rafmacalaba/datause-encoder-data huggingface dataset:
- Herbst, J. I. (2000). _States and power in Africa: comparative lessons in authority and control_ . Princeton studies in international history and politics. Princeton, N.J: Princeton University Press. 1

- Hjort, J., Moreira, D., Rao, G., & Santini, J. F. (2019). How Research Affects Policy: Experimental Evidence from 2,150 Brazilian Municipalities. SSRN Scholarly Paper ID 3406456, Social Science Research Network, Rochester, NY. 5

- Independent Evaluation Group (2008). Decentralization in Client Countries: An Evaluation of World Bank Support, 1990–2007. 1, 7

- Independent Evaluation Group (2018). Approach paper: Evaluation of the World Bank Group engagement on strengthening subnational governments. 1, 5

- Jones, S., Imber, V., Gray, J., Lawson, A., Ghartey, A., Kashangaki, J., Maphiri, D., Mwanwina, I., Therkildsen, O., Wyatt, A., Kotoglou, K., & Shah, A. (2006). Developing capacity: An evaluation of DFID-funded technical cooperation for economic management in Sub-Saharan Africa. 7

- Kandpal, E., Khanna, M., Loevinsohn, B., Pradhan, E., Fadeyibi, O., McGee, K., Odutolu, O., Fritsche, G. B., Meribole, E., & Vermeersch, C. (2020). The effect of direct facility financing, autonomy, community engagement, supervision, and performance-based payments in strengthening primary health care: a large scale quasi-experimental trial in Nigeria. 28

- Khemani, S. (2015). Political Capture of Decentralization: Vote Buying through Grants to Local Jurisdictions. In _Is Decentralization Good for Development? Perspectives from Academics and Policy Makers_ . Oxford University Press. 5

- Khemani, S. (2019). What Is State Capacity ? Tech. Rep. 8734, The World Bank. Publication Title: Policy Research Working Paper Series. 5, 6, 13, 28

- Khemani, S. (2020). Legitimacy and Trust in the Times of COVID-19. Tech. Rep. Research & Policy brief No. 32, World Bank, Washington DC. 6

- Knack, S. (2001). Aid Dependence and the Quality of Governance: Cross-Country Empirical Tests. _Southern Economic Journal_ , _68_ (2), 310–329. Publisher: Southern Economic Association. 5

- La Ferrara, E. (2016). Media as a tool for institutional change in development. 28

- Legovini, A., & Jones, M. R. (2020). Administrative data in research at the World Bank: The case of Development Impact Evaluation (DIME). In _Handbook on Using Administrative Data for Research and Evidence-based Policy_ . https://admindatahandbook.mit.edu/. 28

- Levy, B., & Kpundeh, S. (2004). Building state capacity in Africa: New approaches, emerging lessons. 7

32
|Praia Port Project||
|---|---|
|Praia Port Project||
|Praia Port Project||
|Praia Port Project||
|Praia Port Project||
|Industrial Finance and Promotion Project||
|Industrial Finance and Promotion Project||
|Industrial Finance and Promotion Project||
|Industrial Finance and Promotion Project||
|Industrial Finance and Promotion Project||
|Industrial Finance and Promotion Project||
|Industrial Finance and Promotion Project||
|Industrial Finance and Promotion Project||
|Industrial Finance and Promotion Project||
|Basic Education Development Project|Carmen E. Perez Castano|
|Basic Education Development Project|Carmen E. Perez Castano|
|Basic Education Development Project|Carmen E. Perez Castano|
|Basic Education Development Project|Carmen E. Perez Castano|
|Basic Education Development Project|Carmen E. Perez Castano|
|Basic Education Development Project|Carmen E. Perez Castano|
|Basic Education Development Project|Carmen E. Perez Castano|
|Basic Education Development Project|Carmen E. Perez Castano|
|Basic Education and Training (BET)Project|Adriana Jaramillo|
|Basic Education and Training (BET)Project|Adriana Jaramillo|
|Basic Education and Training (BET)Project|Adriana Jaramillo|
|Basic Education and Training (BET)Project|Adriana Jaramillo|
|Basic Education and Training (BET)Project|Adriana Jaramillo|
|Basic Education and Training (BET)Project|Adriana Jaramillo|
|Basic Education and Training (BET)Project|Adriana Jaramillo|
|Infrastructure Rehabilitation Project||
|Infrastructure Rehabilitation Project||
|Infrastructure Rehabilitation Project||
|Infrastructure Rehabilitation Project||
|Infrastructure Rehabilitation Project||
|Infrastructure Rehabilitation Project||
|Social Sector Development|Johanne Angers|
|Social Sector Development|Johanne Angers|
|Social Sector Development|Johanne Angers|
|Social Sector Development|Johanne Angers|
|Social Sector Development|Johanne Angers|
|Social Sector Development|Johanne Angers|
|Social Sector Development|Johanne Angers|
|Transport and Infrastructure Project|Gylfi Palsson|
|Transport and Infrastructure Project|Gylfi Palsson|
|Transport and Infrastructure Project|Gylfi Palsson|
|Transport and Infrastructure Project|Gylfi Palsson|
|Transport and Infrastructure Project|Gylfi Palsson|
The financial account records transaction from the financial side, capturing the acquisition of financial assets and the incurrence of financial liabilities. We focus on the Financial Account portion of the BOP data.

There are several presentations of the BOP data.<sup>45</sup> The standard presentation disaggregates the data by flow type and instrument. Figure A1 illustrates this structure, with the available breakdowns by sector. The analytic presentation, which is the one available within the IMF’s International Financial Statistics (IFS), reports exceptional financing (used to meet balance-of-payments financing needs) separately from the standard presentation.<sup>46</sup> The analytic presentation can be useful to separate some public flows from private flows, because exceptional financing can be viewed as an alternative instrument to the use of reserve assets or IMF credit to help deal with balance of payments shortfalls.<sup>47</sup> We use the sectoral presentation, which breaks down the standard presentation by domestic institutional sector, but we also use measures of exceptional financing from the analytic presentation to allocate all exceptional financing flows to the public sector.

> 45See Chapter 14 Section C of the 6th edition BOP manual for a description of the various presentations. 46Exceptional Financing is usually classified under the other investment category.

> 47See the 6th edition BOP manual Appendix 1 for a description of Exceptional Financing. See Alfaro, S ̧ebnem Kalemli- Ozcan,<sup> ̈</sup> and Volosovych (2014) for discussion and use of IFS data to divide net flows into public and private components.

50
<!-- Start of picture text --> N<br><<br>Ew<br>on<br>Ww£5<br>s<br>3<br>Su<br>2<br>2<br>=g<br>S0<br>—<br>aH<br>°<br>25-34 35-44 45-54 25-34 35-44 45-54<br>male female<br><!-- End of picture text --> <!-- Start of picture text --> N<br><<br>Ew<br>on<br>Ww£5<br>s<br>3<br>Su<br>2<br>2<br>=g<br>S0<br>—<br>aH<br>°<br>High school or below College High school or below College<br>male female<br><!-- End of picture text --> <!-- Start of picture text --> =<br>co<br>g<br>€<br>Bio<br>ex<br>a<br>Ww<br>€<br>S2<br>£<br>D<br>&<br>2<br>Low<br>e&N<br>gs<br>2<br>=<br>tt<br>© FF@ CEC FFe CFS SF@ €§ SC Fo><br>}S KS r & }< RS r fe)<br>eSRy Fe SsRy<br>Vv Vv<br>male female<br>(ON «5-9 years [EMM 10-14 years [MM 15-19 years since migration<br><!-- End of picture text -->
in FDI sources, as well as in destination<sup>sectors,which lean heavily toward electricaland</sup> electronic products is a risk factor<sup>for East Asianeconomies.</sup> Source countries within East<sup>Asia that appear among the top three in this tableare</sup> Japan and Hong Kong, which may<sup>not be surprising,but the tablealso showsthat some</sup> of the NIEs (specifically Singapore<sup>andTaiwan)are among the top three investorsin</sup> some countries.<sup>4</sup> 1 From outside<sup>the region,themajor sourceof irnvestment,</sup> unsurprisingly, is the U.S. In<sup>Figure4.5, the EU was shown to be thetopinvestor in</sup> ASEAN. Table 4.8 shows this<sup>investmentcomes primarilyfrom only a few European</sup> countries - the Netherlands (in<sup>Malaysia), France(in Singapore),and the U.K.(in</sup> Indonesia and Vietnam).<sup>42</sup> Although Japan appears as the<sup>number oneor twoinvestor in the ASEAN-543</sup> countries in Table 4.8, according<sup>to Figure4.5,itsshare of investmentin ASEAN has</sup> declined in recent years. Does<sup>this mean that Japan is investing less in ASEANand more</sup> in other countries? In fact, one-fifth<sup>of total JapaneseFDI in 2001went to East and</sup> Southeast Asia, some of which<sup>was redirected from the U.S.44;however,30 percentof</sup> this amount went to China while<sup>ASEAN receivedless.Although Japan is not included</sup> as a top-three investor in China<sup>in Table 4.8,the Japaneseinvestnentgap betweenChina</sup> and the four ASEAN countries of<sup>Indonesia, Malaysia,the Philippinesand Thailand has</sup> become almost nil since 1999.<sup>Prior to that, Japaninvested morein the ASEAN-445but</sup> this began to decline sharply in<sup>1997.46</sup> Japanese transnationals began to<sup>increase theirinvestmentin China in the1 990s.</sup> A recent survey conducted by<sup>JapanBank for International Cooperation (JBIC)47</sup> revealed that Japanese companies<sup>with overseasmanufacturingbases in Chinarose</sup> rapidly from about 100 in<sup>1993 to nearly 700 in 2001.Although the survey also revealed</sup> the number of companies with<sup>bases in ASEAN-4was higher than that (over1,000in</sup> 2001), it is possible the number<sup>in China will surpass that in the nextfew years.China</sup> has been at the top of the list<sup>of promisingdestinations for manufacturingFDI by</sup> Japanese companies over the<sup>mediumterm as reportedin these annual surveyssince</sup> 1996. Other ASEAN countries<sup>ranked below China but Thailand,Indonesia,Malaysia</sup> and Vietnam were among the top<sup>ten in all years.48Nicholas Lardy4 9points to the</sup> relocation of Japanese electronics<sup>manufacturingto China as"emblematic"of the trend</sup> of MNCs to redirect FDI from<sup>SoutheastAsia to China.50</sup> Figure 4.6 presents the shares of<sup>East Asia'sFDI inflowsfor China and ASEAN</sup> and gives a fairly clear indication<sup>that Chinaistaking shares of FDI from ASEAN.</sup> 21
records, which contain extensive information on firms and individuals that are often not captured by traditional data collection mechanisms such as household surveys. Therefore, tax records can provide valuable input to improve the measurement of income inequality, with the caveat that they also present important limitations. Not only do tax records contain little information about the bottom segments of the income distribution (where individuals are not required to file a tax return), but also filers have the financial incentive to report their income in a way that limits their tax liabilities, therefore reducing the marginal tax rate (Burkhauser et al. 2012), and the estimation could be biased due to tax evasion. That said, examining the top of the income distribution is essential to understanding inequality, and tax records are a fundamental source for this purpose.

As shown by the key contributions of Atkinson (2007) and Alvaredo (2011), the share of income concentrated at the top of the income and wealth distribution can have dramatic effects on inequality. Recent studies have therefore proposed to rely on both household surveys and tax records to improve the estimation of the Gini coefficient. In particular, Atkinson (2007) proposes an approximate methodology to estimate the Gini coefficient. The first stage of his methodology consists in choosing a group of top income earners (say, the top 0.1 percent). Then, if this group of top earners is approximately an infinitesimal fraction of the population, with share _S_<sup>_∗_</sup> of total income, the Gini coefficient can be approximated by the following calculation: _S_<sup>_∗_</sup> + (1 _− S_<sup>_∗_</sup> ) _G_ . In this formula, _G_ is the Gini coefficient of the population that excludes the top earners. More recently, Alvaredo (2011) extends this methodology to the case in which the group of top earners chosen is not infinitesimal relative to the size of the population (for example, the top 1 or 5 percent of the income distribution).

These methodologies have two salient features worth noting. First, the choice of which – group of top earners to consider in the formula is arbitrary the top income group is not optimally chosen based on specific criteria. Second, the Gini coefficient _G_ for the population that does not include the chosen top earners group is computed using the household surveys. This means that, since we cannot determine precisely the extent of underreporting at the top of the income distribution, it is not clear that the _G_ estimated from household surveys 2
||<br>|of households and 20<br>production sectors<br>||
|---|---|---|---|
||Glomm<br>and<br>Jung (2014)|Dynamic model of<br>small open economy<br>with<br>multiple<br>generations<br>of<br>households|Economic impacts (i.e., GDP impacts) are<br>sensitive to the schemes of recycling the saved<br>subsidies to the economy; energy subsidy<br>removal causes welfare gain in most cases of<br>recycling the saved subsidy.|
|Kuwait|Gelan (2018)|Static CGE model|Reduction of energy subsidies causes GDP to<br>drop if the saved subsidy is not recycled to the<br>economy, whereas it would increase the GDP if<br>the saved subsidy is recycled to consumers as a<br>cash transfer.|
|Gulf<br>Cooperation<br>Council<br>(GCC)<br>countries|Al<br>Iriani,<br>M.A. and M.<br>Trabelsi<br>(2015).|Econometric<br>estimates of causal<br>relationships<br>between<br>energy<br>consumption<br>and<br>GDP and implying<br>the effects of subsidy<br>removal based on<br>this relationship|Impacts differ across GCC countries; some<br>countries are likely to have negative impacts of<br>subsidy removal (Oman) while others have no<br>impacts (Bahrain, Kuwait) or small negative<br>impacts, which can be compensated through<br>other policies (Saudi Arabia, Qatar)|

The approaches used in the input-output or econometric studies discussed above are not adequate to capture the economic impacts of energy subsidy removal for two reasons. First, these approaches do not account for linkages between the economic agents (e.g., households, governments, industries and rest of the world), although input-output models capture the linkages within industry sectors through fixed coefficients. Second, these models do not account for the opportunity cost of the subsidy. The savings from subsidy removal can contribute to economic growth if they are invested in productive sectors which have a comparative advantage. The savings could increase social welfare if they are recycled to households as a cash transfer instead of subsidizing household energy consumption. This would have a significant distributional or equity effect if savings are recycled to low-income households. Since the input-output and econometric approaches used in existing studies are not capable of measuring the full economic impact of subsidy removal, some researchers have developed partial equilibrium models to analyze the economic impacts of energy subsidy removal or reform (see, e.g., Breton and Mirzapour, 2016).

Defining the price difference between the border price (or opportunity cost) and actual price in the retail market (i.e., the price that consumers actually pay) as the energy subsidy, Breton and Mirzapour (2016) investigate the impacts of energy subsidy removal in the Islamic Republic of Iran by developing a partial equilibrium empirical model. Their study finds that typical 6
villages ( _N_<sup>_NE_</sup> ). Note that we still have to control for the number of neighboring evaluation villages ( _N_<sup>_E_</sup> ) in order to account for the fact that the identifying variation is non-zero for 42 percent of the evaluation villages (i.e., those that have at least another evaluation village in their radius of zero to five kilometers) and in order to assure that the exclusion restriction is valid (because of the correlation across village neighborhoods between _N_<sup>_E_</sup> and _N_<sup>_NE_</sup> ).

The corresponding estimation results are reported in table 6. The point estimate of the first-stage parameter for the effect of an additional treatment group village on neighborhood treatment density is estimated at 1.43 (column 1). The corresponding t- statistic is 1.97 and the F test of the excluded instrument is 3.9, which is statistically significant at the five percent level. The reduced form coefficient for the effect of the number of treated villages in the zero- to five-kilometer radius on secondary school enrollment is 0.028 (column 2), which is very much in line with the corresponding estimate reported in column 1 of table 3. Column 3 reports the IV point estimate of the relationship between the frequency of neighboring program localities and secondary school enrollment, which is positive but not significantly different from zero. The IV point estimate has a smaller magnitude than the OLS one reported in column 2, which reflects the larger-thanone first-stage relationship and is estimated with more noise due to the reduced statistical power in this specification.

As a last robustness exercise, we consider alternative measures of program take-up based on the health component of the program. We use household-level information from the post-program survey round of October 1998 on the uptake of three screening tests that form part of the health requirements of the _Progresa_ program: hypertension (blood pressure test), diabetes (blood sugar test), and cervical cancer (via the PAP smear test).<sup>20</sup> 20. The household respondents were asked whether or not any household member had been screened for these conditions in the previous six months. In order to maintain full comparability with the estimates of program spillover reported in the paper, we estimate at the household-level the linear model reported in equation (1) in the paper on the same sample that we consider in the main empirical analysis. Results (available upon request) are very similar in both magnitude and precision if we instead consider the larger sample of all program-eligible households in October 1998.

23
Table 11: Impact of Heat Exposure on Grade Progression

||Grade Pro<br>|gression(F<br>|raction)<br>|
|---|---|---|---|
||All|Male|Female|
||(1)|(2)|(3)|
|Lag 1: T_>_33C|_−_0_._0004|_−_0_._0003|_−_0_._001|
||(0_._0005)|(0_._0005)|(0_._0005)|
|Lag 1: T 30-33C|0_._001|0_._0005|0_._0003|
||(0_._001)|(0_._001)|(0_._0004)|
|Lag 1: T 27-30C|_−_0_._0002|_−_0_._0001|0_._00004|
||(0_._0003)|(0_._0004)|(0_._0003)|
|Lag 1: T 24-27C|0_._00004|0_._0001|_−_0_._0002|
||(0_._0002)|(0_._0002)|(0_._0003)|
|Lag 1: T 21-24C|0_._0004|0_._0005|0_._0005|
||(0_._0004)|(0_._0004)|(0_._0004)|
|Lag 1: T_<_18C|0_._00001|0_._0001|_−_0_._0002|
||(0_._0005)|(0_._0005)|(0_._0004)|
|Outcome Mean:|-0.016|-0.011|0.017|
|Female Control||||
|**Fixed Effects**||||
|Year|X|X|X|
|Region|X|X|X|
|Observations|2,482|2,475|2,475|
|R<sup>2</sup>|0.015|0.014|0.016|

Notes. Estimates are from linear regressions using panel fixed effects run at the school level. The dependent variable is the fraction of students in each school who enrolled in grade 12 as compared to the class composition in the previous year (grade 11). All regressors include the number of days in the temperature bin during the time when these students were in their 11th grade. All regressions control for the number of days in the precipitation bins during the time that the current cohort of students were in their 11th grade. Errors are clustered at the region level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

30
For 2011, we use the tariff rate data in the ―Unified Customs Tariffs of the Customs Union of Belarus, Kazakhstan, and Russia‖ as of January 1, 2012.<sup>11</sup> The reason that we use the tariff data of January 1, 2012 for 2011 is that we want to have an assessment of the impact of the Customs Union on the tariffs of Russia. Since for 2010, we use pre-Customs Union tariffs, and for 2012, we use tariff rates that are impacted by the WTO commitments, we need to take a date for the tariff rates that is representative of the Customs Union tariff rates. The changes in the tariff code implemented on January 1, 2012 were potentially significant and are the most representative of the tariffs of the Customs Union. We should interpret ―2011‖ in our calculations as Customs Union tariffs, and we can interpret the difference in tariffs between 2010 and 2011 as the impact of the Customs Union schedule on the tariffs of Russia.

The source of data on tariff rates of Russia after the WTO accession is a combination of the Customs Union tariff schedule mentioned above and the Schedule of Concessions and Commitments on Goods of the Russian Federation related to the WTO accession. Commitments at the WTO are known as ―bound‖ tariffs; this means they are the maximum tariff that can apply at the tariff line level. The actual applied MFN tariff may be less than the bound tariff, a situation referred to as ―water in the tariff schedule.‖ For the years 2012 and later, we take the applied MFN tariff as the minimum of the bound tariff in any year and the applied tariff of the Customs Union as of January 1, 2012.

The WTO commitments schedule became publicly available from the WTO after the Russian Federation was formally invited to join the WTO on December 16, 2011. The Russian Federation is scheduled to implement its commitments to the WTO in stages, beginning with the summer of 2012, with the longest transition period eight years after accession, or 2020. We calculate the impact of the phased implementation of the commitments, starting from the impact of the commitments in 2012.

> cm3; and 870324 is for cylinder capacities exceeding 3 000 cm3. Within each six digit category, we take a simple average of the lower and upper bounds as the expected value of the cylinder capacity, i.e., for 870321 we have 500 (0+1000)/2=500).  Therefore, the following expected engine volumes apply: 500 cm3 for 870321, 1250 cm3 for 870322, 2250 cm3 for 870323, and 3000 cm3 for 870324.  Then we know that all ten digit tariff lines with the same 6 digit code have this engine capacity and we can apply the specific tariff to the numbers of cars in the import data based on the engine capacity that applies to these ten digit tariff lines from the tariff schedule.

> 11 This document is available from the website of the Customs Union, http://www.tsouz.ru/.

6