rafmacalaba/datause-encoder-data huggingface dataset:
24

|Country,<br>L<br>economy, or<br>territory|ast<br>lu<br>P<br>Date|btished<br>censu<br>opulation<br>(1000)*|s<br>Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, unless indicated)|Source for mortality<br>(1985-90, unless indicated)|
|---|---|---|---|---|---|
|Thailand|Apr 80|44825 F|Official<br>est., National<br>Economic and Social<br>|Official<br>est., National<br>Economic<br>and Social|Official<br>est., National<br>Econfmic<br>and Social|
||||Development<br>Board|Development<br>Board|Development Board|
|Togo|Nov 81|2705 F|Bank projection from census|Bank assessment<br>of 1961<br>and 1971 censuses|U.N. 1988 revision (prelim.|
|Tokelau|Oct 82|2 F|UNPVSR<br>4/87 (official<br>est.)|--||
|Tonga|Nov 76|<br>90 J|UNPVSR<br>4/87 (official<br>est.)|Bankest.|Bank est.|
|Trinidad**and**|May80|1080 F|U.N. 1988 revision (prelim.)|Based on 1987DHS|1980-85, U.N. 1984|
|Tobago|||||assessment; 1985-90,<br>Bank projection|
|Tunisia|Mar84|6966 F|Bank projection from census|Bank est.|BasedonU.N. 1984<br>assessment|
|Turkey|Oct 85|50664 F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessmen.|
|Turks andCaico<br>Islands|sMay80|7 F|UNPVSR<br>1/88 assessment|||
|Tuvwlu|May 79|<br>7 F|UNPVSR<br>4/87 (official<br>est.)|Bank est.|Bank est.|
|Uganda<br>|Jan 80<br>|12637 F<br> <br>|Official<br>eat.<br>|U.N. 1988 revision (prelim.)|Bankest.|
|United Arab<br>Emirates|Dec 80|<br>1043 F|U.N. 1988 revision (prelim.)|BasedanU.N. 1988 revision<br>(prelim.)|Based on U.N. 1984<br>assessment|
|United Kingdo|m Apr 81|55678 F|Eurostat 1987|Eurostat 1987|Eurostat 1987|
|United States<br>of America|Apr 80|226546 F|UNPVSR<br>1/87 (official<br>est.)|Based on 1981-84 TFR, USBOC<br>1985, and 1985 CBR, UNPVSR<br>4/87|Based on UNPVSR<br>4/81|
|Uruguay|Oct 85|<br>2931 F|Bank projection<br>from<br>official<br>1986 est.|1980-85,<br>Celade printout<br>2/87; 1985-90, Sank<br>projection|1980-85,<br>Celade printout<br>2/87; 1985-90, Bank<br>projection|
|U.S.S.R.|Jan 79|262436 F|U.N. 1984 assessment|Based an INED 10/87|Based on official<br>1984-85<br>life table|
|Vanuatu|Jan 79|<br>111 F|USBOC 1985|Bank est.|Bank est.|
|Venezuela|Oct 81|<br>14517 F|Official<br>est.|1980-85,<br>U.N. 1988 revision<br>(prelim.);<br>1985-90, Bank<br>projection|<br>1980-85,<br>U.N. 1988 revision<br>(prelim.);<br>1985-90, Bank<br>projection|
|Viet Nan|Oct 79|52742 F|Bank projection fromcensus,<br>adjusted for 0.962 undercount|Bank est.|Bank est.|
|Virgin Islands<br>(U.S.)|Apr 80|97 J|USBOC<br>1985|Based on USBOC<br>1985|Based on USBOC<br>1985|
|Uake Island|Apr 70|<br>2 F|UNPVSR<br>4/87 (official<br>est.,|Bank est.|Bank est.|
|Uallis<br>and<br>Futuna|Mar 69|<br>9 F|USBC 1985|||
|Western<br>Sahara|Dec 70|<br>76 F|UNPVSR<br>4/87|--|-.|
|Western Samoa|<br>Nov 81|156 F|U.N. 1984 assessment|BasedanUSBOC<br>1985|USBOC 1985|
|YeTenArab Rep.|Feb 86|9274 F|Bank projection from|Bank assessment ofWorld|Bank assessment ofWorld|
||||6/80 census|Fertility Survey|Fertility<br>Survey|
when we consider the scenarios with half the available budget or 75% of the official poverty line.

# **VI.  Discussion**

In this paper we have used a “Poverty Map” produced in Morocco to simulate the impact on poverty of transferring an exogenously given budget to geographically defined subgroups of the population according to their relative poverty status. We have asked to what extent effectiveness of targeting in reducing poverty improves as we define these sub-groups at progressively lower levels of spatial disaggregation.

We have found large gains from targeting smaller administrative units, such as provinces, communes or districts. We have shown that the benefits from targeting are particularly clearly discerned when expressed in terms of budgetary savings of achieving a given rate of poverty reduction.

Our assessment of targeting performance has been based on an optimal use of estimates from poverty maps. There might be grounds for concern that the design of transfer schemes based on such optimized routines suffers from lack of transparency and would be difficult to describe in simple terms. Governments may consequently not be able to apply such schemes in practice. We have considered, therefore, an alternative transfer scheme, based on a naïve, non-optimal use of the poverty map. We have found that while this naïve scheme does not achieve the same success as the optimal targeting scheme, its performance remains surprisingly good in rural areas. On the other hand, we saw that in urban areas, the naïve scheme we implemented can perform less effectively than even a uniform lump-sum transfer. To the 20
# **1 Introduction**

The COVID pandemic was a major shock to the global economy. Global trade declined dramatically at the onset of the pandemic (Brenton et al., 2022) but trade bounced back sharply from late 2020 onward (Meijerink et al., 2020). Surveys of firms around the world, namely the World Bank’s Business Pulse Surveys, documented their responses to the COVID shock including their access to and use of government support programs (e.g., Constantinescu et al., 2022; Cirera et al., 2021). Despite the key role of government agencies that promote – trade Trade Promotion Organizations (TPOs)– highlighted in a recent survey by Srhoj et al. (2020), little is known about the impact of the pandemic on their activities. To address this gap, the World Bank implemented a survey of TPOs in 57 countries between fall 2021 and spring 2022 with the objective of better understanding their responses to a crisis such as the COVID pandemic as well as their functioning more broadly.

The early literature in the 1990s was critical of TPOs’ capacity in developing countries to promote exports (Hogan et al., 1991), as they tended to be inadequately funded, lacked leadership, and were too bureaucratic and not client oriented, while hiring staff with links to the government instead of the export sector. However, as TPOs reformed, the more recent literature suggests that they can successfully promote exports when sufficiently funded (Lederman et al., 2010), or when focusing on firms’ extensive rather than intensive export margins, both at the market, product and exporter/non-exporter levels (Volpe Martincus and Carballo (2008), Cruz (2014) and Broocks and van Biesebroeck (2017)). TPO returns are also higher when they spend a large share of their budget on marketing activities in a few sectors (Olarreaga et al., 2020), or when TPO activities are combined with investment promotion (Harding and Javorcik, 2012).

Randomized experiments at the firm level have also shown that TPO programs can have large returns. Atkin et al. (2017) offer the opportunity to export high-quality carpets to a random set of firms. Treated firms had a 20 percent increase in exports and a significant boost to the quality of their products.

There is also evidence that TPOs have been successful in helping exporters weather the 1
Tinker, Taper, QE, Bye? The Effect of Quantitative Easing on Financial Flows to Developing Countries Jamus Jerome Lim, Sanket Mohapatra, and Marc Stocker<sup>_∗_</sup> Keywords: Gross financial flows, quantitative easing, developing countries JEL Classification: F21, F32, O19

> _∗_ The World Bank. This paper served as a background paper for the World Bank’s _Global Economic Prospects_ , 1st half 2014, report. We thank, without implicating, Andrew Burns, David Gould, Sergio Schmukler, and Luis Serv ́en for valuable comments that substantially improved the quality of the work. All errors remain firmly our own. The findings, interpretations, and conclusions expressed in this article are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
|ShamsAca<br>Sa")|4Adc_b<br>~~~~~~~~~<br>(1)3)|Emeq<br>~~|Pw_cedFood|_UL_<br>Inui<br>(4)|HaVyb1hufty<br>mS|_OLMmaf._<br>(6|Trnpoet<br>(7)|Cnmmwe<br>m|Snies<br>(9)|No-mahet<br>(!°)|Ws Vahe<br>(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
||29|6|is|6|4|2|13|9|10|3|794.2|
|VFl<br>|37|1|4<br>|2<br>_|||19|14|15|_7|324.9|
|EBipoysn<br> <br>|24|1|3|6|S|2|9|is|23|9|2U42|
|Capit<br>Stock|45|1|4|1|0|1|17|16|16|0|2364|
|ExPort|74<br>|_|3|S|0|0|11|0|2|0|55.9|
|wmpts|2|25|12|16|20|3|10|2|10|I|73.9|
|a<br>_enu<br>Oncverae<br>Rovew_|24<br>__|6<br>.,|3|24<br>__|31|5|3|2|2|O|331.01|
|ImportTax|3|9|3|30|46|S|O|O|O|O|1562|
|Expon Tax|_<br>__<br>10_0|n_|o_|o|o|o|o_ooo||||43.t8|
|Vab<br>T-<br>d<br>x<br>|40|2|4|2|1|1|20|15|16|O|403|
|Excite**Tax**|O|O|O|60|40|O_|O|O|O_O_2t|.||
|Easticites:||||||||||||
|ImpornDeman<br>|0.9|0.6|0.7|1.2|0.6|0.9|0.6|0.6|0.6|0.4||
|ExponSupply<br>|1.0|0.6|1.0|1.2|0.6|0.6|0.6||0.6|_<br>__||
|ConrumerDemand|-0.4|-0.3|-0.4|40.5|-0.2|-0.2<br>||0.4|-0.3<br>||<br>0.4|-0.2||
|Income;aEbsticity<br>|0.8|1.2|0.8|1.4|0.6|0.8|1.2|I|1.4|1.6||
|Capitul Labor<br>Substitution<br>Elasticity|0.8|0.8<br>I___I_I|0.8|0.|0.8|0.8|0.8|0.8|0.8|0.8||

_Notes: All_ etries except elasticities amr ectoral perenages of the corresponding variable (e.g. agricuture's re ia goss output is 29 percent).

a. All values ia rows I to 6 **are** 10 billion FMa except employment (1,000 man yer). AHI tax collection data re in billion FMG.

b. The difference betwea aectorml tax revenue (269.4 billion FMG) and toal govenmet revenue is accounted for by the wage tax (14.4 billion FMG), the profits tax (22.7 bilion FMG). the income tax (12.3 billion FMG) **and** the agicultal monopoly indirect tax (24.8 billion FMG), all of which are icludd in the model.
FUNDING subsample. These results suggest that high reliance of countries on foreign funding is associated with stronger response of PRIVATE CREDIT to FOREIGN FUNDING. Again, the results are in line with the hypothesis that high reliance on foreign funding is responsible for explaining the difference between the ECA and non-ECA samples.

Lastly, we split our full sample using FOREIGN OWNERSHIP. Based on the latest pre-crisis data, we create high and low FOREIGN OWNERSHIP subsamples. The impulse-response results are presented in Figure 6 and the variance decompositions are presented in Table 7, Panel B. We find that the difference between the responses of PRIVATE CREDIT to a FOREIGN LIABILITIES shock in the high and low FOREIGN OWNERSHIP samples are not statistically significant (row 3). The variance decompositions show that in the high FOREIGN OWNERSHIP subsample a FOREIGN LIABILITIES shock explains 10.0% of the variation in PRIVATE CREDIT while it explains 9.1% in the low FOREIGN OWNERSHIP subsample, which is not materially different. These results suggest foreign ownership itself is not associated with a stronger response of PRIVATE CREDIT to FOREIGN LIABILITIES, in contrast with high LDR and high FOREIGN FUNDING. Therefore, we find no evidence that foreign ownership _per se_ is responsible for the observed differences between ECA and non-ECA countries in their impact of a FOREIGN LIABILITIES shock on PRIVATE CREDIT.

Taken together, our findings suggest that high LDR and high FOREIGN FUNDING in ECA have significantly contributed to the difference in responses of PRIVATE CREDIT to FOREIGN LIABILITY shocks in ECA vs. non-ECA countries. In contrast, we do not find that high FOREIGN OWNERSHIP drives the difference. This implies that the funding model of banks in ECA is at the heart of ECA’s vulnerability to external financial shocks rather than the strong presence of foreign banks in the region _per se_ .

# _E. What factors explain differences of ECA with the rest of the world?_

Our previous results suggest that the differences in the impact of FOREIGN LIABILITY shocks on PRIVATE CREDIT between ECA and other regions are not driven by differences in FOREIGN OWNERSHIP. Instead, these differences seem to be driven primarily by the lack of a 25
23 To investigate the issue further, we look at the consistency of the poverty status of the household with other welfare indicators. We observe that the national poverty line leads to a higher and stronger correlation. For example, the correlation coefficient for access to safe drinking water varies over the three surveys in Mozambique from ρ = 0.25 to 0.28 as compared to ranging from 0.12 to 0.17 under the urban-rural line, while in Ghana ρ = 0.35 instead of 0.22. In the case of Mozambique, child anthropometrics are part of the 1997 and 2009 survey data, shedding direct light on the povertynutrition nexus. The correlation between poverty status of the household and the presence of stunted children is ρ = 0.15 in 1997 and ρ = 0.08 in 2009 under the national poverty line and falls to 0.07 and 0.06 when poverty rankings are based on the urban-rural line.

The table also shows that adding specificity to the poverty line beyond the rural-urban dimension only aggravates the loss of consistency. For example, the poverty patterns under the spatial domains CSPL indicate that the rural dimension largely disappears. Likewise, the correlation between poverty and stunting in the Mozambique survey falls from 0.15 to 0.06 in 1997 and from 0.08 to 0.02 in 2009. The implication that households, whether poor or not, would be practically equally vulnerable to child malnutrition is counterintuitive.

For the case of Mozambique, a study by Tarp et al. (2002) addresses the same issue, namely the robustness of poverty patterns for the choice of the poverty line. Our results are in line with that paper, although the authors pay little attention to the fact that the association between poverty on the one hand and other provincial-level welfare indicators like child malnutrition and asset ownership on the other hand appears particularly weak (Tarp, op. cit., Table 9 and Table 10).

In summary, the findings in this section concur with the arguments in section 2 and section 3. The high elasticity of calorie prices in Table 2 indicates that the poor try to move along the calorie-constraint to a more balance diet within the limits of their poverty condition, rather than opting for more calories in the prevailing diet. Hence, as argued in section 2, observed diets with low cost calories tend to express poverty rather than a preference for the chosen foods. Likewise, Table 3 shows that one cannot expect a context-specific poverty line that is adjusted to the consumption and price pattern of specific population groups to be an improvement over a national line that applies to all population groups. As discussed in section 3, the better performance of a national unadjusted poverty line over an urban-rural line can partly be explained from the trade-off between a relative affordability of basic foods in rural areas and a relative affordability of basic services in urban areas.
(National Academies of Sciences, Engineering, and Medicine, 2018). For example, this type of modeling promotes cognitive development (Bandura, 1986; Brainerd, 1978; Rosenthal & Zimmerman, 1978). Hattie (2009) synthesized over 800 meta-analytic studies of various designs and found that learning activities where the teacher is actively involved with the lesson (e.g., works through the problem step-by-step with students) are particularly effective at promoting learning gains in students compared with activities where the teacher is less involved (e.g., teacher instructs students to complete a problem sheet independently). Beyond student gains, modelling is a means of promoting students’ self-efficacy and self-regulation (Schunk & Zimmerman, 2007), which is central to students’ learning process (Checa, Rodríguez‐Bailón, & Rueda, 2008).

Enacting involves showing students the steps needed to complete a task. For example, effective teachers use worked examples (e.g., in mathematics, the teacher provides a step-bystep solution to a problem), which allows students to focus on understanding the logical stepby-step process to solving the problem without the cognitive burden on their working memory (Rosenshine, 2012).

Further, by thinking aloud and walking students through thought processes, students are then able to take a similar approach to solving similar problems on their own. The thinkaloud strategy has been found to be particularly effective at promoting reading comprehension (Davey, 1983; Schon, 1987; Tishman, Jay, & Perkins, 1993), as it helps learners understand what the text really means and provides them with strategies that can be used to answer comprehension questions (Duke & Pearson, 2009; Durkin, 1978; Ortlieb & Norris, 2012; Parker & Hurry, 2007).

# **3.3.2. Checks For Understanding**

One of the most salient characteristics of effective teaching is to recognize the difference between teaching a new concept and students’ actual understanding of that same concept (Lemov, 2015). Teachers play an important role in checking whether and how much students understand the learning material. Strategies that teachers can apply to check student learning as they teach are described below. As such, _Teach_ included three specific teacher behaviors that gauge whether the teacher is effective at checking for students’ understanding: 20
peri-urban (“Ger”) areas is allowed in Mongolia in the form of long-term possession rights (up to 60 years with a possible one time extension of up to 40 years). However, transfer of possession rights is constrained by the need for permission from the relevant authorities. The rules and regulations for this are, however, not yet well established or published. This leads to lack of transparency in land allocation decisions in the urban and peri-urban (“Ger”) areas (Sommer 2003).

**Figure 6:  Firms’ access to credit in Mongolia**

<!-- Start of picture text --> All firms<br>Loan 100%<br>Maturity of<br>1 year With a loan Without a loan<br>27%<br>27.9% 72.1%<br>Loan<br>Maturity > Applied Did not apply<br>5 years<br>0.9% 4.0% 68.1%<br>Did not need<br>Why? Discouraged a loan<br>Lack Low return<br>Collateral To capital? 25.9% 42.2<br>3%l 1%<br>Why? Why?<br>High cost of Collateral Low return<br>Capital To capital?<br>22.0% 18.7% 42.2%<br><!-- End of picture text --> _Source: Mongolia Productivity and Investment Climate Survey (PICS) 2004._ While the cost of capital is high and access to capital, especially long term financing, is limited, most of the firms that needed a loan got it. According to the 2004 Productivity and Investment Survey (PICS) 56 percent of Mongolian firms interviewed for the survey complained that the cost of capital was a severe obstacle to business growth, but only 22 percent of the firms in the survey were discouraged and did not apply for a loan because of the high cost of capital (Figure 6).<sup>29</sup> Similarly, the survey results on access to capital suggest that while 42 percent of firms claimed that access to credit was a severe obstacle, 70 percent of the firms either obtained a loan (28 percent of firms) or did not need a loan (42 percent of the firms) (Figure 6).

Given the large number of firms that do not need to borrow at all, it follows that the high cost of capital is not the reason for the low private investment outside mining and construction. What are the reasons for this? To answer this question we turn to an assessment of returns to private investment in the non-mining related sectors in Mongolia.

> 29 Of those that were discouraged the main reason they gave for not applying was the high cost of capital (82.8 percent or 22 percent of all survey respondents), but they also gave additional reasons which included stringent collateral requirements (72 percent), cumbersome application procedures (59 percents), and perceived corruption in the allocation of credit (15 percent).

16
with similar results. Gross et al. (2005) analyze the case for a sample of Japanese outward investment projects to Europe. They demonstrate how Japanese FDI in manufacturing attracted other Japanese FDI in services, among others, but they also show that the pattern of inter-sectoral linkages in FDI changed substantially over time.

Finally, the paper is also conceptually connected to a series of papers containing findings of what we would label state-dependency in policy effectiveness. Aghion et al (2008) find that the effect of de-licensing in India depends on the institutional framework of different states. Auerbach and Gorodnichenko (2012) claim that the fiscal policy multiplier (the impact of fiscal policy on output) is highly dependent on whether the economy is experiencing a period of expansion or a recession. Aghion et al (2013) find that the effects of product market reform on innovation depends on the importance of the level of protection of intellectual property rights.

The paper is structured as follows. Section 2 describes the data and in particular discusses the new policy information drawn from the Services Trade Restrictions Database. Section 3 outlines a stylized model of cross-border M&A. Section 4 describes the empirical strategy, while Section 5 contains the empirical results. Finally, Section 6 concludes.

# **2 Data description**

## **2.1 Cross-border Mergers and Acquisitions Data**

We use a comprehensive dataset on global mergers and acquisitions from ThomsonReuters Platinum database, spanning the period 2003-2009.<sup>2</sup> The dataset consists of individual crossborder equity deals between the home country of the acquirer and the host country where the target firm is domiciled. A deal’s sector affiliation is determined based on the target firm’s SIC classification.

> 2We focus on the M&A part of investment flows for two reasons. First, modeling the choice between M&A and Greenfield as a mode of investment goes beyond the scope of this paper, though we plan to investigate this important aspect in future work. Second, the best Greenfield data with global coverage that is currently available are not of a quality comparable to the M&A data used here. In addition, its sectoral breakdown is less detailed and often is defined differently than in the M&A data.

5