rafmacalaba/datause-encoder-data huggingface dataset:
# **References** :

- Armendariz, de Aghion Beatriz (1991). “Long-term Capital Reflow Under Macroeconomic Stabilization in Latin America.” _OECD Development Centre Working Paper_ No. 38

- Barrett Devlin (2009). “US sues UBS to get identities of thousands of possible tax cheats”, Financial Times.

- Bayraktar, Nihal and Hippolyte Fofack (2007). “Specification of Investment Functions in Sub-Saharan Africa.” _World Bank Policy Research Working Paper_ No. 4171, The World Bank Group.

- Berg, Andy and Zia Qureshi (2005). “The MDGs: Building a Momentum.” _Finance and Development_ , 42 (3).

- Blejer, Mario and A. Ize (1989). “Adjustment uncertainty, confidence, and growth: Latin America after the debt crisis., Mimeo, IMF.

- Boyce, James and Leonce Ndikumana (2007). “New Estimates of Capital Flight from Sub-Saharan African Countries: Linkages with External Borrowing and Policy Options.” Mimeo, University of Massachusetts.

- Cardoso, E. and R. Dornbusch (1989). “Foreign Private Capital Flows.” in H. Chenery and T. Srinivasan (eds), _Handbook of Development Economics_ Vol. II, Amsterdam, North Holland.

- Collier, Paul, Anker Hoeffler and Catherine Pattillo (2001). “Flight Capital as a portfolio choice.” _World Bank Economic Review_ 20 (3): 335-349.

- Dornbusch Rudiger (1991). “Credibility and stabilization.” _Quarterly Journal of Economics_

- Dornbusch Rudiger (1991b). “Policies to move from Stabilization to Growth.” _World Bank Economic Review_ Supplement, 19-48.

- Easterly William (2002). “How did Heavily Indebted Poor Countries Become Heavily Indebted? Reviewing Two Decades of Debt Relief.” _World Development_ , 30 (10), 1677-1696.

- Edwards Sebastian (1990). “Capital Flows, Foreign Direct Investment, and Debt-Equity-Swaps in Developing Countries.” _NBER Working Paper_ No. 3497, National Bureau of Economic Research.

- Fofack Hippolyte (2008). “Technology Trap and Poverty Trap in Sub-Saharan Africa.” _World Bank Policy Research Working Paper_ No. 4582, The World Bank Group.

- Fofack Hippolyte (2009). “Determinants of Globalization and Growth Prospects for Sub-Saharan African Countries.” _World Bank Policy Research Working Paper_ No. 5019, The World Bank Group.

- Fofack Hippolyte (2009). “Causality between External Debt and Capital Flight in Sub-Saharan Africa”. Forthcoming _World Bank Policy Research Working Paper_ , The World Bank Group.

- Gupta Sanjeev, Catherine Pattillo and Smita Wagh (2007). “Impact of Remittances on Poverty and Financial Development in Sub-Saharan Africa.” _IMF Working Paper_ , WP/07/38.

- IMF (2007). “Reaping the Benefits of Financial Globalization.” International Monetary Fund, Washington DC.

- Kasekende Louis (2001). “Capital Account Liberalisation: The Ugandan Experience.” _Development Policy Review_ 19 (1), 101-120.

- Kose Ayhan, Eswar Prasad, Kenneth Rogoff and Shang-Jin Wei (2006). “Financial Globalization: A Reappraisal.” _IMF Working Paper_ 06/189.

- Laban Raul (1991). “Coordination failures and the costly transition from stabilization to growth.” Mimeo (MIT, Cambridge).

**20**
Table 6: The Impact of Initial Conditions on Employment of Refugees: Language Regions

|||Main sample||Re|stricted sa|mple|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Attitudes X German|0.0221<sup>_∗∗∗_</sup>|0.0185<sup>_∗∗_</sup>|0.0169<sup>_∗∗_</sup>|0.0236<sup>_∗_</sup>|0.0228<sup>_∗_</sup>|0.0235<sup>_∗_</sup>|
||(0.00737)|(0.00884)|(0.00855)|(0.0129)|(0.0127)|(0.0126)|
|Attitudes X French|0.0341<sup>_∗∗∗_</sup>|0.0372<sup>_∗∗∗_</sup>|0.0343<sup>_∗∗∗_</sup>|0.0160|0.0179|0.0193|
||(0.0101)|(0.0112)|(0.0109)|(0.0152)|(0.0137)|(0.0147)|
|Attitudes X Italian|0.0406<sup>_∗∗∗_</sup>|0.0340<sup>_∗∗_</sup>|0.0298<sup>_∗∗_</sup>|0.0282<sup>_∗_</sup>|0.0270|0.0191|
||(0.0121)|(0.0135)|(0.0133)|(0.0165)|(0.0164)|(0.0162)|
|Unemployment X German||-0.0182<sup>_∗∗_</sup>|-0.0193<sup>_∗∗∗_</sup>||-0.0135|-0.0122|
|||(0.00746)|(0.00739)||(0.0115)|(0.0118)|
|Unemployment X French||-0.0214<sup>_∗∗∗_</sup>|-0.0226<sup>_∗∗∗_</sup>||-0.0219<sup>_∗∗_</sup>|-0.0209<sup>_∗_</sup>|
|||(0.00740)|(0.00715)||(0.0108)|(0.0113)|
|Unemployment X Italian||-0.0212<sup>_∗∗∗_</sup>|-0.0189<sup>_∗∗_</sup>||-0.0248<sup>_∗_</sup>|-0.0181|
|||(0.00818)|(0.00883)||(0.0147)|(0.0119)|
|Network X German|||0.000750|||0.00115|
||||(0.00267)|||(0.00530)|
|Network X French|||0.00571|||-0.00159|
||||(0.00418)|||(0.00720)|
|Network X Italian|||0.0144<sup>_∗∗_</sup><br>(0.00635)|||0.0219<sup>_∗∗_</sup><br>(0.00961)|
|Current year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country of birth FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Years since migration FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Arrival year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Canton of arrival FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Canton of arrival X Current year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Education FE|No|No|No|Yes|Yes|Yes|
|Observations|696135|696135|696135|127737|127737|127737|

Dependent variable is Employment dummy. Network is defined as log (nb of co-nationals+1). All regressions also include age, age squared, gender, marital status. Standard errors are clustered at the level Canton of arrival _×_ Year of arrival. * p < 0.10, ** p < 0.05, *** p < 0.01 32
WPS4216

# **Underlying Dimensions of Knowledge Assessment: Factor Analysis of the Knowledge Assessment Methodology Data**

**Derek H. C. Chen* The World Bank**

**Kishore Gawande** Texas A&M University**

The Knowledge Assessment Methodology (KAM) database measures variables that may be used to provide an assessment of countries' readiness for the knowledge economy, and has many policy uses. Formal analysis employing KAM data is faced with the problem of which variables to choose and why. Rather than make these decisions in an ad hoc manner, we recommend factor-analytic methods to distill the information contained in the many KAM variables into a smaller set of ``factors". The main objective of the paper is to quantify the factors for each country, and do so in a way that allows comparisons of the factor scores over time. We investigate both principal components as well as true factor analytic methods, and emphasize simple structures which help to not only provide a clear political-economic meaning of the factors, but also allow comparisons over time.

World Bank Policy Research Working Paper 4216, April 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*Economist, Knowledge for Development Program, Human Development Department, World Bank Institute.  ** Professor, Bush School of Government and Public Service.
important factor as to whether events are likely to be affected by recall decay. Bradburn et al. (2004) identify three factors that determine salience: “(1) the unusualness of the event, (2) the economic and social cost or benefits of the event, and (3) the continuing consequences of the event” (p. 64). Loftus and Marburger (1983) assess the improvement in accuracy of retrospective accounts when surveys use a highly salient landmark event (e.g. the eruption of Mt. St. Helens) to mark the beginning of the reference period. In their study on migration, Smith and Thomas (2003) find that migration events that are of greater salience to the respondent are less likely to be affected by recall decay. Relating specifically to agriculture, Judge and Schechter (2009) test the distribution of the first significant digit of various data to detect abnormalities. Using Benford’s Law, they find evidence of the importance of salience in data quality from an agricultural survey in Paraguay, specifically that more accurate information is recorded for crops that represent a larger share of household income.

Second, recall decay can affect the details of events reported by respondents. Much of the research in this area relates to consumption experiments, which focus on the reliability of reports of consumption or expenditure on food and non-food consumer goods. One exception is Coleman (1983) who addresses recall decay in agriculture through comparisons of the mean number of reported labor hours for each day during the week prior to the interview. Using a dataset of 129 households in 12 villages in Benue State, Nigeria from 1979/1980, he takes as a reference point the mean number of hours reported for the day before the survey. With each additional day from the reference point, he finds a consistent and significant over-reporting of labor hours, ranging from 17.5 percent on the day before the reference day to 61.6 percent on the first day of the recall period. His finding with regard to agricultural labor contradicts expectations based on marketing and household consumption data, which would predict increased under-reporting with the passage of time, as respondents forget events. Gibbs et al. (1986), in their study on agricultural innovation discovery in Southern Australia, find that information collected with an aided recall module was generally reliable over a recall period of at least 12 months despite the relatively low salience of the events. In addition, they find the quality of the information does not diminish substantially with increases in the length of the recall period.

Studies of recall in household consumption data assess the reliability of data by the length of time to which the data refer, such as food expenditures over the past 7 days, 30 days, or even as long as the past year. These studies suggest that longer recall periods are associated with 5
- In _Tajikistan_ , the government‘s 2005 health financing strategy aimed to improve equity, efficiency and cost-effectiveness of the health system by increasing public funding (in particular to primary health care) and introducing a basic-benefits-package (BBP). The BBP provides free services for vulnerable population groups and provides a legal framework for developing the policy for co-payments by patients for selected health services in hospitals.

- Finally, in _Turkey_ , the Turkish government launched a major reform program in 2003 to make the health system more effective by improving governance, efficiency, user- and provider-satisfaction and long-term sustainability. The main elements of the program were to establish a single purchaser in the health system, make the public sector health services delivery network autonomous and strengthen human resources management and information systems. The program has had important effects on access to care, especially for the poor. In addition to improved health insurance coverage for the poor, productivity of health personnel and availability of services have increased. There has also been a rise in the immunization of under-five-year-old children, the use of ante-natal services by pregnant women and the overall utilization of health services (Chakrabourty, 2009).

With respect to perceptions of the need for unofficial payments when interacting with public officials, the LiTS2 data give a mixed picture on their evolution (see Table 4). In Albania, Bulgaria, Macedonia and Russia, fewer respondents in 2010 felt that unofficial payments are usually, or always, needed compared to 2006, while the opposite was true in Azerbaijan, the Kyrgyz Republic, Moldova and Turkey.

# **3.3. Perceptions of overall government performance**

Average perceptions of improvements or worsening in overall government performance appear to have been strongly colored by the severity of impact of the recent global economic crisis (Figure 9). When asked to assess the overall performance of the government, respondents in Tajikistan, Georgia, and Azerbaijan rated the performance of their respective national governments much more highly than did respondents in Romania, Croatia, and Lithuania. At the individual level, we estimate a probit regression where the dependent variable is the rating of government performance (a binary variable equal to 1 if respondent thinks government performance is worse than three years ago) and the main explanatory variable is the respondent‘s subjective assessments of the extent to which he or she was affected by the crisis. Using country fixed-effects and controlling for income and other demographic factors, those reporting being affected ―a great deal‖ by the crisis were on average 14 percent more likely to respond that the performance of the national government worsened in the past 3 years compared to those who were not affected by the crisis. This coefficient is significant at the 5 percent level and this impact of the crisis on government ratings is also found (albeit to a lesser extent) for ratings of local and regional performance.

11
# **Table 2. Estimation results for Model 1 (conflict dynamics)**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||OLS|FE|OLS|FE|AB|OLS|FE|OLS|FE|AB|OLS|FE|OLS|FE|AB|
|VARIABLES||||||||||||||||
|International Troops (per 1, 000 inhabitant) t ‐ t‐1|0.0304**|0.0400**|0.0283*|0.0369**|0.0190|0.0252|0.0326*|0.0227|0.0290*|0.0226|0.0375**|0.0450***|0.0349**|0.0415**|0.0228|
||(0.0147)|(0.0162)|(0.0146)|(0.0161)|(0.0640)|(0.0164)|(0.0177)|(0.0162)|(0.0174)|(0.0807)|(0.0155)|(0.0168)|(0.0154)|(0.0167)|(0.0612)|
|International Troops (per 1, 000 inhabitant) t‐1 ‐ t‐2|‐0.135***|‐0.152***|‐0.130***|‐0.146***|‐0.150***|‐0.0966***|‐0.115***|‐0.0927***|‐0.109***|‐0.109***|‐0.131***|‐0.149***|‐0.127***|‐0.143***|‐0.144***|
||(0.0185)|(0.0206)|(0.0185)|(0.0205)|(0.0178)|(0.0204)|(0.0233)|(0.0202)|(0.0230)|(0.0175)|(0.0187)|(0.0207)|(0.0186)|(0.0206)|(0.0182)|
|Afghan Troops (per 1, 000 inhabitant) t ‐ t‐1|0.0232|0.0154|0.0122|0.00481|0.0610*|0.0105|0.00472|0.00189|‐0.00435|0.0389|0.0414|0.0315|0.0292|0.0196|0.0997***|
||(0.0367)|(0.0386)|(0.0366)|(0.0384)|(0.0349)|(0.0394)|(0.0417)|(0.0390)|(0.0410)|(0.0437)|(0.0387)|(0.0411)|(0.0387)|(0.0410)|(0.0258)|
|Afghan Troops (per 1, 000 inhabitant) t‐1 ‐ t‐2|‐0.00392|‐0.0327|0.00503|‐0.0200|0.000606|0.00594|‐0.0187|0.0118|‐0.0106|‐0.00490|‐0.0146|‐0.0358|‐0.00501|‐0.0231|‐0.00332|
||(0.0373)|(0.0518)|(0.0371)|(0.0515)|(0.0215)|(0.0388)|(0.0549)|(0.0383)|(0.0540)|(0.0386)|(0.0379)|(0.0519)|(0.0378)|(0.0516)|(0.0241)|
|Casualties (per 1,000 inhabitant) t‐1 ‐ t‐2|||‐0.148**|‐0.153**|‐0.162**|||‐0.176**|‐0.193***|‐0.191**|||‐0.143**|‐0.150**|‐0.148**|
||||(0.0649)|(0.0681)|(0.0709)|||(0.0689)|(0.0727)|(0.0787)|||(0.0649)|(0.0681)|(0.0619)|
|Year Trend|||||||||||0.0438|0.0361|0.0400|0.0328|0.0630**|
||||||||||||(0.0304)|(0.0320)|(0.0302)|(0.0316)|(0.0293)|
|Constant|0.148***|0.186***|0.154***|0.188***|0.135***|‐9.30e‐05|0.0651|0.0312|0.121|0.0846|0.0342|0.0867|0.0500|0.0983|‐0.0362|
||(0.0498)|(0.0606)|(0.0494)|(0.0598)|(0.0435)|(0.110)|(0.141)|(0.109)|(0.140)|(0.130)|(0.0931)|(0.106)|(0.0924)|(0.105)|(0.0865)|
|Year Dummies|No|No|No|No|No|Yes|Yes|Yes|Yes|Yes|No|No|No|No|No|
|Observations|192|192|192|192|160|192|192|192|192|160|192|192|192|192|160|
|Adjusted R‐squared|0.209|0.110|0.227|0.133||0.270|0.164|0.292|0.196||0.214|0.112|0.230|0.133||
|Number ofprovince|32|32|32|32|32|32|32|32|32|32|32|32|32|32|32|

_Note:_ Standard errors are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two and three asterisks respectively. The preferred estimation is highlighted in yellow. AB = Arellano and Bond; FE = fixed effects; OLS = ordinary least squares.

12
have FDI siblings. Furthermore, the average foreign sibling presence in the woven industry is 6.1 percent, nearly 10 times higher than that of the non-woven industry. This is true even if I restrict the comparison to only those domestic ...rms with FDI siblings. The foreign sibling presence for domestic ...rms with FDI siblings in the woven industry is 11.3 percent, while the same variable for the non-woven industry is only 3.3 percent. Di¤erences between the two industries may be driven by other industry level variables, such as trade policies and demand shocks. I will, instead, rely only on the within ...rm variations in foreign sibling presence in the regressions.

# 5 Reduced Form Regression Results

The two industries in the garment sector of Bangladesh, namely, woven and non-woven have very di¤erent production structures and techniques. The Appendix discusses how two separate industry speci...c production functions are estimated using Ackerberg et al (2007) in a three step procedure that takes into account endogeneity of labor and material inputs, and how input and investment decisions may depend on the FDI status of ...rms. This technique is similar to that of De Loecker (2007), to allow production function to depend on exporter status. Here, I focus on relating foreign sibling presence to product scope and sales per worker, output per worker and the estimated ...rm productivity. The Appendix also contains a discussion on the construction of the very crucial ...rm speci...c price indexes. Output and material inputs of ...rms used in the production function estimation are constructed by de‡ating total revenue and cost of materials using these ...rm speci...c output and material price indexes. This signi...cantly improves what Haskel, Pereira and Slaughter (2007) describe as a “pervasive problem in the literature on micro panels”that uses industry prices in place of the often missing ...rm level prices.

## 5.1 Shared supplier spillovers: Do FDI ...rms improve the performance of their domestic siblings?

To identify SSS, I relate the performance of domestic ...rms to their foreign sibling presence, as de...ned in equation (7) ; in a domestic ...rm only panel data set: 13
While students in grade 4 were 1.8% more likely to be sampled in control schools than in treatment schools, we do not find systematic difference between the two groups in other student-level variables. The _p-_ values from the joint F-test are 0.477 (school) and 0.329 (student), which is consistent with successful randomization.

Similar to schools in the double grant experiment, schools in the information experiment also have large differences in the educational backgrounds of parent and teachers. The majority of parent association presidents report primary school as their highest level of education, while most teachers had completed a teaching college degree or a university degree. At the student-level, the surveys for experiment 2 were administered to students in grades 3, 4 and 5, and nearly half of the study sample is female. We perform the same procedure as Table 1 to construct the wealth index.

# **4 Conceptual Framework & Measures**

Parental involvement programs (also known as family engagement programs) aim to improve schooland-parent communication to support children’s overall learning environment. These interventions target parents and caretakers, as it is widely accepted that parents play an important role in shaping children’s educational experience and outcomes (Doepke, Sorrenti, & Zilibotti, 2019).

Parental involvement programs recognize that parents may not be able to fully engage with their children’s education because of biased beliefs (Barrera-Osorio et al., 2020; Bergman, 2016; Dizon-Ross, 2019; Rogers & Feller, 2018) and limited cognitive bandwidth, particularly for lowincome parents (Mani et al., 2013; Mullainathan & Shafir, 2013). Thus, the group-based information interventions in our study are aimed at overcoming these psychological and informational barriers that often impede school-and-parent communication.

Parental involvement programs also acknowledge that institutional discrimination hinders schooland-parent communication. Research suggest that schools can exclude parents whose culture or lifestyle differs from that of the dominant culture (J. S. Lee & Bowen, 2006). The power imbalance between schools and less advantaged parents makes it difficult for parents to take an active role in their children’s education. Thus, the double grant experiment in our study is aimed at giving parents power through direct influence over resource allocation in schools.

The theory of action underlying parental involvement programs consists of three steps. The 9
17 income developing countries started getting a larger chunk of the long-term credits. Statistics on long-term OSECs to SSA were available for only 1989 when they accounted for 12 percent of total new commitments over one year.

Statistics on sectoral distribution of OSECs are available only for long-term commitments (see Tabie 4). In 1989, transportation, with a share of 32 percent, led the list, followed by industry, 30 percent, and telec,mmunications and energy, 9 percent each. In the case of SSA, the share of transportation was an overwhelming 53 percent. On the other hand, industry received only 16 percent of the new commitments in this region.

# IV. Distortions due to Export Credit Subsidies

We noted that ECAs provide two main types of export subsidies.<sup>12</sup> First is the explicit subsidy through lower-than-market interest rates. The government either lends to foreign importers directly at rates below its marginal cost of funds, or subsidizes such loans made by commercial banks. This kind of subsidy appears in the government's budget aB an expenditure. The second type is the implicit subsidy provided through the official insurance and guarantee schemes which often charged flat premi_:ms not reflecting the actual risks of nonpayment. Financial difficulties of most ECAs in the early and mid-1980s witness 12 Henry (1987) provides evidence on the magnitude of these subsidies. The study shows that financial profits reported by ECAs are due to inappropriate or misapplied accounting methods.
Table A1.2: Example of SSG Price-Based Measure Notification

# - <u>Price based Measure</u>

The following notification concerning the price-based special safeguard ( **Table MA.4** ) was received from the Permanent Mission of **Costa Rica** on 7 October 2002.

## **MARKET ACCESS:  Costa Rica**

# Notification under Article 5 of the Agreement: Special Safeguard:  Price-Based

––––––––––––––––––––––––––––––––––––––––––––––––––-

- (1) Description of product: Rice in the husk (2) Tariff item number: 1006.10.90 (3) Trigger price: US$223.78/tonne (as calculated in Annex 1)

- (4) Date of application: 27 September 2002

_______________ SPECIAL SAFEGUARD: PRICE-BASED: Annex 1 to Table MA.4 Information required for the calculation of the Trigger Price Prices in the reference period:1986 US$201.60/tonne 1987 US$213.35/tonne 1988 US$256.07/tonne Average US$223.78/tonne Source of price information: IICA - CIAPA.

–––––––––––––––––––––––––––––––––––––––––––––– Source: WTO document (G/AG/N/CRI/14) 52