rafmacalaba/datause-encoder-data huggingface dataset:
- We now know much more about the developing world, including how poor it is.  For example, the World Bank’s first estimate of the “$1 a day” poverty counts in 1990 used data from only 22 national household surveys for 22 countries; today the estimates use 700 surveys for 115 countries.  An interactive software platform, _<u>PovcalNet</u>_ <u>, provides</u> free access to the data derived from these surveys, allowing users to replicate the Bank’s global poverty counts and make their own estimates under different assumptions (such as alternative poverty lines).

- The _World Development Indicators_ has emerged as a central data resource.  Key development data are now available through Google.  One important change in April 2010 has been to provide these data free of charge to all users through the _Open Data_ website.

As a result of the Bank’s research and data collection, _and_ the efforts of Bank researchers and their colleagues to promulgate the findings and lessons of that research, the global community knows more than in the past, and is better placed to confront theory with evidence. Past development debates involved wide swings in philosophy and theories and, as a result, the Bank also experienced major shifts in terms of research priorities.<sup>5</sup> Pronouncements on policy are now more firmly grounded in market, governmental and institutional realities. A consensus has evolved among development economists that there is no simple recipe for development, applicable to all countries and situations. Markets and states need to complement each other, in a more pragmatic approach that recognizes the diversity of situations and confronts policies and programs with close empirical scrutiny. Similarly, economy-wide development policies and sector-based policies to enhance efficiency and inclusiveness need to be complementary.

This shift toward more empirically-grounded development research, while still reflecting the need to formulate coherent and testable theory, is the hallmark of current development research. To achieve these goals, World Bank researchers must challenge themselves as well as reach out to other disciplines and practitioners in the field. This includes working with experts in

> 5 Jean-Jacques Dethier, “World Bank Policy Research:  A Historical Overview.” Policy Research Working Paper 5000, World Bank, Washington, DC, 2009.

8
Table 5: Growth elasticities of food poverty reduction across municipalities, 1992–2014

||**(1)**<br>**1992-2014**|**(2)**<br>**1992-2000**|**(3)**<br>**2000-2014**|**(4)**<br>**2000-2005**|**(5)**<br>**2005-2010**|**(6)**<br>**2010-2014**|
|---|---|---|---|---|---|---|
||||**a. All mu**|**nicipalities**|||
|_gi_(_yit_)|_−_1_._425***<br>(0.089)|_−_1_._291***<br>(0.072)|_−_1_._671***<br>(0.083)|_−_1_._504***<br>(0.114)|_−_1_._472***<br>(0.082)|_−_1_._736***<br>(0.077)|
|Obs.|2,361|2,361|2,361|2,361|2,361|2,361|
|R<sup>2</sup>|0.535|0.411|0.517|0.432|0.427|0.549|
||||**b. Urban m**|**unicipalities**|||
|_gi_(_yit_)|_−_1_._513***<br>(0.109)|_−_1_._348***<br>(0.091)|_−_1_._736***<br>(0.092)|_−_1_._620***<br>(0.133)|_−_1_._457***<br>(0.102)|_−_1_._751***<br>(0.094)|
|Obs.|944|944|1,017|1,017|1,022|1,022|
|R<sup>2</sup>|0.540|0.391|0.511|0.456|0.400|0.521|
||||**c. Rural m**|**unicipalities**|||
|_gi_(_yit_)|_−_1_._142***<br>(0.041)|_−_1_._018***<br>(0.052)|_−_1_._210***<br>(0.150)|_−_0_._942***<br>(0.060)|_−_1_._482***<br>(0.067)|_−_1_._680***<br>(0.111)|
|Obs.|1,417|1,417|1,344|1,344|1,339|1,339|
|R<sup>2</sup>|0.599|0.488|0.580|0.312|0.540|0.686|
|||**d. Municipa**|**lities in stat**|**es along the **|**U.S. border**||
||_−_1_._878***|_−_1_._112**|_−_1_._837***|_−_1_._276***|_−_1_._551***|_−_1_._904***|
|_gi_(_yit_)|(0.308)|(0.531)|(0.166)|(0.371)|(0.255)|(0.175)|
|Obs.|267|267|267|267|267|267|
|R<sup>2</sup>|0.501|0.127|0.600|0.201|0.339|0.660|
|||**e. Munic**|**ipalities in n**|**on-U.S. bord**|**er states**||
||_−_1_._298***|_−_1_._263***|_−_1_._434***|_−_1_._436***|_−_1_._324***|_−_1_._698***|
|_gi_(_yit_)|(0.065)|(0.077)|(0.095)|(0.143)|(0.074)|(0.092)|
|Obs.|2,094|2,094|2,094|2,094|2,094|2,094|
|R<sup>2</sup>|0.587|0.464|0.453|0.435|0.450|0.516|

_Source_ : World Bank calculations. _Note_ : The table presents estimates of the parameter _η_ in equation (5), weighted by municipal population at the initial year of each period under study. The dependent variable is the annualized growth in the food poverty headcount ratios of municipalities over the period. _gi_ ( _yit_ ) are the annualized changes in mean per capita income at the municipal level over the period at August 2014 prices. Urban (rural) municipalities are defined as those with more (fewer) than 15,000 inhabitants. The intercepts are shown in table 32 in the ancillary file. Robust standard errors are in parentheses. *** _p < ._ 01, ** _p < ._ 05, * _p < ._ 1 should have experienced higher subsequent rates of poverty reduction over the period under study. To test this, let 23
# **7 Conclusions**

Using a newly assembled dataset that does not suffer from fundamental limitations of FDI statistics, and guided by a simple analytical framework that highlights the possibility of implicit profit shifting through real investment decisions, the purpose here has been to revisit the questions of the extent to which, and through precisely which channels, international corporate tax arrangements affect aggregate cross-border tangible investment. Addressing that has also enabled an analysis of what the implications for tangible investment might be of what is likely the most profound of the fundamental reforms of the international tax architecture now underway: the adoption of a global minimum corporate tax.

There emerge effects of statutory tax rates on tangible investment that, across a wide range of specifications, are strongly significant and large relative to prior consensus values. Moreover, spillover effects from tax rates elsewhere appear to be about as large as the investment effects of the host country’s own tax rate. Our preferred estimated semi-elasticity, with respect to both the host statutory rate and the weighted average of rates elsewhere, if pressed to provide one, would be around 3.6. This is noticeably higher than many previous estimates. The reason, we have argued, is that a dampening of apparent tax effects is an inherent consequence of the conduit arrangements that are reflected in the FDI data that have commonly been used, but which are not present in FAI statistics. These effects from statutory rates, moreover, are consistent with the relatively little recognized channel of implicit profit shifting through tax-induced changes in real investment decisions. Indeed effects through this route appear empirically to dominate any associated with profit shifting by means of artificial arrangements lacking real substance, which have received much more attention both in the literature and from policy makers. Spillover effects from statutory rates appear, as one might expect, to be stronger within the relatively integrated market of the EU, but they are nonetheless also marked outside.

The impact of the traditional closed economy King-Fullerton marginal effective tax rate, in contrast, is weak at best. This is more in line with previous findings, though it also tends to refute the idea that the composite effective rate derived in the theoretical part above (the _I_ - _METR_ ) can serve as a sufficient statistic for tax effects on inward FAI. Interaction 41
# **1 Introduction**

One of the reasons for a country to join a trade agreement is the potential welfare gains from trade.<sup>1</sup> Trade agreements may also foster deeper ties among the partnering countries, which may further amplify the welfare gains from trade.<sup>2</sup> On the flip side, do we expect larger welfare loss when countries with closer ties withdraw from existing trade agreements or face higher tariffs? Quantifying the potential impacts of existing trade agreements requires detailed knowledge of how trade may respond to trade policy changes given the specific trading relationship of these partnering countries in different industries. In other words, to study the trade and welfare impacts of existing trade agreements, we need partner country specific trade elasticities. These estimates do not exist.

The objective of this paper is to fill the gap in the literature regarding the productlevel trade elasticities with bilateral variations. As an application, this paper studies a hypothetical No-Deal Brexit. We first estimate the detailed bilateral trade elasticities of the European Union (EU) with respect to imports from its top 100 trading partners, including the United Kingdom (UK), at the HS 6-digit level.<sup>3</sup> We then evaluate the welfare and trade impacts, based on these trade elasticity estimates. While the actual Brexit is governed by the Trade and Cooperation Agreement between the EU and the UK, which came into effect in 2022 and provides for free trade in goods, findings based on a No-Deal Brexit are still informative as they provide a clean policy experiment to isolate the impacts of sudden tariff hikes on trade and welfare losses of countries that are closely linked through existing

> 1Standard trade models have emphasized that when countries specialize in producing and exporting products according to their comparative advantage, there may be efficiency and consumption gains. See Krugman, Obstfeld and Melitz (2022) for a textbook treatment.

> 2See World Bank (2020) and Fernandes, Kee and Winkler (2022).

> 3In this paper we estimate import demand elasticities, which is the percentage change in quantity imported with respect to a one percent change in import price. The (negative) import demand elasticity equals trade elasticity when prices and quantities of other products are held fixed. See Section A1 of the Appendix for the proof. Throughout this paper, we use the terms import demand elasticities and trade elasticities interchangeably when there is no confusion.

1
therefore to infer with confidence that our results are not driven by spatial sorting biases in the 2001 dataset.

Table 17 shows the impact of violence on school attainment in 2007 for the sample of nonmigrant individuals. The coefficients in columns 1-3 do not significantly differ from estimates in table 10. Our results of the effects of the 1999 violence on school completion in 2007 do not seem therefore to be driven by a process of spatial sorting (i.e. are not driven by those that migrated). Columns 4-6 of table 17 show slightly weaker results than those in table 11 and 12, but the main coefficients do not change substantially.

# **6. Conclusions**

The aim of this paper was to examine the effects of the conflict in Timor Leste on educational outcomes among boys and girls exposed to the violence that took place in 1999 following the withdrawal of Indonesian troops from the territory. We analyzed the short-term impact on primary school attendance and grade deficits in 2001, and the longer-term impact on school completion for cohorts of children of primary school age in 1999 observed in 2007. We also analyzed the impact of peaks of violence in the 1970s and 1980s on schooling outcomes in 2007 (among those that were of primary school age at the time of the various violent events), as well as the overall impact of the conflict. This has enabled us for the first time to compare the impact of violent conflict on educational outcomes in the short, medium and long terms.

The conflict in Timor Leste has led to considerable adverse impacts on educational outcomes among children exposed to the violence, boys in particular. We find that the impact of the conflict on girls‘ education is statistically insignificant in most cases, and positive in some cases. Because girls‘ educational outcomes were catching-up to boys‘ education before 1999, we interpret this result as suggesting that the catching up process continued once the 1999 conflict was over. The 25 years of violent conflict had, in contrast, a clear negative impact on the education of boys in Timor Leste. This result is consistent for different peaks of violence throughout the conflict in Timor Leste: the education of boys was more negatively affected by peaks of violence in the earlier stages of the conflict, by the violent events in 1999 and on average throughout the overall conflict. Generations of young Timorese boys have therefore experienced considerable reductions in their accumulation of human capital, which may well now be reflected in increases in insecurity, unemployment and violence in the territory since 2006.

We believe this impact of violence on boys‘ education reflects a potential household investment trade-off between education and economic survival, where boys would have been 32
-57 -

_45._ Hatzakis A, Katsouyanni K, Kalandidi A, Day N. Trichopoulos D (1986) Short-term effects of air pollution on mortality in Athens. Int J Epidemiol 15: 46. Charpin D, Kleisbauer JP, Fondarai J, Graland, Viala A, Gouezo F (1988) Respiratory symptoms and air pollution changes in children: The Gardanne coal-basin study. Arch Environ Health 43:2227.

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48. Bates DV, Sizto R (1987) Air pollutionand hospital admissions in southern Ontario: The acid haze effect. Environ Res 43:317-331.

49. Ponka A (1990) Absenteeism and respiratory disease among children and adults in Helsinki in relation to low-level air pollution and temperature. Environ Res 52:3446.

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_51._ Schwartz J. Wypij D. Dockery D et al. (1991) Daily diaries of respiratory symptoms and air pollution: Methodological issues and results. Environ Health Perspect 90:181-187.

52. Schwartz J, Hasselblad V, Pitcher H (1988) Air poflution and morbidity: A firther analysis of the Los Angeles student nurses data. J Air Pollution Control Assoc 38:158-162.

53. Thurston GP, Ito K, Kinney P and Lippmann M (1992) A multi-year study of air pollution and respiratory hospital admissions in three New York State metropolitan areas: Results for 1988 and 1989 summer. J Expos Anal Environ Epidemiol 2:429-450.

_54._ Burnett RT, Dales RE, Raizenne M et al. (1992) The relationship between hospital admissions and ambient air pollution in Ontario, Canada: a preliminary report. 85th Annual Meeting, Air and Waste Management Association.

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_57._ Schwartz I and Zeger _5._ (1990) Passive smoking, air pollution and acute respiratory symptoms in a diary study of student nurses. Am Rev Respir Dis 141:62-67.

58. U.S. Environmental Protection Agency, Office of Research and Development (U.S. EPA) (1990) Air quality criteria for lead: supplement to the 1986 addendum. EPA/600/8-90/049F
<!-- Start of picture text --> Non-Manufacturing Manufacturing<br>JC JC<br>6000<br>4000<br>2000<br>0<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>Year<br>Value of Exports in M US$<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Integrated Trade Solutions (WITS). The vertical red dotted line splits the periods before and after the implementation of the JC.

regression equation: E E 0 1 2016 3M M 2016 ∗M M + ε where E E is the value of Jordan’s exports at year ee ee , 2016 is a dummy equal to one for ee ee SSee EEtt = ββ + ββ ∗PPEEPPtt + ββ + γγPPEEPPtt + λλXX year in 2016 and onwards, M M is a dummy variable equal to one if export category is ee manufacturing in year EEtt and zero otherwise, and 2016tt PPEEPPtt ∗M M is a dummy equal to one if ee exports flow is manufacturing exports as of 2016. tt is the parameter of interest. is the set of ee tt PPEEPPtt pre-trends controls. Robust standard errors at the country level. For the analysis, we exploit all ee γγ XX data available in WITS disaggregated by sector and destination country, ranging between 20022022.

**Table 4: Difference-in-differences estimation results**

||Value of Exports(M US$)|Value of Exports(M US$)|
|---|---|---|
|Post 2016|190.83*|190.83|
||(104.35)|(128.65)|
|Manu|1961.92***|1661.47***|
||(235.55)|(104.52)|
|**Post 2016 * Manu**|**2236.23*****|**2536.69*****|
||(485.06)|(533.12)|

21 | P a g e
**Table 7** Attrition: Female Sample

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||_×_**Boys**_×_**G**|**oal**<br>_×_**Boys**|_×_**Goal**|**Levels**|
|Panel A. Diferential Attrition by|Treatment|Status, fully-int|eracted||
|Boys|||-0.031<br>(0.038)|-0.012<br>(0.032)|
|Goal||||0.005<br>(0.025)|
|Panel B. Diferential Attrition by|Treatment|Status and Key|Measures||
|Never talk to mom about SRH|0.033|-0.014|-0.056|0.001|
||(0.094)|(0.050)|(0.078)|(0.041)|
|Age|-0.010|0.008|-0.001|-0.000|
||(0.012)|(0.010)|(0.008)|(0.007)|
|Highest grade|0.019<br>|-0.016<br>|-0.003<br>|0.006<br>|
||(0.014)|(0.010)|(0.010)|(0.008)|
|Married or cohabiting|-0.076|-0.011|-0.082|0.021|
||(0.101)|(0.085)|(0.065)|(0.065)|
|Own house|0.091|-0.058|-0.076|0.027|
||(0.080)|(0.041)|(0.054)|(0.029)|
|House has electricity|0.057|-0.027|0.011|0.020|
||(0.066)|(0.045)|(0.040)|(0.030)|
|Number of household members|-0.044|-0.004|0.009|-0.008|
||(0.028)|(0.016)|(0.019)|(0.011)|
|Boys|||0.004|0.077|
||||(0.196)|(0.154)|
|Goal||||0.108<br>(0.112)|
|Observations||3,178|||

_Notes._ Each panel presents coefficients from a separate regression where the outcome, _Yic_ , is an indicator equal to 1 if the female was not resurveyed at endline. In each panel, the rows list variables included in the model, and the columns indicate interaction terms. Column 1 presents coefficient estimates on interactions between the row variables and a treatment indicator of being assigned to both the _Boys_ and _Goal_ treatment, column 2 presents estimates of coefficients on interactions of the row variables with being assigned to the _Boys_ treatment, column 3 presents estimates of coefficients on interactions of the row variables with being assigned to the _Goal_ treatment, and column 4 presents estimates of coefficients on level controls of the variables at the start of the rows. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

54
**<u>Table 8: Type of remarriage for divorcees by union and husband’s characteristics.</u>**

||Co-resides with<br>husband|Polygamy|Husband works in<br>the formal sector|
|---|---|---|---|
|_Ref: dissolution after age 40. _<br>||||
|Dissolution before age 25|0.252<sup>**</sup>|-0.145|-0.227<sup>+</sup>|
||(0.107)|(0.163)|(0.141)|
|Dissolution between ages 25-39|0.113|-0.055|-0.145|
||(0.106)|(0.158)|(0.140)|
|Last dissolution; rural resident|0.014|0.016|-0.180<sup>***</sup>|
||(0.071)|(0.076)|(0.070)|
|No children born last union|0.032|0.070|-0.138|
||(0.080)|(0.085)|(0.089)|
|Had a son at time of divorce|-0.024|0.034|-0.090|
||(0.079)|(0.082)|(0.078)|
|Number of marital dissolutions|-0.048|0.003|-0.038|
||(0.059)|(0.072)|(0.075)|
|Ever been to a French school|-0.079|-0.153<sup>**</sup>|0.133<sup>**</sup>|
||(0.071)|(0.075)|(0.068)|
|Polygamous father|-0.116<sup>*</sup>|0.144<sup>**</sup>|0.011|
||(0.064)|(0.064)|(0.066)|
|Fostered before age 15|-0.093|-0.186<sup>**</sup>|0.069|
||(0.075)|(0.085)|(0.083)|
|_Ref: Wolof/Lebou._||||
|Serere|-0.129|-0.189<sup>*</sup>|-0.038|
||(0.086)|(0.099)|(0.089)|
|Poular|-0.088|-0.167<sup>**</sup>|0.002|
||(0.077)|(0.082)|(0.082)|
|Other ethnicity|-0.048|-0.102|-0.003|
||(0.091)|(0.098)|(0.102)|
|Mean of dep. Var|0.73|0.48|0.34|
|N|207|207|198|
|Pseudo R2|0.084|0.087|0.105|

Note: Logit model, marginal effects shown. Sample of ever-divorced women.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1, + _p_ < 0.12.

Source: Authors’ estimations using PSF1.

39
|(5)<br>(6)<br>(7)<br>(8)<br>Average<br>p-value<br>p-value<br>recomm. in 2015<br>Individual<br>Control vs.<br>Mean<br>p-value<br>vs. Average<br>Individual<br>T2-T4<br>T2-T4 vs. C<br>recomm.<br>recomm.|138.90<br>0.94<br>0.00<br>0.00<br>(12.38)<br>18.67<br>0.91<br>0.00<br>0.00<br>(16.73)<br>30.34<br>0.98<br>0.07<br>0.00<br>(17.45)<br>1,672.20<br>0.94<br>0.00<br>0.55<br>(215.33)<br>404<br>sample. Column 1 reports self-reported average amounts applied and total cost of fertil-<br>nd report in column 2 the p-value of an F-test that the dummies are jointly equal to zero.<br>mn 4 we compare the individual recommendations to those that the control group would<br>s to columns 3 and 4 for the average input recommendations and costs for farmers in _T_2,<br>nd individual recommendations given to farmers in _T_1 by regressing each variable on a<br>amounts of each fertilizer and costs of individual and average recommendations by using<br>eports the p-value of a t-test that_T_1=0. We use administrative data on recommendations<br>4.|
|---|---|
|(4)<br>idual<br> in 2015<br>p-value<br>T1 vs. C|0.86<br>0.24<br>0.77<br>0.33<br>ers in our study<br>ment dummies a<br>rs in _T_1. In colu<br>d 6 are analogou<br>l group in 2014 a<br>8 compares the<br>armers in_T_1. It r<br>in February 201|
|(1)<br>(2)<br>(3)<br>Fertilizer<br>Indiv<br>application<br>recomm.<br>Control mean<br>p-value<br>Mean<br>in 2014<br>T1-T4 vs. C<br>T1|Urea(kg/ha)<br>267.81<br>0.72<br>151.23<br>(135.25)<br>(32.64)<br>DAP(kg/ha)<br>49.41<br>0.87<br>30.65<br>(65.27)<br>(26.32)<br>KCL(kg/ha)<br>9.45<br>0.78<br>33.27<br>(26.15)<br>(28.16)<br>Cost(pesos/ha)<br>1,895.49<br>0.55<br>1,882.97<br>(1,181.91)<br>(398.83)<br>Observations<br>130<br>133<br>Note: This table reports average amounts of fertilizers and costs and balance checks for the 678 farm<br>izers in 2014 by the control group (SDs in parentheses). We regress each variable on our set of treat<br>Column 3 reports the average input recommendations and the corresponding input costs for farme<br>have received by reporting the p-values of an F-test analogous to those in column 2. Columns 5 an<br>_T_3 and _T_4. Column 7 compares the amounts of each fertilizer and costs self-reported by the contro<br>dummy that takes value of 1 for farmers in_T_1. It reports the p-value of a t-test that_T_1=0. Column<br>the sample of farmers in_T_1_−T_4 and regressing each variable on a dummy that takes value of 1 for f<br>given to farmers (and hypothetical recommendations to controls) and Baseline survey data collected|

33