sociated with greater volatility while this was not the case during the recent crash. Fur‐ ther, negative equity shocks were associated with greater volatility during the crashes of the first Gulf War and financial crisis. For example, while unconditional variance (with no equity shocks) was just 3.5 percent during the 2008/09 crash, the conditional variance <!-- Start of picture text --> 4 —<br><!-- End of picture text --> _— 4 —_
to assess their stances. A is coded as if it policy party left-leaning emphasizes policies such as market regulation, state ownership, and pro-peasant initiatives, whereas rightleaning parties prioritize free-market policies, nationalism, and law-and-order measures. If party statutes are unavailable, we classify candidates based on their government plans submitted to the electoral authority, applying the same policy-based criteria.<sup>15</sup> Candidates from parties that do not fit clearly into the left or right categories are classified as centrist or unaligned.

For presidential elections, we classify candidates running in first-round elections from 2000 to 2022 using the same methodology. Electoral data was obtained from Colombia’s electoral authority, and for the 2022 election, we scraped official results from the registry’s website to compile a complete dataset. A detailed description of the classification proceB. dure is provided in Appendix Descriptive statistics for all variables used in the main specifications are in Table C.1. There is significant variation in the turnout of the mayoral elections (the mean is 65 percent and the standard deviation is 11 percent). The dominance of a center-oriented political ideology is clear: on average, 66 percent of voters supported center parties, while 16 percent supported right-wing parties and only 6 percent supported left-wing candidates. On average, these mayoral elections were competitive (0.63).

. **_Other municipal controls_** We also use several municipal characteristics measured before the 2018 implementation of PEP and interact them with election-year indicator variables to flexibly account for non-parametric trends in observable variables that may bias the main results. Descriptive statistics for all controls are illustrated in Table C.2. The data comes from multiple sources listed in the footnote of Table C.2.

> 15A substantial portion of candidates is classified using individual-level information rather than solely relying on party affiliation: 40% based on their government plans, 20% through their social media posts, and 14% based on coalition composition (see Table A.4). This approach minimizes the risk of over-relying on party ideology and better captures local variations in candidate positioning, particularly in light of the large number of centrist candidates.

18
Mobarak 2014).<sup>6</sup> Nevertheless, our indicator for estimated effects after one year is statistically insignificant and negative, implying noisy, but on average lower average effects on yield after the first year of the subsidy program (table 3, column 4).

Lastly, we examine whether input subsidies may be associated with spillover effects. Among the studies included in our meta-analysis, study 1 (Carter, Laajaj, and Yang 2021) is the only one with estimated spillover effects. We include an indicator for estimated spillover effects and find that while the estimated relationship is positive, it is not statistically significant (table 3, column 5). This is likely due to the fact that only 6.5% of the estimates are spillover effects. Nonetheless, the possibility of spillover in this setting could improve the cost-benefit calculations of input subsidy programs since nonbeneficiaries may adopt the technology without receiving the program. We also include all indicators in one regression and find similar estimated coefficients.

_Table 3 The relationship between program characteristics and estimated effect on yield_

||(1)<br>RCT|(2)<br>100% subsidy|(3)<br>Other interventions|(4)<br>Long term|(5)<br>Spillover|
|---|---|---|---|---|---|
|Program characteristics|-0.020|0.075|-0.143**|-0.093|0.194*|
||(0.053)|(0.054)|(0.051)|(0.096)|(0.099)|
|Observations|32|32|32|32|32|
|Mean indep. var.|0.250|0.625|0.188|0.156|0.062|

Notes: Each column comes from a separate meta-analysis regression. The indicator RCT takes the value one if the study used a randomized control trial. The indicator in other interventions takes the value one if the input subsidy program includes other interventions such as extension service. Long run takes the value one if the effect is estimated after the first year of the program. Spillover takes the value one if the effect is estimated on nonbeneficiaries. Standard error clustered at the study level. Significance: * p<0.10, ** p<0.05, *** p<0.01.

# **3.3 Robustness**

For robustness, we conduct the meta-analysis with several sample restrictions. First, we exclude the long-run effects and spillover effects to focus only on the short-run effects of input subsidy programs, and find an average effect of 19.5%. Second, we drop the effects from study 4 (Ragasa and Mazunda 2018) and study 11 (Ricker-Gilbert and Jayne 2017) where the estimated effects are the increases in yield per kg/ha increase of fertilizer used instead of the effect of program participation. We also exclude effects from study 12 (Abman and Carney 2020) as it is the district-level increase in maize yield in areas with higher subsidy intensity. We find that input subsidy programs lead to an average of 22.8% increase in yield. These estimated effects are similar to our earlier estimated effect of 18%. Third, we use one

> 6 de Mel, McKenzie, and Woodruff (2008) find that the effect of a positive capital shock on profit in 5-8 quarters post treatment is lower than that 1-4 quarters post treatment. Bryan, Chowdhury, and Mobarak (2014) find that the consumption effect of migration one year after the intervention is about 60%-75% as large as the same year effect.

13
3 FOREIGN DIRECT INVESTMENT IN KENYA <!-- Start of picture text --> 2500<br>150 2000<br>Alternative/Renewable energy<br>Automotive Components Automotive OEM<br>Automotive OEM Chemicals<br>100 Communications 1500 Coal, Oil and Natural Gas<br>Consumer Electronics Communications<br>Financial Services Financial Services<br>Food & Tobacco 1000 Metals<br>Industrial Machinery, Equipment & Tools Real Estate<br>50 Metals Transportation<br>500 Warehousing & Storage<br>0 0<br>Industry sector Industry sector<br>(a) Chinese FDI in Kenya by sector (b) FDI by sector<br>Figure 23: China invests most in metals and communications in Kenya (2003-2015)<br>Capital Investment (USD Millions)<br>Capital Investment (USD Millions)<br><!-- End of picture text --> Source: fDi Intelligence from The Financial Times Ltd 2015

**Chinese companies invest the most in manufacturing activities** Chinese firms focus on manufacturing in Kenya, investing US $296.17 million between 2003 and 2015. Manufacturing was 64 percent of total investment for Chinese companies compared to only 24 percent of total investment among all companies. The average capital investment of China’s manufacturing projects was US $49.36 million with a total of six projects; the overall average project size of manufacturing projects was slightly larger at US $58.31 million with 59 projects. Compared to overall trends, Chinese FDI is absent in major sectors: electricity, construction, ICT and internet infrastructure, and logistics, distribution, and transportation. Even though companies have worked on high profile projects, Chinese investment in construction is tiny. The EXIM bank only provides loans: Chinese firms worked on the Standard Gauge Railway and the Thika Superhighway, but the government of Kenya must pay back the debt. Overall, construction had an average project size of US $215.4 million, with five projects in total.

<!-- Start of picture text --> 300<br>3000<br>Business Services<br>200 Construction<br>Business Services Design, Development & Testing<br>Design, Development & Testing 2000 Electricity<br>Education & Training Extraction<br>Headquarters Headquarters<br>Manufacturing ICT & Internet Infrastructure<br>100 Sales, Marketing & Support Logistics, Distribution & Transportation<br>1000 Manufacturing<br>Sales, Marketing & Support<br>0 0<br>Industry Activity Industry Activity<br>(a) Chinese FDI by industry activity (b) FDI by industry activity<br>Figure 24: Chinese companies invest most in manufacturing (2003-2015)<br>Capital Investment (USD Millions) Capital Investment (USD Millions)<br><!-- End of picture text --> Source: fDi Intelligence from The Financial Times Ltd 2015 25
|**Table 2. Client Characteristics**|(1)<br>All<br>Clients|(2)<br>Regular<br>Clients|(3)<br>Casual<br>Clients|(4)<br>_p_-value for<br>regular = casual|
|---|---|---|---|---|
|Occupation|||||
|Truck Driver|0.18|0.18|0.18|0.78|
|Government Employee|0.28|0.29|0.27|0.41|
|Businessman / Salaried Workers|0.20|0.21|0.18|0.32|
|Shop Owner|0.11|0.10|0.14|0.02**|
|<br>Bicycle Taxi Driver|0.07|0.06|0.08|0.19|
|<br>Works in Bar / Restaurant / Hotel|0.13|0.13|0.13|0.72|
|Other|0.04|0.04|0.03|0.41|
|Sex Worker thinks Client is at High Risk of HIV/AIDS<sup>1</sup>|0.38|0.34|0.51|0.40|
|Sex Worker Rates the Client's Wealth as:|||||
|Above average|0.53|0.50|0.62|0.01***|
|About average|0.40|0.43|0.33|0.11|
|Below average|0.06|0.07|0.05|0.07*|
|Sex Worker Rates the Client as:|||||
|Handsome|0.57|0.63|0.44|0.01***|
|About average attractiveness|0.36|0.31|0.48|0.01***|
|Below average attractiveness|0.07|0.06|0.09|0.72|
|Sex Worker Thinks the Clients Has Unprotected Sex:<sup>2</sup>|||||
|More often than average client|0.49|0.49|0.49|0.60|
|About as much as average client|0.13|0.11|0.20|0.49|
|Less often than the average client|0.38|0.40|0.31|0.25|
|Number of Observations|4195|2968|1204||
|Number of Women<br>Notes: Averages are sex worker reports from daily diarie<br>portion of the data.<br>The number of observations in Column 1 does not equal<br>client information is missing for some observations.<br>The_p_-values in Column 4 are from regressions with sex<br>Standard errors in those regressions are clustered at the<br>The exact number of observations differs from variable to<br>1Response to the question on HIV risk is non-missing for<br>The rest were coded as "don't know" or were left blank.<br>2The question about unprotected sex is the total amount<br>the client has, with all of his sexual partners.|s. Client d<br>the sum i<br>worker fix<br>individua<br>variable<br>only 195<br>of unprot|ata is onl<br>n Column<br>ed effects<br>l level.<br>due to mi<br>2 respond<br>ected sex|y availabl<br>s 2 and 3<br>.<br>ssing valu<br>ents.<br>the woma<br>108|e for a<br>because<br>es.<br>n thinks|