rafmacalaba/datause-encoder-data huggingface dataset:
(2006) and François, Hoekman and Manchin (2006)). To give an example using data from this study, between 90% and 97% of qualifying African exports of apparel enter the US and EU under their respective preferential regimes, the AGOA for the US and Everything But Arms or Cotonou preferences for the EU (see table 1). Yet, as shown here, export patterns of textiles and apparel to the two destinations has been drastically different in recent years (see figure 1). When, the origin requirement was drastically simplified under AGOA, in spite of remarkably similar average preferential margins in the US and in the EU (US MFN tariff of 11.5% in 2004 and EU preferential margin of 11.0%), growth of apparel exports to the US took off while apparel exported to the EU remained flat. Thus, assessing the restrictiveness of RoO only by inspecting utilization rates would suggest low costs, while ignoring that export growth rates to the two destinations have diverged around the time when meeting origin requirements to one market, the US, were relaxed.

This is why it is desirable to go beyond inspection of utilization rates and indices of restrictiveness to isolate the effects of meeting origin requirements. This paper isolates the costs of meeting these origin requirements by exploiting a relaxation of the ‘yarn forward’ rule for Textiles and Apparel applied by the US in its preferential trade policies. Early on under AGOA, the US introduced a ‘special regime’--the ‘third-country provision’--which consists in allowing the use of fabric from any origin in the making of apparel (rather than requiring US fabric originating domestically or from the US), relative to the no-change environment of EU preferential regimes still requiring European or locally-produced fabric. To our knowledge, this is the first such estimate.

Controlling for other factors, we estimate that relaxing RoO by allowing the use of fabric from any origin increased apparel exports to the US by about 168 percent for the top 7 (out of 22) qualifying African exporters in the group. We attribute the lack of supply response in the other countries receiving the AGOA’s Special Rule to institutional weakness. These estimates are based on product-level exports at the HS4-digit level for knitted apparel (Chapter-61) and nonknitted apparel (Chapter-62) over the period 1996-2004 which spans the period when the US relaxed the origin requirement for African apparel with the “Special Rule” to be described 4
- 36  John RM. Price elasticity estimates for tobacco products in India. _Health Policy and Planning_ 2008; **23** :200– 209.

- 37  World Health Organization. Tobacco Market Contry Profile: Republic of Moldova. 2012.

- 38  Gallus S, Schiaffino A, La Vecchia C, _et al._ Price and cigarette consumption in Europe. _Tobacco control_ 2006; **15** :114–119.

- 39  Pisinger C, Godtfredsen NS. Is there a health benefit of reduced tobacco consumption? A systematic review. _Nicotine & tobacco research_ 2007; **9** :631–646.

- 40  Tverdal A, Bjartveit K. Health consequences of reduced daily cigarette consumption. _Tobacco control_ 2006; **15** :472–480.

- 41  Fact sheet about health benefits of smoking cessation. 2016.

- 42  Riley L, Cowan M. Noncommunicable diseases country profiles 2014. _Geneva: World Health Organization_ 2014.

- 43  Denisova I, Kuznetsova P. The effects of tobacco taxes on health: An analysis of the effects by income quintile and gender in Kazakhstan, the Russian Federation, and Ukraine. Published Online First: 2014.

- 44  James E, Saxena A, Franco Restrepo C, _et al._ The Distributional Consequences of Increasing Tobacco Taxes on Colombia’s Health and Finances. 2017.

- 45  Saywell Jr RM, Zollinger TW, Lewis CK, _et al._ A model for estimating the economic impact of secondhand smoke exposure: a study in Indiana. _Journal of Public Health Management and Practice_ 2013; **19** :E10–E19.

- 46  Fuchs A, Meneses F. Regressive or Progressive? The Effect of Tobacco Taxes in Ukraine. 2017.

19
# 5.1 Intention-to-Treat

Table 2 shows our main ITT estimates of the impacts of NEP-B and NEP-I on standardized scores of child developmental outcomes. The results suggest that the offer of NEP-B improves receptive language (TEVI-R) by 0.076 SD (statistically significant at the 10% level) and NEP-I improves these by 0.10 SD (statistically significant at the 5% level). Accounting for multiple hypothesis testing, only the NEP-I impact remains statistically significant. In column 4 we show that we cannot reject the hypothesis of equal impacts of NEP-B and NEP-I.

Both NEP-B and NEP-I lead to an increase in the composite index of socio-emotional development of 0.064 SD and 0.132 SD, respectively. However, only NEP-I is statistically significant at the 5% level and robust to multiple hypothesis testing. These impacts are driven by changes in the subscales of interaction with adults and the social role, results reported in Table A6 in Appendix 2.

The interventions also lead to a decrease in behavioral problems (internalization and externalization) but the impacts are small and not statistically significant. The estimated impacts on executive function and sustained attention are also small and insignificant. These results are robust to the inclusion of additional controls, such as child age and sex, baseline outcomes, or caregiver socio-economic characteristics. This is shown in table A6 in Appendix 2.

Table 2: ITT estimates of child development outcomes

||Obs.|NEP-B|NEP-I|P value Test B=I|
|---|---|---|---|---|
|Receptive Language|2895|0.076*<br>(0.044)|0.100**†<br>(0.045)|0.623|
|Personal-Social Development: Composite Index|1532|0.064|0.132**†|0.258|
|||(0.061)|(0.062)||
|Behavioral problems: Externalization|1971|-0.022|-0.014|0.874|
|||(0.050)|(0.050)||
|Behavioral problems: Internalization|1887|-0.028|-0.019|0.856|
|||(0.049)|(0.049)||
|Executive Function|2879|-0.008|0.035|0.329|
|||(0.044)|(0.045)||
|Sustained attention|2893|-0.035|0.009|0.318|
|||(0.044)|(0.044)||

Note: Each line reports estimates from a separate regression. Dependent variables are standardized to be mean 0, SD=1. Executive function is measured with the DCCS test, Receptive language is assessed using the TEVI-R test, personal social development is administered using the BDIST II, and behavioral problems (externalizing and internalizing behaviors are assessed using the CBCL instrument. All regressions control for health center’s fixed effects. *p<=10%, **p<=5% testing individual hypotheses, †p<=10% testing multiple hypotheses.

Table 3 presents ITT estimates for caregiver outcomes such as child-rearing practices, caregiver beliefs, attitudes and perceptions, and caregiver psychological well-being. Regarding parental behaviors, receiving an invitation to participate in NEP-I significantly improves the quality of the 14
ing changes in the economic outcome variables of employment and enterprise counts. For the percentage change in enterprise counts for the whole period from 2004-2012 (column 1), the estimated elasticity with respect to nighttime lights is -0.0356 which is statistically significant. This finding differs vastly from the cross-sectional regression where the elasticity was positive. The employment regression in column (2) yields a similarly small SOL coefficient. This suggests that the enterprise growth is negatively correlated with increases in nighttime light output, which is counterintuitive. The analysis focuses now on the simple Landsat indices and assesses the changes in the mean NDBI and NDVI as predictors for changes in the economic outcome variables. The estimated coefficients for both measures are positive and highly significant. Thus an increase in urban areas and vegetation is associated with expansion of enterprises and employment at the commune level. Overall, the model performs poorly at capturing the overall variation over time and yields very little predictive power with R-squared measures of close to zero.

To control for serial correlation, a long-difference regression is estimated for the same period in columns (3) and (4). This yields positive coefficients on nighttime light intensity, although this parameter is imprecisely measured in the enterprise regression. Again there are strong positive effects for NDBI and a negative effect of NDVI in the enterprise regression, but an insignificantly positive effect of NDVI in the employment model. The sign changes in these regressions suggest the presence of large measurement error. Again, there is very low predictive power with R-squared statistics of at most 0.017. In summary, the time series models perform poorly and the parameter instability suggests that the model can not nail down the relationship between changes in satellite measures and economic activity well.

The expenditure data is limited by the lower sample size and the shorter time period of consistent data. The results indicate negative coefficients for the SOL measure which are imprecisely estimated. While the coefficients on NDBI are largely similar in both the 2010-2012 and 2004-2008 regressions and estimated to be around 0.3, they are only statis21
relying overly on any one set of expert assessments. We can get a particularly clean illustration of potential differences of opinion between expert assessments by comparing the CPIA ratings of the World Bank and the African Development Bank. These two institutions have in recent years harmonized their procedures for constructing CPIA ratings. Essentially, an identical questionnaire covering 16 dimensions of policy and institutional performance is completed by two very similar sets of expert respondents, namely country economists with in-depth experience working on behalf of these two organizations in the countries they are assessing.

Despite the homogeneity of the respondents and the very similar rating criteria, there are non-trivial differences between both organizations in the resulting assessments on the 16 components of the CPIA. Consider for example CPIA question 16 on "Transparency, Accountability, and Corruption in the Public Sector". The data for 2005 from both organizations are publicly available for a set of 38 low-income countries in Africa.<sup>12</sup> As reported in Table 2, the correlation between these two virtually identical expert assessments, while unsurprisingly positive, at 0.67 is nevertheless quite far from perfect. In the next section of the paper we discuss in more detail how we can interpret such differences of opinion as measurement error in each of the assessments, and how to quantify the extent of this measurement error. For now, however, we do note a very simple practical implication: when even very similar experts can provide significantly different assessments, it seems prudent to base assessments of governance for policy purposes on the views of a variety of different expert assessments.

Another critique often leveled against expert assessments of governance is just the opposite of the one we have discussed: that the country ratings assigned by different groups of experts are too highly correlated. The point here is a simple one. Suppose that one set of experts "does their homework" and comes up with an assessment of governance for a set of countries based on their own independent research, but a second set of experts simply reproduces the assessments of the first. In this case, the high correlation of two expert assessments cannot be interpreted as evidence of their accuracy. Rather, it would reflect the fact that the two sources make correlated errors in measuring governance. A priori, this should be a question of

> 12 Starting with the 2005 data, both the African Development Bank and the World Bank have made public their CPIA scores.  The AfDB does so for all borrowing countries while the World Bank does so only for countries eligible for its most concessional lending.

17
- 18 -

Table 2. RegresIo Eaudtons for Agdricutural-SuDply In Sub-Sabaran A-frca: CrM models

||Depndan|t v<br>ble:|total agrcu|ltural exports|
|---|---|---|---|---|
||Cocoa|Coffee|Cotton|Tea|
|Producer price a/|0.22*<br>(1.75)|0.23**<br>(8.07)|0.67**<br>(4.02)|-0.04<br>_(-0.50)_|
|Real effecdve exchange rate a/|4.35**<br>(-3.91)|_0.055*_<br>(1.94)|-0.68**<br>(-5.04)|<br>0.126<br>(0.65)|
|Disasters variable|-0.0067<br>(-1.49)|-0.0037*<br>(-2.86)|* 0.0026<br>(1.64)|-0.0192**<br>(-3.14)|
|Weather variable|-0.20<br>(-1.65)|-0. 163**<br>(-2.82)|0.226*<br>(2.4)|0.033<br>(0.21)|
|Intercept|5.16**<br>(5.8)|<br>3.30**<br>(15.2)|4.43**<br>(3.9)|<br>4.24**<br>(4.02)|
|Degrees of freedom|107|219|171|59|
|Countries|7|14|11|4|

Notes: Exports, producer price, exchange rate, and the rainfall variable have been expressed in logarithms. Their coefficiants may be interpreted as elasticities.

Years refer to split crop years (e.g. 1980/8 1). Since marketing occurs in the second of the two calandar years, export data for the 1981 calandar year correspond to prices, rainfal, etc. for 1980.

The pooled cross-ection time series procedure was used.

Figures in parentheses are t-values. Significance levels are * (955) and ** (99%).

a/ Two year moving average (t and t-1)
Policy Research Working Paper 6108

# **Abstract**

A new Services Trade Restrictions Database collects and makes publicly available information on services trade policy assembled in a comparable manner across 103 countries, five sectors (telecommunications, finance, transportation, retail and professional services) and the key modes of service supply. It contains richly textured policy information as well as a preliminary quantification of policy measures. This paper is a guide to the database, and provides a description of the data, how it was collected, how policy information was quantified, and how the data are presented in the publicly available, interactive Web database. The database is best seen as a first response to the strong demand for better information from policy-makers, negotiators, researchers and the private sector. Even in its present version, the database can play an important role in advancing policy reform by facilitating the analysis of services policies, informing international negotiations by providing data on actual policies, and provoking dialogue and refinements by making information on policies publicly available. Through feedback from various interested parties, the database may evolve into a collectively created public good.

This paper is a product of the Trade and Integration 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 amattoo@worldbank.org, i.borchert@sussex.ac.uk and bgootiiz@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
# **6 Conclusion**

The demographic divergence between the aging high- and upper-middle-income countries – with shrinking workforces – and the LLMICs – with still expanding youth population – provides a huge opportunity for many countries to leverage migration for their own benefits (Abdel Jelil et al., 2024, World Bank, 2023). As seen in this study, BLAs can be a credible policy instrument to facilitate such flows between countries. For LLMICs, the welfare gain from an agreement can be US$120 million annually, and US$2.1 billion for agreements with destinations in the GCC countries where the migration impacts of the BLAs are large. However, this study points to an important role of government capacity – particularly for origin countries in Africa where most of the youthful population is and will be concentrated – to implement such agreements and realize the gains from migration.

# **References**

- **Abdel Jelil, Mohamed, Samik Adhikari, Quy-Toan Do, Heidi Kaila, Federica Marzo, Olive Nsababera, Ganesh Seshan, and Maheshwor Shrestha.** 2024. _Migration: Africa’s Untapped Potential. Africa Development Forum_ , Washington, DC:World Bank.

- **Allen, Treb, Cauˆe de Castro Dobbin, and Melanie Morten.** 2018. “Border walls.” National Bureau of Economic Research.

- **Angelucci, Manuela.** 2015. “Migration and financial constraints: Evidence from Mexico.” _Review of Economics and Statistics_ , 97(1): 224–228.

- **Arpaia, Alfonso, Aron Kiss, Balazs Palvolgyi, and Alessandro Turrini.** 2018. “The effects of European integration and the business cycle on migration flows: a gravity analysis.” _Review of World Economics_ , 154: 815–834.

- **Bah, Tijan L, Catia Batista, Flore Gubert, and David McKenzie.** 2023. “Can information and alternatives to irregular migration reduce “backway” migration from The Gambia?” _Journal of Development Economics_ , 165: 103153.

- **Baseler, Travis.** 2023. “Hidden income and the perceived returns to migration.” _American Economic Journal: Applied Economics_ , 15(4): 321–352.

24
||Are there -- or are there going to be -- public standpipe/|
|---|---|
||hydrant operators ? (if the answerisno, change the indicator<br>to "I',otherwise leave as**v0n)**<br>|
|concess.<br>indic.|0|
||Estimated delivery rate of vendors (if no information,<br>use 2 cum/day/vendor):<br>|
|cum/day/||
|vendor|0<br>|
||Opportunity cost of vendor labour (may be different from their<br>net income as a result of monopoly or restricted entry -- if no<br>information, use Rp 2500/day/vendor):|
|Rp/day/||
|vendor|0|
|cum/day/|Average delivery rate of standpipes:<br>|
|tdi|<br>0|
|sanpp|<br>|
||Opportunity cost of standpipe/hydrant<br>operator services (may<br>be different from their net income as a result of a monopoly<br>-<br>if no information, use Rp 2500/day/concessionaire):|
|Rp/day/||
|conc.|0|
||Average time required to get 1 cubic meter water from standpipe<br>(hauling excluded):|
|hour|0|
||Average distance from standpipes to those households which rely<br>on water from standpipes:|
|meter|0|
||Value of private time (if no information, Rpl 50/hour in<br>Java/Bali, Rp200/hour In the Outer islands is proposed):|
|Rp/hour|0|
|6.|Did local people boil the drinking/cooking<br>water before project<br>implementation<br>?(if the answer is yes, change the indicator tof1I,no<br>a1 jIfno write f0')|
|boiling<br>indic.|0|
||Do you assume project will make a difference in boiling habits|

19
The second approach of employing statistical modelling techniques to impute missing data can offer a promising alternative when data are scarce. Within the imputation approach, there are also two directions: one is imputation at the micro level, using household (or individual) data from household surveys, and the other is imputation at the global level using country data. We refer interested readers to Dang, Jolliffe, and Carletto (2019) for a more detailed review of the literature on micro imputation methods. As for macro imputation methods, Bonjour et al. (2013) applied mixed modeling techniques to impute estimates for solid fuel use for household cooking for 155 countries over the period 1980-2010 and obtained encouraging results. Their approach has also been employed to impute estimates for missing electrification rates in a recent joint global study by the UN Statistics Division, the World Health Organization, and the World Bank (International Energy Agency et al., 2018).

For a brief illustration, we adopt a modified version of this statistical model with some further refinements, and provide estimates of the electrification rate for several African countries in Figure 7.<sup>28</sup> The selected countries represent different levels of electrification rate and include Uganda (low electrification), Nigeria (medium electrification), and Tunisia (high electrification). While the imputed rates (solid line) do not perfectly coincide with the actual data points, they closely track the latter in all three cases.<sup>29</sup> Estimates for all countries in the African region as a whole are also encouragingly close to those based on the actual data.

Estimations results using both micro and macro imputation methods thus appear promising and could be applied to provide estimates for other indicators where data are unavailable. There

> 28 We offer more discussion on the technical details in Appendix 1.

> 29 The actual data points themselves are the best available estimates of electrification that come from different data sources including household surveys and population censuses, so some inconsistency may be expected; see International Energy Agency et al. (2018) for further details on the data.

31
# **The Patterns and Determinants of Household Welfare Growth in Jordan**

# **2002-2010**

**Keywords.** Jordan, counterfactual decompositions, Re-centered Influence Function (RIF) regressions, economic growth, welfare distribution, Poverty incidences, Gini coefficient, household expenditure.

## **JEL Classification.** D12, D30. D31, D60, D63, I30, I31, I32, P46, R2, R28.

## **Sector Board:** _Economic Policy._

**Acknowledgments:** The author is grateful for the guidance and comments of Mr. Ndiame Diop (lead economist) and Mr. Essamah-Nssah (senior economist). The author would also like to acknowledge the valuable comments received by Mr. Bernard Funck (sector manager) and the able editing work of Ms. Zaina Zteityeh. The author would like to thank the Department of Statistics and the Research and Studies Department at the Ministry of Planning and International Cooperation in Jordan for their valuable collaboration.