rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Top ten developing country exporters of intermediate parts for passenger vehicles and motorcycles, ranked by 2006 exports, with annual growth 1988-2006**

||**1988**|**2006**|**CAGR 1988-2006**|
|---|---|---|---|
|Mexico|790|27,930|21.9%|
|China*|109|26,361|35.6%|
|Brazil|417|7,855|17.7%|
|Thailand|53|6,349|30.5%|
|Taiwan|274|5,064|17.6%|
|Turkey|57|3,365|25.4%|
|Indonesia|17|2,904|32.9%|
|Philippines|18|2,564|31.7%|
|India|189|2,190|14.6%|
|Argentina|25|1,579|26.0%|

* Includes Hong Kong

**Source:** UN Comtrade, using modified BEC classification (see, Sturgeon and Memedovic, forthcoming).

# **3.2  Development of the industry before and during the crisis in Canada**

The growing importance of developing countries as final goods markets and production platforms has been felt strongly by suppliers in the mature markets. Before turning to the different development patterns in developing countries, we first present some insights from the recent experience of Canadian suppliers. A recent survey by the Canadian Auto Parts Manufacturers,<sup>9</sup> demonstrates that the growing importance of markets in East Asia provides both opportunities and threats. Canadian firms are under pressure to compete harder for domestic business and also to consider establishing manufacturing facilities overseas.

Table 3 groups the answers to five questions that probe the surveyed firms about their own production activities, sourcing, investments, and customer demands. These responses illustrate the changing geographical activities of Canadian supplier firms before the crisis. Comparing the geographical distribution of three activities – production, sourcing, and investment – a clear trend away from Canada and towards Asia is apparent. While almost 70% of the firms’ production takes place in Canada, only 51% of inputs are currently sourced domestically and only 49% of greenfield investments is occurring in Canada. In contrast, Asia is the production location for only 0.3% of current output, but the source of 4.7% of inputs. Most importantly for the future, 28% of all greenfield investments by Canadian automotive parts suppliers are being made in Asia, which is even ahead of the United States.

An important impetus for Canadian suppliers to invest overseas is explicit requests from its current customers: 64% of suppliers report that they have received such a request in the last three years, to aid the overseas expansion of their customers. Some suppliers also indicated that they believe serving Japaneseowned firms in other countries would increase their chances of gaining new business to supply the Canadian assembly plants of these firms.

> 9 Published in Asia Pacific Foundation of Canada (2005).

13
WPS6779 Policy Research Working Paper 6779

# Wealth Gradients in Early Childhood Cognitive Development in Five Latin American Countries

_Norbert Schady Jere Behrman Maria Caridad Araujo Rodrigo Azuero Raquel Bernal David Bravo Florencia Lopez-Boo Karen Macours Daniela Marshall Christina Paxson Renos Vakis_ The World Bank Development Research Group Poverty and Inequality Team February 2014
297

# Figure E.3

<!-- Start of picture text --> Net Exports Per Worker<br>Costa Rica -1965<br>250<br>TROP<br>200-<br>*<br>100~~~~~~~~~~~~~~~~~~~~~0<br>o 100<br>g . - -g200 ANL<br>0 PETRO MAT FOR ANL CER LAB CAP MACH CHEM<br>so -<br>.100-<br>.150- -<br><!-- End of picture text --> <!-- Start of picture text --> Net Exports Per Worker<br>El Salvador- 1965<br>10 s -<br>TROP<br>100 *<br>o 50 CER<br>,,, MT FOR PETRO | ANL I LAB CAP MACH CHEM<br>050<br>.100<br><!-- End of picture text -->

<!-- Start of picture text --> Net Exports Per Worker<br>Costa Rica -1988<br>800<br>TROP<br>S 500 -<br>*<br>O ANL LAS<br>0 o PETRO MAT FOR CE * CAP MACH CHEM<br>-200-<br>-400<br><!-- End of picture text --> <!-- Start of picture text --> Net Exports Per Worker<br>El Salvador- 1988<br>300<br>TROP<br>200<br>0100 I<br>PETRO MAT FOR ANL CER LAB CAP MACH CHEM<br>-100 I<br>-200<br><!-- End of picture text -->
- Meeting the Challenge of the Resource Curse – International Experiences in Managing the Risks and Realizing the Opportunities of Non-Renewable Natural Resource Revenues – United Nations Development Programme – January 2006.

- Micheal L. Ross – The Political Economy of the Resource Curse. 1999. Johns Hopkins University Press.

- Paul Stevens - Resource Impact - Curse or Blessing? A Literature Survey London: IPIECA – 2003.

- Sachs, Jeffrey D; and Warner, Andrew M. (1999) the Big push, Natural Resource Booms and Growth. Journal of Development Economics, Vol59, 43-76.

- ------ 1995. Natural Resource Abundance and Economic Growth. Working paper 5398 (Cambridge, Massachusetts: National Bureau of Economic Research).

- ------ 2001 Resource abundance and Economic Development. Oxford University Press. Combating the Resource Curse: An Alternative Solution to Managing Mineral Wealth - Erika Weinthal and Pauline Jones Duke University March 2006 Vol. 4.

- ------ 2001. The Curse of Natural Resources, European Economic Review, Vol.45, pp 827-38.

- ------1997. Natural Resource Abundance and Economic Growth. Harvard University, Cambridge MA.(November, 1997.

- Schiavo-Compo S. and Tommasi D., Managing Government Expenditure, Asian Development Bank, 1999.

- Takizawa. H, Gardner. E. and Ueda. K, 2004. Are Developing Countries Better Off Spending Their Oil Wealth Upfront? IMF Working Paper WP/141(Washington : International Monetary Fund).

- W. Max Corden; J. Peter Neary. Booming Sector and De-Industrialization in a Small Open Economy,(Dec, 1982). The Economic Journal, Vol. 92, No 368. Pp 825-848.

- Wakeman-Linn. J, Aturupane. C, Danninger. S, Gvenetadze. K, Hobdari. N, and Le Borgne. E, 2004. Managing Oil Wealth: The Case of Azerbaijan. (Washington: International Monetary Fund).

- World Bank Policy Research Working Paper 2899, October 2002 – Benn Eifert, Alan Gelb and Nils Borje Tallroth. – The Political Economy of Fiscal Policy and Economic Management in Oil Exporting Countries.

- World Bank Policy Research Working Paper 4256, June 2007 – Nina Budina, Gaobo Pang, and Sweder van Wijnbergen. – Nigeria’s Growth Record: Dutch Disease or Debt Overhang?

- World Bank, a _Chance to_ Learn: Knowledge and Finance for Education in Sub-Saharan _Africa_ , February 2000.

- World Bank, Botswana: An Example of prudent Economic Policy and Growth. Africa Region Findings 2002, p. 161.

42
32

**Table 19. Determinants of Fiscal Decentralization**

|Regressors / Dependent Variable|Local govemments<br>Local governments<br>totalreveues<br>s<br>percentuesas<br>consolidated regional<br>budget revenues|Local governments<br>total expenditures<br>beoretransfxentrs<br>a<br>before transfers as<br>percent of the<br>consolidated regional<br>budget expenditures|
|---|---|---|
|Constant|91.18|83.84|
||(18.3)|(21.32)|
|Infant mortality in 1990|-0.94|-0.87|
||(-3.16)|(-3.62)|
|Inflation|-0.03|-0.02|
||(-4.85)|(-3.44)|
|Dummy Republic|-8.71<br>(4.64)|-8.61<br>(-6.22)|
|Dummy Autonomous Okrug|-10.55|-6.84|
||_(-3.55)_|(-3.05)|
|Dummy Northern Region|-5.66 *<br>(-2.15)||
|Dummy Ural Region||7.05<br>(4.12)|
|Dummy Western Siberia Region||7.47<br>(3.7)|
|Dummy Eastern Siberia Region|6.66<br>(2.59)|10.67<br>(4.97)|
|Sample|1994 1996|1994 1996|
|Total Panel (Unbalanced) Observations|<br>251|253|
|Adjusted R-squared|0.31|0.32|
|Durbin-Watson|0.97|0.64|
|White's Heteroskedasticity Test|49.52<br>(0.00) **|44.32<br>(0.00) **|
|Standard Error of Regression|10.18|8.45|
|Root Mean Squared Residual 1994|8.76|7.57|
|Root Mean Squared Residual 1995|9.72|7.97|
|Root Mean Squared Residual 1996|11.45|9.31|

Notes: Unless otherwise indicted, numbers in parenthesis are t-statistics calculated with the White heteroskedasticity consistent standard errors of OLS coefficients. Unless otherwise indicated, OLS coefficients are statistically significant at the 99% level of confidence.

* Statistically significant at the 95% level of confidence

** P-value
political terror might deter civil resistance, it also increases perceived injustices and might thus facilitate mass mobilization.

Following the resource-mobilization theory, we control for the effect of information technologies and infrastructure by including the number of _mobile phones_ per 1,000 inhabitants reported by the World Bank. The ease of mobilization is controlled for by including a variable reflecting the extent of urbanization, proxied by the percentage of the population in urban areas. Appendix 1 provides a correlation matrix for the variables included in the hybrid model 1.

# **3. Estimation results**

To examine the relationship between subjective well-being and nonviolent conflict, we estimate an Ordinary Least Squares (OLS) regression on the number of nonviolent conflict events. Results are shown in Table 2 and standard errors are robust and clustered at the country level. We also estimate a Poisson model and obtain qualitatively similar results, reported in Appendix 2 (Column 1-2). In this section, we report the results of the more parsimonious OLS regression. Although the Hausman test supports the use of the more efficient random-effects model, we report the results of the model including country-fixed effects in the main text while those of the random-effects model are reported in Appendix 2 (Column 3-4). We do so because there are some concerns regarding the comparability of subjective-well-being scores across countries due to linguistic and cultural differences (e.g., Argyle, 2001; Kahneman & Riis, 2005). In addition, the fixed-effects coefficients can be more easily compared with those obtained with the GMM estimator in the robustness checks.

# **Baseline Results**

Our baseline results, shown in Table 2, are the results of the OLS estimations, including country and year fixed-effects and the annual number of strikes and demonstrations as the dependent variable. In the first column, we show the baseline results of a model (1) in which we have only the covariates of the hybrid conflict model. In this regression, only GDP growth is significant 18
**_The scale transformation Command_**

12 maximization (i.e. `type(1)` ), we select the transformation that yields the highest Gap Growth from the resulting data set, which is equal to .695732. Then, we use the corresponding coefficients and constant and apply the 6th-degree polynomial transformation in equation (2). Figure 2 plots the original versus transformed scores on the left and the test score gap evolution by sex between year 1 and 2. This particular Gap Growth maximization makes the gap in year 1 as small as possible while, at the same time, making the gap in year 2 as large as it can.

Note that the original gap magnitude was .343418, about half the size of the transformed gap. These results are particular to this specific scenario and, in practice, the algorithm might behave differently depending on the data and the objective function at hand.

## Figure 2: Original vs Transformed Scores

<!-- Start of picture text --> Original vs Transformed Scores Original vs Transformed Gap Evolution<br>Max Gap Growth by Sex 1<br>4<br>2 .5<br>0<br>0<br>−2<br>−.5<br>−4<br>−4 −2 0 2 4<br>Transformed Scores<br>−1<br>Scores Y1 Scores Y2 1 2<br>kdensity Transformed Scores Y1 kdensity Transformed Scores Y2 Years<br>45 degree line<br>Original Gap Evolution Transformed Gap Evolution<br>Original Gap Growth=.343417677879334<br>Max Gap Growth=.6978828 Note: Top and bottom lines for each color represent sex groups<br>Original Scores Test Scores<br><!-- End of picture text -->

# **4.2 Interpretation**

Regardless of the optimization object chosen, the resulting data set has a similar structure. Each row contains the results for one simulation. The `obj` variable contains the optimization object, while the rest of the variables contain the parameters of the 6th-degree polynomial transformation that achieves that object and the initial random starting values for these parameters. This format allows the user to not only have the full distribution of results but also access the necessary parameters to replicate the desired scale transformation if needed.

For the Gap Growth maximizing and minimizing options, one should be aware that the program finds the most extreme transformations, some of which might be extremely unlikely (although theoretically plausible). Thus, for instances where the bounds are too wide, it might be useful to benchmark against likely transformations (such as those found by the `scale` ~~`t`~~ `ransformation` program that maximize correlation or R-squared) and explore other properties of test scores that could help us discard some of the most extreme and very unlikely transformations. For instance, Ho and Yu (2015) present values for skewness and kurtosis from 330 United States state-level examinations. Based on this, one could look at the moments of the resulting distributions and discard those
|_Financial Market Development_|A combined measure of the amount of capital and capital<br>markets’ efficiency. Measures local equity market size, venture<br>capital availability, ease of accessing loans, regulations of<br>exchanges, banking regulations, and investor rights and<br>protections|
|---|---|
|_Technological Readiness_|The economy’s adoption of existing technologies to enhance<br>productivity, with a focus on ICT. Measures availability of latest<br>technologies,<br>FDI<br>and<br>technology<br>transfer,<br>internet<br>penetration, and firm-level technology adoption|
|_Market Size_|Combined measurement of local and foreign (via exports)<br>market sizes<br>The overall quality of business networks and individual firms’<br>operations and strategies. Measures cluster development,|
|_Business Sophistication_|process sophistication, value chain breadth, competitive<br>advantages, quality and quantity of suppliers, the extent of<br>marketing, etc.|
|_Innovation_|Measures capacity for innovation, R&D spend (both public and<br>private), quality of research institutions, availability of<br>scientists and engineers, and procurement of advanced<br>technology|

Source: World Economic Forum Two of these twelve pillars—market size and business sophistication— cannot be comfortably tagged. Hence, we do not include them.

We include an additional category—Diversification—tied to UNCTAD’s Export Concentration Ratio.

Measures progress in key non-oil sectors into which GCC countries have been attempting to diversify, including tourism, _Diversification_ manufacturing, logistics, SMEs, and financial markets. Any laws containing any element related to these sectors is included Since finding keywords for Business Sophistication and Market Size is difficult, we drop the two pillars. Adding diversification, we establish our eleven legislative categories:

|I.|Institutions|II.|Infrastructure|
|---|---|---|---|
|III.|Macroeconomic Environment|IV.|Health and Primary Education|
|V.|Higher Education and Training|VI.|Goods Market Efficiency|
|VII.|Labor Market Efficiency|VIII.|Financial Market Development|
|IX.|Technological Readiness|X.|Innovation|
|XI.|Diversification|||

Table A1.2 shows the Arabic keywords we used to tag the laws and decrees into pillars.

**Table A1.2 Arabic keywords for policy pillars**

|**Pillar**|**Keywords**|
|---|---|
||منازعات | مظالم | محكمة | محاكم | جزاءات | جزائي | قضاء | قضائي|
||| النیابة<br>| مرافعات |<br>محكوم<br>|<br>عقوب | محامي | محاماة | محاماه | عدال |<br>|
|_Institutions_|قضا | طعن | قانونیة | تحكیم | عدالة | محام | وسائل الثبات<br>|<br>نیابات |<br> <br>|
||<br>محكم | معاقب | احداث<br>| الحداث<br>|لستئنافي<br>| جزائ | جنائي | خدمة|
||<br><br>قاض | مستشار القانون |<br>قضــــائي | كتاب بالعدل | قاضي | فساد | ھدایا|

21
The first major point of consideration in the bilateral asymmetry measure is that it involves a comparison between trade values recorded under different valuation methods. That is, bilateral asymmetry measures compare the Free on Board (FOB) valuation of exports and the Cost Insurance and Freight (CIF) valuation of the equivalent import value. While it is preferable to simply take the FOB valuation for import, they are rarely available. This implies that bilateral asymmetry measures will indicate import overreporting in cases where import records simply include CIF adjustments. Several approaches to account for differences in the valuation of recorded values have been proposed. In the BACI database (Gaulie & Zignago, 2010), the CIF rate is estimated (and removed) via a gravity-type model before discrepancies are further resolved.

Alternatively, the influence of the CIF rate can be factored in by accounting for product composition (Farhad et al., 2023), which will inevitably contain the magnitude of this ratio. Finally, the inclusion of the CIF rate in the interpretation of bilateral asymmetry measures has been implemented using simple thresholding, with many flagging transactions whose CIF rate exceeds the 10% threshold.

However, the variability of the CIF rate across partners and products suggests that this threshold is a useful, yet rough estimate. This facilitates the need for bilateral asymmetry measures that are more penalizing toward discrepancies that are least likely to be explained by logistic factors, such as differences in valuation. In addition, many proposed bilateral asymmetry measures can indicate the magnitude of the discrepancy, but not its direction. In other words, without additional information, these measures alone cannot indicate whether the discrepancy was due to an over or under reported import value. Considering the differences in valuation outlined earlier, the direction of the discrepancy can help isolate discrepancies that go beyond CIF adjustments.

Measures of bilateral asymmetry, however, can only quantify the level of divergence of reported trade values on a single bilateral transaction. To capture reporters’ reliability, there is a clear need to summarize the distribution of bilateral asymmetries across partners and commodities. Therefore, measures of bilateral asymmetry are usually accompanied by an aggregation function that captures important distributional properties of the observed discrepancies across trade activities and partners.

8
<!-- Start of picture text --> 1 2<br>1 Dominican Republic, Kaza-<br>khstan, Sri Lanka, Malawi,<br>T ̈urkiye<br>2 Azerbaijan, Algeria, Georgia, Ko-<br>rea, Rep., Madagascar, Mali, Ro-<br>mania, South Africa<br><!-- End of picture text --> Table A.1: Transitions from partition 1 (rows) to 2 (columns), 2 clusters, 3 partition total, 4 controls

||1|2|
|---|---|---|
|1||Bolivia, Honduras, Madagascar,|
|||Trinidad & Tobago, Yemen, Rep.,|
|||South Africa|
|2|Kazakhstan, Kenya||

Table A.2: Transitions from partition 2 (rows) to 3 (columns), 2 clusters, 3 partition total, 4 controls 49
# **_4.1.1 Do poorer states have a higher JDY application rate?_**

Typically, there is a there is a positive relationship between account applicants and state gross national income. In India, however, we find that the number of applications for traditional accounts does not have a significant relationship with the wealth of a state. This could be due to many reasons. For example, while richer economies may have higher per capita income, they may not have other advantages such as better banking infrastructure or higher financial capability, among others. In contrast, the number of JDY account applications is significantly smaller in wealthier states (Figure 3). This suggests that the JDY policy is making it easier for poor people to sign up for an account regardless of where they live.

**Figure 3: Account applications for JDY accounts and state-level national income per capita** Adults who applied for an account (%)

<!-- Start of picture text --> UP CG<br><!-- End of picture text --> <!-- Start of picture text --> MH<br>KL<br><!-- End of picture text --> _Source_ : Per Capita National Income data are from World Bank-World Development Indicators (2017).

# **_4.1.2 Reasons why people apply for a JDY account_**

This section examines peoples' motivations for applying for a JDY account. Adults that replied they opened a JDY account were asked a follow-up question on the reason for opening the account. Respondents could give multiple reasons, such as receiving, sending, and saving money, using the account to collect subsidies, the overdraft facility, and receiving a bonus payment from the government in exchange for opening an account. About 70 percent of JDY applicants cited receiving, sending and saving money as their main reason for applying (Figure 4).

13
Table 7: Effect of Contract Enforcement on Owner and Tenant Outcomes

||N<br>(1)|Control<br>mean<br>(2)|Treatment<br>coefcient<br>(3)|Standard<br>error<br>(4)|_p_-value<br>(5)|
|---|---|---|---|---|---|
|**Water and Sanitation Access**||||||
|Compound has a piped water connection|600|0.916|-0.036|0.024|0.135|
|Main toilet facility is a pour-fush system in the compound|608|0.942|-0.014|0.019|0.471|
|Compound has a _working_ piped water connection|591|0.573|-0.033|0.041|0.423|
|Main source of water is piped water to compound|589|0.306|0.040|0.037|0.286|
|Piped water consumption (meter reading)|474|169.554|2.583|20.684|0.901|
|Amount household spent on water for all uses last month (US$)|334|6.617|0.664|1.005|0.509|
|Time spent fetching water in a week [1]|601|118.187|-17.569|21.682|0.418|
|Somewhat or very satisfed with main water source|608|0.443|0.004|0.040|0.924|
|**Property owner**||||||
|Index: Perceptions of service delivery fairness|570|-0.000|0.070|0.084|0.404|
|Index: Perceptions of service delivery quality|589|0.000|-0.016|0.079|0.840|
|Index: Relationship with tenants|371|-0.000|-0.043|0.105|0.680|
|**Tenant**||||||
|Index: Perceptions of service delivery fairness|357|0.000|0.030|0.107|0.779|
|Index: Perceptions of service delivery quality|402|0.000|0.086|0.101|0.395|
|Index: Psychological well-being|403|0.000|0.139|0.105|0.186|
|Index: Community activism|403|0.000|-0.084|0.101|0.411|
|Relationship with property owner (scale of 1 to 10)|403|8.266|0.232|0.241|0.337|

*** ** * _Notes_ . p<0.01, p<0.05, p<0.1. Sample includes all compounds that were included in the follow up survey: control and treatment compounds in enforcement-treatment clusters. Property owner and compound water outcomes are from the property owner survey. Tenant outcomes are from the tenant survey. Indices are computed following Anderson (2008), normalized by the control group. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated. [1] In minutes.

35
- 22 -

# Table 2.1. Distribution of Manufacturing Establishments by Source of Electricity

|Sourc, of<br>|Mf|g**ria**|**Indo**|**ne ti**|**Tha**|**i(and**|
|---|---|---|---|---|---|---|
|Electricity<br>___________|~<br>Percent<br>_ ____________|_NF1ber_of<br>Firms|Percent<br>_______|Numer of<br>Firm|Percent|NFbr of<br>Fi rm|
|Pulic<br>Only|7.8|14|34.5|100|94.0|282|
|PSubic<br>Sup~pLy<br>**as**Mafn|78.2|140|50.7|147<br>_<br>_<br>_<br>_<br>_<br>_|5.3<br>_<br>_<br>_<br>_<br>_<br>_|16|
|Generators<br>**as**Main|11.2<br>_<br>_<br>_<br>_<br>_<br>_|20<br>_<br>_<br>_<br>_<br>_<br>__|10.7|31<br>_<br>_<br>_<br>_<br>_|0.7|2|
|Generaetors<br>OnLy__|2.8<br>_<br>_<br>_<br>_<br>_|5<br>_<br>_<br>_<br>_<br>_<br>_|4.1|12|0.0|0|
|ALL|100.0|179|100.0|290|100.0|300|

## NOTE: From Appendix Tables Bi1 C1, and DI.

--ource World Sank Nigeria Infrastructure Project Establishment Survey 1988. World bank Research Project (RPO 676-71) EstablIshment Survey for Indoresia _and_ Thailand 1992.