rafmacalaba/datause-encoder-data huggingface dataset:
some sectors (e.g., health) may need larger improvements in efficiency than, for example, education. MAMS estimates suggest that if the level of foreign grants is constrained to 40 percent of the amount needed in the “full” foreign grant scenario, the overall level of public sector efficiency would need to rise by 49 percent relative to the 2004 levels. This means that in primary education, the same outcome could be achieved with 19 percent fewer teachers (relative to the “full” foreign grant scenario), while in health, the MDGs could be reached with 21 percent fewer physicians. Overall, the cost savings from increased efficiency amount to US$7.8 billion (starting from the accelerated growth scenario and reaching the same HD targets as in the MDGs’ achievement scenario) over the 2004-2015 period. Rather than suggests a specific normative view of required efficiency gains, this serves to illustrate a basic point that while (i) there is scope for significant efficiency gains in the social sectors, (ii) additional, well targeted and spent resources are required to achieve the full set of MDGs considered in this exercise.

**Figure 4: Resource requirements for full achievement of MDGs**

<!-- Start of picture text --> Total public expenditure, trillions constant Cedis<br>70<br>Cost of providing non-MDG services<br>Cost of providing MDG services<br>60<br>50<br>No removal of growth bottlenecks<br>Removal of growth bottlenecks<br>40<br>30<br>20<br>10<br>0<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> The pursuit of MDGs has important consequences for the macroeconomy. Due to the sustained inflows of foreign aid in the foreign grants MDG scenario, real GDP growth accelerates by an additional 0.4 percentage points per year relative to the accelerated growth scenario (Table 4). The aid inflows have important “Dutch disease” implications for Ghana: by raising incomes and spending on consumption goods, they result in increased consumption of imports and loss of export competitiveness, which are manifested in real exchange rate appreciation. In particular, the growth of exports is dampened significantly relative to the accelerated growth scenario, although it still remains above the baseline. Although a permanently higher level of the real exchange rate may represent a new equilibrium for the economy, the real danger lies in a sudden cessation of aid flows––a kind of international aid “sudden stop” equivalent of private 17
specifications are the third and sixth columns, i.e., those with full controls. We highlight a few interesting patterns that corroborate the descriptive results in the earlier sections.

**<u>Table 7: Bidirectional measurement error in plot size and the impact of information provision</u>**

||(1)<br>**Measuremen**<br>**[log(self-r**<br>|(2)<br>**t error: Post-har**<br>**eported)-log(GPS**<br><sup>**</sup>|(3)<br>**vest**<br>**)]**<br><sup>**</sup>|
|---|---|---|---|
|**Treatment**|0.040|0.054|0.063|
||(0.024)|(0.026)|(0.027)|
|Plot size|-0.000|-0.000<sup>***</sup>|-0.001|
||(0.000)|(0.000)|(0.007)|
|**Treatment x Plot size**|-0.311<sup>***</sup>|-0.310<sup>***</sup>|-0.311<sup>***</sup>|
||(0.042)|(0.041)|(0.044)|
|Measurement error-pre-treatment|1.000<sup>***</sup>|1.000<sup>***</sup>|0.995<sup>***</sup>|
||(0.000)|(0.000)|(0.007)|
|**Treatment x Measurement error-pre-treatment**|-0.807<sup>***</sup>|-0.789<sup>***</sup>|-0.789<sup>***</sup>|
||(0.075)|(0.072)|(0.070)|
|Measurement error-pre-treatment x Plot size||-0.000|-0.002|
|||(0.000)|(0.004)|
|**Treatment x Measurement error-pre-treatment x plot size**||0.034|0.035|
|||(0.043)|(0.043)|
|Constant term|0.310<sup>***</sup>|0.310<sup>***</sup>|0.323<sup>***</sup>|
||(0.000)|(0.000)|(0.097)|
|Controls|No|No|Yes|
|Mean of dependent variable|0.334|0.334|0.337|
|R-squared|0.589|0.590|0.595|
|No. observations|1546|1546|1535|

**Notes:** measurement error is computed as difference between log-transformed self-reported and GPS values, log(selfreported)-log (GPS). Standard errors, clustered at enumeration area level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05, *** _p_ < 0.01.

The main coefficients of interest in Tables 7 and 8 are those that involve interactions between treatment and either pre-treatment measurement error or plot size. For example, the interaction between treatment and pre-treatment measurement error in Table 7 shows reasonably strong, albeit incomplete, response to information. While we can reject the null hypothesis of onefor-one correction of pre-treatment NCME, holding constant everything else, at mean plot size the information treatment does correct 79-81 percent of pre-treatment measurement error. This represents the level of correction at the mean plot size and pre-treatment measurement error and is expected to represent maximum adjustment. These results confirm that learning is incomplete – given that almost two-thirds of plots were overestimated pre-treatment – that overestimation remains much larger than underestimation after treatment, and that learning is asymmetric.

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42 _Figure 2._ Search process and results for evaluated professional development programs <!-- Start of picture text --> 4,294 records identified through<br>search of databases<br><!-- End of picture text --> <!-- Start of picture text --> 20 records identified through other<br>sources<br><!-- End of picture text --> <!-- Start of picture text --> 4,272 records excluded<br><!-- End of picture text --> <!-- Start of picture text --> All records screened<br><!-- End of picture text --> <!-- Start of picture text --> 42 full texts assessed for eligibility<br><!-- End of picture text --> <!-- Start of picture text --> 18 full texts excluded<br><!-- End of picture text --> <!-- Start of picture text --> 23 studies (26 programs) included<br><!-- End of picture text -->
<!-- Start of picture text -->
1. Input testing 2. Mixing/blending/cooking 3. Anti-bacterial 4. Packaging 5. Food storage<br>Penson) systems Minimal-processing Manual packing in Minimal protection,<br>(visual, smell,etc.) color, Manual process preservationti methodsthod b jags, bottlesbottles or or b boxes someroe exposure earsto<br>ReviewA of supplieray Mechanicalain equipment Anti-bacterialF A wash or Human operated Ambient5 conditionsPahin<br>testing on Certificate of requiring human force soaking mechanical equipment closed building<br>Analysis to operate for packaging in bags,<br>bottles or boxes<br>Non-computer- poe hears Thermal Processing Some climate control<br>controlled testing kits pan acon Technologies Automated process in secured building<br>with minimal human<br>interaction<br>Computer testing such Power equipment Other advanced Fully automated<br>as chromatography or controlled by methods such as High- ; climate and security-<br>spectroscopy computers or robotics, pressure processing Fully Automated with controlled building<br>with minimal human (HPP) and Pulsed Robotics<br>interaction electric field (PEF)<br><!-- End of picture text -->
- Li, Y., G. Guo, A. Shi, Y. Li, T. Anme, and H. Ushijima (1999) ‘Prevalence and correlates of malnutrition among children in rural minority areas of China.’ _Pediatrics International_ 41, 549–556

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Community and Technical Colleges. (CAPSEE Working Paper). New York, NY: Center for Analysis of Postsecondary Education and Formal Employment.

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- <mark>Carrell, S. E., and M. Kurlaender. 2019. “Estimating the Productivity of Community Colleges in Paving the Road to Four-Year Success.” (No. w22904). National Bureau of Economic Research.</mark>

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34
bad, since, for example, the collection of a regressive tax can be used to make transfers that benefit the poor to a greater extent and, therefore, the combination of these two policies helps to reduce inequality. In addition to the Kakwani coefficient, it is important to calculate the marginal contribution of each of the interventions to the reduction of inequality. The marginal contributions of a policy are calculated as the difference between the inequality index when it is in hand, and when the transfer nor the tax under consideration is not taken into account.

Another way to study the progressivity of the tax system is to look at the participation of each decile in each component of the system. The progressivity in absolute terms of direct and indirect taxes, direct transfers, and education spending is evident, given that, for example, 85 percent of direct taxes are paid by deciles 8, 9, and 10. While 55 percent of direct transfers are received by the bottom 40 percent of the distribution (Figure V-2). On the other hand, health spending seems to be a neutral policy, given that while households in the upper part of the distribution (deciles 5 to 10) receive 50 percent of transfers in kind for health; the transfers delivered to the deciles on the lower part of the distribution (decile 1 to 5) are also equivalent to 50 percent of the total health expenditure.

<!-- Start of picture text --> FIGURE V-2 : PROGRESSIVITY IN ABSOLUTE TERMS OF TAXES, TRANSFERS, AND SUBSIDIES<br>1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Direct taxes Direct transfers Indirect taxes Subsidies Education Health<br>Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Decile 10<br>Source: World Bank Group staff estimates based on ENPH 2017<br><!-- End of picture text --> Figure V-3 presents the general results of the tax system and spending in Colombia. As can be seen, except for indirect taxes, all other policies in Colombia are progressive and contribute to reducing inequality in the country. Annex A shows both the Kakwani coefficient for each of the taxes and transfers considered in this paper, as well as the marginal contributions for the reduction of inequality and poverty when pensions are considered a transfer. The results are presented in Annex B when pensions are considered deferred income. What follows from this section seeks to discuss the main results.

34
<!-- Start of picture text --> — Customs ——- VAT ------- Income —-- Excise —— Other Taxes<br>40<br>an a ee _--—:-<br>= =_— —-— 2oa — ~<br>» Saale ee > Sccccceeeee Le ee<br>5 Me er =<br>Y 20 Tomes eeesesecec es<br>oO<br>law<br>_ . — _ —_-<br>eT oT<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: Government of Nepal (2011) Note 1: Customs includes import tariffs, export duties, Indian Excise Refund
<!-- Start of picture text --> HB economic growth Qf unemployment [inflation Q§) famine J hunger J malnutrition QJ economic development [ Third World ff migration Jf globalization<br>0.003005,<br>0.002804) \.<br>0.00240%%) Fd *) =<br>0.00200%%|ne [SN /f] ~—N<br>0.00160% 7 oN<br>‘0.00080% SEE OT IO<br>0.00040%%) y Zz: f—<br>0.00000F gna TE20 Te40 Ta60 7880 Ts00 7920 7940 TS60 7980 2000<br><!-- End of picture text -->
**B.3 Distribution of the sample across sizes and sectors**

Table A2: Frequencies by size; unweighted

||Micro(0-4)|Small(5-19)|Med(20-99)|Large(100+)|Missing|Total|
|---|---|---|---|---|---|---|
|AFG|0.0|52.8|36.0|10.9|0.3|100.0|
|ALB|5.1|29.9|39.6|25.4|0.0|100.0|
|ARM|100.0|0.0|0.0|0.0|0.0|100.0|
|BGD|32.2|35.2|18.2|13.8|0.6|100.0|
|BGR|28.1|34.6|25.9|9.8|1.7|100.0|
|BRA|54.5|28.0|9.5|5.6|2.4|100.0|
|CIV|21.4|51.0|20.8|5.5|1.3|100.0|
|COL|0.0|0.0|0.0|0.0|100.0|100.0|
|CYP|10.2|45.8|29.2|14.0|0.8|100.0|
|DZA|3.5|27.9|43.3|23.9|1.4|100.0|
|GAB|57.7|21.6|12.9|7.1|0.7|100.0|
|GEO|19.2|37.0|31.3|12.4|0.1|100.0|
|GHA|70.3|20.6|6.4|1.2|1.6|100.0|
|GIN|12.1|57.0|19.5|11.4|0.0|100.0|
|GRC|3.7|42.3|34.8|19.2|0.0|100.0|
|GTM|5.3|36.1|30.8|27.9|0.0|100.0|
|HND|6.7|49.7|28.5|14.4|0.6|100.0|
|IDN|20.8|31.6|26.1|19.9|1.6|100.0|
|IND|4.8|24.9|39.3|30.6|0.4|100.0|
|ITA|1.4|43.4|30.7|24.4|0.1|100.0|
|JOR|17.0|38.3|30.7|14.0|0.0|100.0|
|KEN|43.4|28.3|17.6|9.6|1.2|100.0|
|KHM|20.0|33.9|25.9|19.8|0.4|100.0|
|LBR|84.8|12.9|2.1|0.2|0.0|100.0|
|LKA|52.0|26.0|11.0|10.0|1.0|100.0|
|MDA|6.2|34.2|37.8|21.8|0.0|100.0|
|MDG|53.5|22.4|13.1|9.2|1.7|100.0|
|MLI|20.9|42.5|26.0|9.6|1.0|100.0|
|MNG|5.2|42.4|38.2|14.2|0.0|100.0|
|NER|6.8|53.7|32.0|6.8|0.7|100.0|
|NIC|5.5|36.7|39.4|17.7|0.6|100.0|
|NPL|41.3|40.7|12.5|3.8|1.8|100.0|
|PAK|45.6|29.6|16.4|6.8|1.5|100.0|
|PHL|56.1|21.3|8.6|6.0|8.0|100.0|
|POL|25.2|26.1|33.1|14.5|1.1|100.0|

41
in Panel B confirm that, even including the not yet treated provinces as the control group, the magnitude and direction of _ATTs_ do not change, except for the aggregate _ATTs_ of the event study, which are slightly larger for both employment in the agriculture sector and the unemployment rate.

# **4.2 Effects of Heterogeneity**

In this section we explore the effects of heterogeneity by analyzing the impacts along four important margins, i.e. age, gender, educational level and formal vs. informal jobs.

**_Age_** – To begin with, Figure 3 shows the heterogeneous effects across five different age groups. The drought impact appears much stronger and significant among middle-aged workers (1850), among whom we observe a drop of about 2.5 p.p. in the agricultural employment rate in the second year, reaching about 7 p.p. in the fourth year. At the same time, the employment rate in other sectors moves in the opposite direction, though with a nonsignificant magnitude. We also observe an increase in the unemployment rate, with a coefficient that becomes significant and stable at about 2.5 p.p. after the third year, which signals a reallocation failure for only a small share of middle-aged workers. We find nonsignificant effects both for workers under the legal age (15-17) and for workers close to retirement (51-60), with the exception of a sharp but temporary increase in the unemployment rate in the group of very young workers in the first year after the shock.

**_Gender_** – Figure 4 shows the heterogeneous effects by gender. While overall we find significant effects in both females and males, the temporal dynamic appears very different. The drop in the agricultural employment rate is U-shaped for female workers, reaching 9.8 p.p. in the fourth year, vis-á-vis 6 p.p. for males. However, among female workers the effect decreases to about 5.1 p.p. after five years, while it steadily increases for males. As a consequence of the lack of significant evidence for reallocation in other sectors, the unemployment rate for females increases up to 4.9 p.p., more than twice what is observed for males.

14
of marriage and fertility decisions.<sup>4</sup> Given the close link between sexual behavior and HIV infection, and given the burden HIV poses on these economies, this is potentially a very important impact to document.

There are good reasons to think that CCT programs for schooling may affect the sexual behavior of young people. Education has been suggested as a “social vaccine” to change sexual behavior and prevent the spread of HIV (Jukes, Simmons, and Bundy, 2008), but almost all of the evidence we have on this comes from cross-sectional studies. Furthermore, the role of income (especially that of women’s poverty) has often been cited as a significant factor in the spread of HIV in SSA, but again there is little credible evidence showing a causal link between income, sexual behavior, and HIV risk. Given the higher prevalence of HIV infection among young women in SSA (3.2% among women aged 15-24 vs. 1.1% among men in the same age group (UNAIDS, 2008)), the policy importance of identifying any potentially large impacts of CCT programs for schooling on sexual behavior cannot be overstated.

This paper aims to provide new causal evidence on the effects of a CCT program (with **only** school attendance used as a condition to receive the transfers) on the selfreported sexual behavior of the young, female beneficiaries of the program. It does so by examining the one-year impacts of an ongoing two-year randomized intervention in Malawi that provides cash transfers to young women to stay in (or return to) school. Conditional cash transfers blend ‘income’ effects (through the transfers themselves) with ‘price’ effects (through the conditionality), and our study estimates the joint effect of these two policy parameters. As such, this paper provides the first experimental evidence

> 4 We know of one other study (Duflo et. al., 2006) that examines the effect of providing school uniforms on the likelihood of teen marriage and childbearing. The implied transfer size for that study is significantly lower than the transfers in the program being evaluated here.

3
<!-- Start of picture text --> Vietnam Development Goals (VDGs)/Millennium Dev. Goals (MDGs) background papers<br><!-- End of picture text -->

|(allavailableat:www.vdic.org.vn)|
|---|

- Asian Development Bank (2002). Localising MDGs for poverty reduction in Vietnam: Ensuring good governance for poverty reduction. Strategies for achieving the Vietnam Development Goals.

- Asian Development Bank and World Health Organization (2002). Localising MDGs for Poverty Reduction in Vietnam: Improving health status and reducing inequalities. Strategies for achieving the Vietnam Development Goals.

- Department for International Development, DFID (2002). Localising MDGs for Poverty Reduction in Vietnam: Providing quality basic education for all. Strategies for achieving the Vietnam Development Goals.

- Japan Bank for International Cooperation, JBIC (2002) Localising MDGs for Poverty Reduction in Vietnam: Enhancing access to basic infrastructure. Strategies for achieving the Vietnam Development Goals.

- United Nations Development Program (2002a). Localising MDGs for Poverty Reduction in Vietnam: Ensuring environmental sustainability. Strategies for achieving the Vietnam Development Goals.

- United Nations Development Program (2002b). Localising MDGs for Poverty Reduction in Vietnam: Promoting ethnic minority development. Strategies for achieving the Vietnam Development Goals.

- World Bank (2002a). Localising MDGs for Poverty Reduction in Vietnam: Eradicating poverty and hunger. Strategies for achieving the Vietnam Development Goals.

- World Bank (2002b). Localising MDGs for Poverty Reduction in Vietnam: Reducing vulnerability and providing social protection. Strategies for achieving the Vietnam Development Goals.

- World Bank (2002b). Summary of the eight papers on Localising MDGs for Poverty Reduction in Vietnam: Achieving the Vietmam Development Goals: an Overview ofProgress and Challenges. Strategies for achieving the Vietnam Development Goals.

46