rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> = Full Gender Employment Gap Index (GEGI) Comparing Basic and Full GEGIs<br>5 Increase GDPPC from closing gender employment gap (%) YE!<br>Ww oYEM =<br>Vo<br>So<br>oc _CBIOR<br>x ofsyOR ° Ne be<br>So eAEG @PAK eEGaDzse!RN oSAU _© D<br>£oO eIND eomNn Fa @TJK APN<br>g oluk oLBN ; @NP@MRT-@ TUN<br>0)=o enPeMRT°BGD oTUN moOSs ®<br>c=® Marshall Islanke eTUR BHR (0)Ww ences eZ<br>aySo eoghieMicronegia,° Fed, SiSerigtie,2 FF iji@ ° ° Nowe °KARE ©QAT fo) Oj bidaepnp 4 yah e@TUV<br>&S~ ONER ©BF/ ° ° Kiribaty@ gate 0° ° ° n ec ecObiges> ecr<br>2cy geen Islands@B vauatu9, 00“Sorg o@o20 %°2, Palgué8 5 le 004 ahsReinaeee JAN? OOM<br>roles eBDI fo e°° Oo” Sete %a2%© 90° gag© 0°o oeCHtONOF fo)<br>a $250 $500 $1000 $2000 $4000 $8000 $16000 $32000 $64000 0 20 40 . 60 80<br>rc Income Per Capita (log scale) GEGI - Basic<br>Notes: GEGI is increase in long run GDP per capita from closing the employment gender gap. Notes: 45 degree digaonal shown. GEGI_basic rounded up to zero in Burundi, Sierra Leone, Togo.<br>Full GEGI<0 rounded up to 0 in Finland, Lithuania, Moldova, Seychelles and Togo. GEGI_full rounded up to zero in Finland, Lithuania, Moldova, Seychelles, Togo<br>Income is GNIPC in 2017 dollars. Each dot represents a country with Pacific Islands in maroon. GEGI is increase in long run GDP per capita from closing employment gender gaps.<br><!-- End of picture text -->
PRE Wor,king Paper Series

|||Aug=|DM|Contact<br>foraper|
|---|---|---|---|---|
|WPS512|Social Security Reform: The Capital<br>Accumulation and Intergenerational<br>Distribution Effect|Patricio Arrau<br>|October 1990|S. King-Watson<br>31047|
|WPS5|The Businesb Cycle Associated with<br>Exchange-Rate-Based<br>Stabilization|Miguel A. Kiguel<br>Nissan Liviatan|October 1990|E. Khine<br>39361|
|WPS514|Restrictive Labor Practices in<br>Seaports|Alan S. Harding|October 1990|A. Joseph<br>33743|
|WPS515|Stock Markets in Developing<br>Countries: Key Issues and a<br>Research Agenda|Mansoor Dailami<br>Michae&<br>Atkin|October 1990|M. Raggambi<br>37657|
|WPS516|Internationa: Capital Mobility and the<br>Costs of U.S. Import Restraints|Jaime de Melo<br>David Roland-Holst|October 1990|S. Fallon<br>37947|
|WPS517|Do Wage Distortions Justify<br>Protection in the U.S. Auto and<br>Steel Industries?|Jaime de Melo<br>David Tarr|October 1990|S. Fallon<br>37947|
|WPS518|Industrial Organization and Trade<br>Liberalization: Evidence from Korea|Jaimede Melo<br>David Roland-Holst|October 1990|S. Fallon<br>37947|
|WPS519|Taxes, Outward Orientation, and<br>Growth Performance in Korea|Irene Trela<br>John Whalley|October 1990|A. Bhalla<br>37699|
|WPS520|Trade Reform, Policy Uncertainty,<br>and the Current Account|Sweder van Wijnbergen|October 1990|M. Stroude<br>38831|
|WPS521|Wo, ld Bank Treatment of the Social<br>Impact ot Adjustment Programs|Helena Ribe<br>Soniya Carvalho|October 1990|M. Abiera<br>31262|
|**'II**<br>WPS522|A Survey of the Costs of World<br>Sugar Policies|Brent Borrell<br>Ronald C. Duncan|October 1990|A. Kitson-Walters<br>33712|
|WPS523|EC Bananarama<br>1992|Brent Borrell<br>Maw-Cheng<br>Yang|October 1990|S. Lipscomb<br>33718|
|WPS524|The Kuwani NGOsTheirFoie in Aid<br>Flows to Developing Countries|Nural Abdulhadi|October 1990|R. Grimm<br>S12-018|
|WPS525|School Effects on Achievement in<br>Secondary Mathematics and<br>Portuguese in Brazil|Marlaine E. Lockheed<br>Barbara Bruns|October 1990|C. Cristobal<br>33640|
|WPS526|Rural Poverty in India, 1973-86|Nanak Kakwani<br>Kalinidhi Subbarao|October 1990|B. Rosa<br>33751|
||0.24|0.21|0.36|
|---|---|---|---|
|**Public revenues**|2.78|2.06|-0.72**|
||0.33|0.17|0.34|

Note: Standard errors below statistics. * p<0.10, ** p<0.05, *** p<0.01 Next, we compare the cyclicality of the key macroeconomic variables for fiscal rule adopters and non-adopters. Table **11** reports the results for cyclicality with GDP, while Table **12** summarizes our findings for the cyclicality with the real interest rate. Table **11** shows that for most variables, the cyclicality with GDP does not differ much between fiscal rule adopters and non-adopters. The only exceptions are trade balance and current account which tend to be more countercyclical in countries that have fiscal rules in place; and imports which exhibit higher correlation with GDP in fiscal rules adopters. These differences are statistically significant. Interestingly, our results show that having a fiscal rule in place does not have a significant effect on the cyclicality of government consumption or investment. At the same time, total government expenditures are more procyclical with GDP in countries with no fiscal rules. We conjecture that this higher correlation is driven by the higher cyclicality of transfer payments in these countries. Public revenues are more procyclical in countries with fiscal rules, but the difference is not significant.

|**Table 11.** **Cyclicality of key**|**macro aggregates**<br>**no fiscal rule**|**with GDP, by the presen**<br>**with fiscal rule**|**ce of a fiscal rule**<br>**diff**|
|---|---|---|---|
|**GNI**|0.89|0.91|0.01|
||0.03|0.02|0.03|
|**Trade balance/GDP**|-0.09|-0.27|-0.18***|
||0.05|0.04|0.07|
|**Current account/GDP**|-0.12|-0.32|-0.19***|
||0.05|0.04|0.06|
|**Real int rate**|-0.08|-0.04|0.03|
||0.05|0.05|0.07|
|**Terms of trade**|0.10|0.15|0.05|
||0.04|0.04|0.06|
|**Fiscal balance/GDP**|0.17|0.30|0.13|
||0.07|0.07|0.11|
|**Inflation**|-0.02|0.07|0.09*|
||0.03|0.03|0.05|
|**Total investment**|0.59|0.70|0.11**|
||0.04|0.03|0.05|
|**Employment**|0.57|0.59|0.03|
||.|0.07|.|
|**Government consumption**|0.29|0.30|0.00|
||0.04|0.04|0.05|
|**Private consumption**|0.63|0.64|0.01|
||0.05|0.03|0.06|
|**Exports**|0.43|0.46|0.03|
||0.03|0.03|0.05|

32
**National** Expenditure **Ber** Stumiat. Vocational ard Academic **Seornglay Schoos**

**Toble** 5

||National|EPmerditure|National<br>perS|Maxerditure<br>tuxent|Proportional<br>Vocational|<br>Differential<br>School Costs|
|---|---|---|---|---|---|---|
|Year|Academic|Vocational|Academic|Vocational|Rer|Student|
||Swools|Schools|Schools|Schools|||
||1|2|3|4|(4|-3)/4|
|1969/7|0<br>115|114|1.80|2.51|1.39|-f0.39|
|1970/7|1<br>125|164|2.10|2.67|1.27|+0.27|
|1971/7|2<br>201|208|3.67|3.09|0.84|-0.16|
|1972/7|3<br>268|236|4.88|3.35|0.69|-0.31|
|1973/7|4<br>365|357|6.75|5.06|0.75|-0.25|
|1974/7|5<br>466|477|8.12|6.76|0.82|-0.18|
|1975/7|6<br>588|678|10.46|9.50|0.91|-0.09|
|1976/7|7<br>861|1007|15.16|14.05|0.93|-0.07|
|1977/7|8<br>136|166|2.38|2.3)|0.97|-0.03|
|1978/7|9<br>224|289|3.89|3.95|1.015|+0.015|
|1979/8|0<br>474|563|7.70|7.43|0.96|-0.04|
|1980/8|1<br>1143|1325|17.86|16.88|0.915|-0.085|
|1981/8|2<br>2862|3108|43.26|38.54|0.89|-0.11|
|1982/8|3<br>6447|6853|91.69|83.98|0.92|-0.08|
|1983/8|4 18,892|18,892|253|223|0.88|-0.12|
|1984/8|5 117,172|102,882|1518|1152|0.78|-0.22|
|*1984/8|5 129,758|116,080|1772|1300|0.73|-0.27|

EBqxpeitures on educaticn are expressed in current prices: 1969/70 - 1976/77 in millions of Lirot, 1977/78 - 1982/83 in millions of Shekel, from 1983/84 in txousands of new Shekel.

Expenditures per student: 1969/70 - 1976/77 thousands of Lirot, 1977/78 - 1982/83 thousands of Shekel, from 1983/84 rew Shekel.

*New series Source: Statistical Abstract of Israel

- 23 -
Policy Research Working Paper 8368

# **Abstract**

The reallocation of resources from low- to high-productivity firms can generate large aggregate productivity gains. The paper uses data from the Malaysian manufacturing census to measure the country’s hypothetical productivity gains when moving toward the level of within-sector allocative efficiency in the United States to be between 13 and 36 percent. Across three census periods in 2000, 2005, and 2010 (the most recent available), the productivity gaps appear to have somewhat widened. This suggests that the “catching-up” process remains a challenge and a potential opportunity, particularly if total factor productivity is expected to be the dominant source of future economic growth. The simulations, based on different magnitudes of the realization of hypothetical productivity gains, show that Malaysia’s gross domestic product growth can potentially increase by 0.4 to 1.3 percentage points per year over five years. The analysis accounts only for resource misallocation _within sectors._ There may be other, possibly large, resource misallocation _across sectors._ If so, closing those gaps could boost total factor productivity and gross domestic product growth even further.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at llchuah@worldbank.org, nloayza@worldbank.org, and hanguyen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> ito)<br>On<br>><br>so]oO 23<br>BR 7<br>Le<br><2]<br>2 17<br>Sw<br>xe<br>Wwo 15 15<br>w 13<br>3 12 -<br>ge 11<br>To<br>¢<br>S 8 8<br>oO<br>a<br>3<br>o<br>=<br>3<br>a<br>Oo<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br><!-- End of picture text -->
where the globalization coefficients are interacted with a country _c_ specific dummy 1( _i_ = _c_ ) to capture for those country-specific globalization dynamics. This follows FrancoBedoya (2023) analysis of country-specific globalization dynamics and its impact on the estimation of RTA coefficients.

Second, as the global economy has become increasingly structured around global value chains (GVCs), intermediate inputs often cross borders several times along the production process. The implication is that gross trade flows from gross trade statistics no longer reflect the domestic value-added content trade. For this reason, we use other dependent variables that allow us to decompose bilateral trade flows into domestic valueadded and GVC-related trade terms, as follows: where _GVCij_ , _t_ are different GVC trade flows. Total trade, as used so far in our empirical strategy, is the sum of traditional and GVC trade. Traditional trade is the value of trade flows that only cross one border, being absorbed by the direct trade partner. GVC related-trade is the value of bilateral trade that crosses at least two borders, flowing along a supply chain. GVC-related trade can be further decomposed into three components, pure forward, pure backward, and two-sided, as explained by Borin et al. (2021). "Pure backward GVC related-trade" is the value of goods and services produced with imported inputs and exported to the final destination market. "Pure forward GVC related-trade" is the value-added in goods and services entirely generated within the domestic economy (by different sectors) exported to partners, which in turn, re-exports it to other markets. "Two-sided GVC-related trade" is the value of goods and services produced with imported inputs and exported to partners, which in turn re-export them to other markets. Finally, note that the domestic value-added content of trade is the summation of traditional trade and pure forward GVC-related trade. These distinctions are important to understand the impact of a trade agreement on economic activity and will allow for gaining deeper insights regarding the specific effects of CAFTA-DR.<sup>13</sup> 13We access this data generated by Borin et al. (2021), based on Eora, through the WITS website.

11
While Wesolowski, Erbach-Schoenberg, et al. (2017) look at seasonality of movement patterns across Kenya, Pakistan and Namibia, they only connect this theoretically to impact on disease and do not study the relationship with incidence data. Papers that have combined seasonal mobility data from mobile phones with seasonal disease incidence data, such as Wesolowski, C. Metcalf, et al. (2015) for rubella and Wesolowski, Qureshi, et al. (2015) for dengue, have not done so in a causal framework. This paper contributes to the existing work by aiming to measure the causal relationship and size of the eect of imported malaria using a linear dynamic panel-data model and controlling for time xed eects. Therefore, in addition to the two areas already identied by the economics literature as necessary for malaria reductionpricing and adoption of preventative and treatment interventionsthis paper identies targeting of higher risk mobile populations as a third.

While this paper focuses on malaria elimination, it has implications for other diseases whose spread has been associated with travel (Adda 2016, Oster 2012,Prothero 1977, Balcan et al. 2009, Stuckler et al. 2011, Tam, Khan, and Legido-Quigley 2016). Since travel patterns studied using cell phone data could lead to the transmission of any communicable disease, if these data are obtained for other countries or for dierent diseases, it is possible to replicate the analysis using the methods developed in this paper. I demonstrate how new sources of big data can be used to measure externalities associated with travel to develop more eective targeting strategies that can be combined with pricing and adoption policies. This further expands the use of big data for development in areas such as risk-sharing (Blumenstock, Eagle, and Fafchamps 2016), measuring poverty (Blumenstock, Cadamuro, and On 2015, Blumenstock 2016) and providing credit to the poor (Bjorkegren and Grissen 2018).

The paper begins by providing some background and describing the data. It then goes on to model the link between malaria and population movement in section 3. Section 4 outlines the empirical results linking travel to malaria and section 5 examines the cost eectiveness of dierent policies. Some robustness checks are provided in section 6, and the paper concludes with section 7.

5
Our precipitation and evapotranspiration indices depend on local climate only. However, the hydrological cycle in a region involves the continuous movement of water on, above, and below the surface of the Earth over time, so water available as an input to plant growth may depend not only on current local weather and soil conditions but also on their counterparts considerable distances away. Moreover, particularly within the context of the current article, a dam’s irrigation benefit is likely to depend on the available river flow. To capture this river flow within our basins, we employ the Geospatial Stream Flow Model (GeoSFM), which is a semidistributed, physically based hydrological model developed by the United States Geological Survey (USGS (2011b) with particular relevance for Africa’s hydrology (Asante, Macuacua, et al. 2007; Asante, Artan, et al. 2007).<sup>12</sup> More specifically, GeoSFM allows one to simulate the dynamics of runoff processes using spatial information on river basin and network coverage, land cover type, soil characteristics, and daily precipitation and evapotranspiration data. To satisfy the model’s requirements for soil characteristics (water-holding capacity, hydrologically active soil depth, texture, average saturated hydraulic conductivity), we take data from the Digital Soil Map of the World (FAO 2011) and use the procedure by Schuol and Abbaspour (2007) to derive daily precipitation and evapotranspiration series per river basin using the CRU climate data. Using these inputs in GeoSFM produces the daily stream flow in terms of cubic meters per second (m<sup>3</sup> /s), and we sum these to obtain annual equivalents. Importantly, GeoSFM is run solely with the time-invariant geographic characteristics of basins and time-varying climatic information as inputs and does not take into account the influence of flow through dam location and operation rules. Thus, it is the stream flow that would have occurred if water had been allowed to 25
# - 24 -

Reliance on centralized computers proved cumbersome, as property records had to be shipped over long distances. This particularly limited the use of CIATA's post-project data processing services for updating tax rolls and issuing annual bills. The advent of mini- and micro-computer technology has permitted CIATA to decentralize data processing. In larger jurisdictions, CIATA now encourages municipalities to purchase their own computers. Smaller jurisdictions are encouraged to share regional computers, through individual terminals linked through telephone lines.

4.22 The decentralization of data processing has enabled CIATA to expand in scope. Under the earlier, centralized mainframe technology, the use of computers to record payments and monitor collection enforcement was impractical. The increasing availability of computers at the municipal level has made the computerization of treasury operations technically feasible, and corresponding technical assistance components are now made available under the CIATA program.

# D. Impact

4.23 CIATA's principal objective is to produce a comprehensive valuation of the tax base in each project municipality. In general, a CIATA project would be expected to increase the aggregate value of the tax base. Two separate sources of growth can be distinguished. First, a project would be expected to increase the number of properties on the tax rolls through the discovery of mi ;sing properties and expansion of the zone subject to property taxation. Se.ond, it would increase average valuations by producing more accurate information on the physical characteristics of each property and by employing more recent market data to convert property characteristics to estimates of value.

4.24 CIATA clearly has the former impact. CIATA projects consistently and substantially increase the number of properties on the tax rolls. The evidence from a representative sample of CIATA projects is summarized in Tables 5A and 5B. The tables show the results of 241 projects in four project years: 1977, 1978, 1983 and 1984.31 For purposes of comparability, projects are divided between those in the less industrialized North/Northeast and those in the rest of Brazil (termed "south" in the following tables).

# Projects in 1977 and 1978

4.25 As shown in the first row of Table 5A, CIATA projects undertaken in 1977 and 1978 in northern Brazil increased the number of properties on the tax rolls by an average of 25 percent. In southern Brazil, the impact was even greater. Projects in 1977 doubled the number of properties on the rolls; those in 1978 increased the number of properties by about 50 percent.

_DJ Figures for 1977 and 1978 projects are based on 100 percent of observations; figures for 1983 and 1984, on a 33 percent sample. Figures for each project are listed In Annex 3._
**Figure 18: Resolution of SIFIs**

<!-- Start of picture text --> 100<br>73<br>67<br>53 55<br>50<br>47<br>33 33<br>29 29<br>18 1717<br>10<br>0 0 0 0 0 0 0 0 0 0 0 0<br>EU CE & BC WB SC CA Russia Turkey EE RoW<br>Different processes for resolving SIFIs<br>Banks required to file resolution plans<br>Formal supervisory and regulatory framework for the resolution of international banks<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW for** the rest of the world **.** Finally, it is important to note that some of the largest banks in the region are partially or wholly owned by the state. Hence, there is a concern that resolution procedures may not be fully implemented due to political and public pressures. Moreover, the resolution rules may also not have the intended effect of strengthening market discipline as bank managers and investors expect the governments to step in and rescue these institutions that they perceive to be too-important to fail. By the same token, there are challenges that arise in introducing the core bank resolution building blocks proposed after the GFC. For instance, in the absence of well-developed financial markets in many ECA countries, there is no market for contingent convertible debt that can turn into equity if a pre-specified financial stress occurs (Fontán et al., 2019; GFDR 2019/2020).

# **IV Conclusion**

The global financial crisis revealed significant weaknesses in capital regulations that were in place prior to the crisis. Financial institutions lacked high-quality capital to weather the crisis, resulting in government support to rescue these financial institutions. After the GFC, several reforms have been implemented to strengthen capital requirements and to address risk posed by systemically important financial institutions.

22
Figure 2.2. Macro indicators: base scenario annual real growth 2014-2030 (%) <!-- Start of picture text --> Absorption<br>Consumption - private<br>Consumption - government<br>Investment - private<br>Investment - government<br>Exports<br>Imports<br>GDP at factor cost<br>4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6<br><!-- End of picture text --> Figure 2.3. Poverty and under-five mortality for base simulation <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>U5MR - rural (‰) U5MR - urban (%)<br>Poverty - rural (%) Poverty - urban (%)<br><!-- End of picture text --> 10
- 12 -

which is referred to as energy intensity—varies among countries. It depends on the economic and industrial structure. High energy intensity of the economy results from inefficient consumption by not only industries but also households and heating load of the building sector. Serbia is estimated to use three times more electricity than more developed neighboring countries (Figure 5). Bulgaria and Macedonia also seem to be using electricity quite intensively. Croatia is the least energy-intensive economy in the SEE region; only 0.66 kWh is required to produce $1 of GDP. The ECA average (only low- and middle-income countries) is about 2 kWh per GDP.

The observed difference in energy intensity is also partly attributed to the difference in the economy’s production and export structure. In particular in the SEE region, several energyintensive industries are located, such as cement and copper in Albania, steel and zinc- and copper-based metallurgical production in Bulgaria, metal-processing in Macedonia, and aluminum in Montenegro. These industries were often established for political reasons during the Soviet era, and their facilities tend to be out of date and inefficient. Still, they are often playing an important role in production and exports of the economy. Macedonia’s non-metal minerals, iron and steel products account for 18 percent of total exports (Figure 6).<sup>12</sup> Figure 5. Electricity intensity, 2005 <!-- Start of picture text --> 4.0 3.65<br>3.5<br>3.0<br>2.5<br>1.96<br>2.0 1.81<br>1.5 1.35<br>1.04 0.95 1.04<br>1.0 0.77 0.66<br>0.5 0.33<br>0.0<br>1/ 2003 for Serbia.<br>Electricity intensity of the economy (kWh/GDP) Albania Bulgaria Croatia FYR Romania Serbia 1/ Czech Republic Greece Slovak Republic<br>Bosnia and Herzegovina Macedonia,<br><!-- End of picture text --> Source: Author’s calculation based on _WDI_ and IAEA _Energy and Environment Data Reference Bank_ .

12 These export items are classified under the SITC Code 66 non-metal mineral, and 67 iron and steel.