rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Growth of employment and sales, innovators and non-innovators

|||Manufacturing|||||Se|rvices|||
|---|---|---|---|---|---|---|---|---|---|---|
||All|Africa|South Asia|ECA|MENA|All|Africa|South Asia|ECA|MENA|
|**No of Firms**|7846|691|2541|2665|1949|7187|961|781|3993|1452|
|Non-innovators (%)|0.54|0.49|0.26|0.68|0.74|0.74|0.59|0.45|0.79|0.84|
|Process-innovators only (%)|0.18|0.23|0.31|0.12|0.09|0.10|0.22|0.23|0.07|0.07|
|Product-innovators|0.27|0.28|0.43|0.20|0.16|0.16|0.20|0.31|0.15|0.10|
|of which product & process innovators|0.51|0.52|0.60|0.40|0.37|0.29|0.40|0.26|0.26|0.29|
|**Employment growth**<br>|||||||||||
|All frms|0.05|0.08|0.07|0.05|-0.01|0.06|0.10|0.08|0.06|0.04|
|Non-innovators|0.03|0.07|0.05|0.05|-0.02|0.05|0.08|0.09|0.05|0.04|
|Process innovators only|0.07|0.06|0.08|0.08|0.00|0.08|0.12|0.07|0.09|0.02|
|Product innovators|0.06|0.11|0.08|0.04|0.01|0.08|0.12|0.08|0.07|0.07|
|**Sales growth (Nominal)**<br>|||||||||||
|All frms|0.09|-0.04|0.16|0.16|-0.05|0.07|-0.18|0.16|0.16|-0.07|
|Non-innovators|0.06|-0.06|0.14|0.15|-0.06|0.07|-0.13|0.17|0.16|-0.07|
|Process-innovators only|0.11|-0.05|0.15|0.18|-0.06|0.05|-0.12|0.15|0.17|-0.10|
|Product innovators|0.13|-0.02|0.17|0.16|-0.01|0.05|-0.37|0.16|0.15|-0.01|
|of which: Old products|-0.15|-0.33|-0.12|-0.05|-0.34|-0.20|-0.70|-0.13|-0.05|-0.28|
|Newproduct|0.30|0.28|0.34|0.25|0.28|0.26|0.23|0.36|0.24|0.25|

Note: Employment and sales growth are measured over three-year period from time survey was completed. Employment growth is measured as change in full-time employees. Sales growth is measured as change in local nominal currency.
determinants of firms’ debt maturity choices around the world. Comparing the R-squared of the regressions across the three columns of Panel B of Table 3, we find that our proxies for _Credit Information Quality_ and _Legal Institutions_ explain together 44% of the variation in the maturity structure of corporate debt, whereas adding financial development, macro and micro variables only increases the R-squared by an additional 19%.<sup>27</sup>

# **5.3. Robustness checks**

We have performed a number of robustness checks for our results (available on request). First, different measures of the debt maturity structure were used. For instance, following Demirguc-Kunt and Maksimovic (1999), we used the ratio of short-term debt to total assets. We also used the medians (instead of the means) of the short-term debt ratios and our results are unchanged.

Second, alternative specifications of explanatory variables were employed. For instance, we used interchangeably indicator variables for the establishment of credit registries, credit bureau coverage variables and an overall index of the quality of credit information. We replaced the corruption index by the Rule of Law variable obtained from International Country Risk Guide and also included alternative macro and micro characteristics variables such as bank concentration, non-performing loans and market-to-book ratios of firms. These variables were not included in the final specification either because of correlations with other variables or because of limits on data availability. Our main results are robust to alternative specifications of the model.

Third, we investigated potential endogeneity bias in our regressions. In particular, the development of domestic financial sectors as well as firms’ profitability and default risk may be influenced by firms’ financial decisions including debt maturity choices.<sup>28</sup> To address this

> 27 This result appears even stronger when we analyze the sub-samples of developed and developing economies, separately. For developed economies, we find that credit information and the legal framework are the driving factors of debt maturity choices, explaining 74% of the variations in the debt maturity structure. In contrast, including macro and micro variables only marginally improves the R-squared by 12%.

> 28 In their analysis to explain debt maturity structure, Demirguc-Kunt and Maksimovic (1999) use bank assets divided by GDP to proxy for the size of banking sector, which might be endogenous as the size is affected by the aggregate decisions of all firms (The authors use two-stage regressions to control for this dependence). Our proxy for financial development, deposits of total financial system to GDP, may be less affected by a potential endogeneity problem because the amount of short-term funds (i.e. deposits) that are available to the domestic financial system can be regarded as exogenous with respect to firms’ financial decisions measures (see Fan,

22
# **Table 9: Sensitivity to the Trade and FDI Responses Across Market Structures** **<u>Welfare is Hicksian Equivalent Variation (EV) as a Percent on Benchmark Consumption</u>**

||**Low Tr**|**ade or FDI Res**|**ponse**|**Central T**|**rade or FDI R**|**esponse**|**High Tr**|**ade or FDI Re**|**sponse**|
|---|---|---|---|---|---|---|---|---|---|
||Armington|Krugman|Melitz|Armington|Krugman|Melitz|Armington|Krugman|Melitz|
|**Scenario: Import NTMs.**||||||||||
|Percent change in aggregate exports(calibration target)|1.06|1.06|1.06|1.08|1.08|1.08|1.19|1.19|1.19|
|Multiplier on Pareto shape value in table times 4.58(a=4.58)*|NA|NA|0.9|NA|NA|0.975|NA|NA|1.5|
|Multiplier on domestic presence business‐services substitution<br>elasticities,value in table times σ = 3|1|1|1|1|1|1|1|1|1|
|Multiplier on non‐business‐services substitution elasticities**|1.21|0.965|1|1.235|1|1|1.37|1.125|1|
|**EV(% of measured consumption in the benchmark)**|**0.25**|**0.33**|**0.25**|**0.24**|**0.32**|**0.24**|**0.24**|**0.31**|**0.22**|
|**Scenario: FDI Services Barriers Only**||||||||||
|Percent change in aggregate FDI(calibration target)|24.4|26.3|26.3|24.4|26.8|26.8|24.4|27.1|27.1|
|Multiplier on Pareto shape value in table times 4.58(a=4.58)|NA|NA|0.975|NA|NA|0.98|NA|NA|0.98|
|Multiplier on domestic presence business‐services substitution<br>elasticities,value in table times σ = 3***|30|1.5|1.5|30|1|0.995|30|0.9|0.9|
|Multiplier on non‐business‐services substitution elasticities**|1.235|1|1|1.23|1|1|1.23|1|1|
|**EV(% of measured consumption in the benchmark)**|**0.31**|**0.63**|**0.63**|**0.31**|**0.89**|**0.90**|**0.31**|**1.02**|**1.02**|

*For a solution to the Melitz model, the Pareto shape parameter is restricted to be greater than the elasticity of substitution minus one: a(i) > [σ(i)-1];  since σ(i) = 5.1 in textiles and apparel, we must restrict a > 4.1. Then our multiplier on “a” may not be below .9 **See table 4 for central values of the parameters. ***We set an upper bound on the elasticity of substitution between FDI and domestically provided business services of 90 (=30 times the central value in Krugman of σ=3). The Armington model still falls short of fully replicating the FDI response in the Krugman model. Source: Authors’ estimates

56
# **Figure 19 Evolution of Turnover Fixed Effects (Pre Multi-Fund Regime)**

This figure presents the evolution of the coefficients obtained from the regression of the Grinblatt et al. (1995) turnover statistic at the PFAtime-fundtype level on PFA, time, and fund type fixed effects. This figure only considers the period previous to the multi-fund regime (July 1996 to August 2002). The zero-mean time fixed effects are presented in the figure. Coefficients that are statistically significant at the tenpercent level are marked on the figure. T-tests are two-tailed. Significant events that occurred during this period of time are also highlighted on the figure: the Asian crisis (July 1997), the Russian crisis (August 1998), and the introduction of Fund E and the widening of the minimum return band (October 1999). Numbers represent percentages (results are multiplied by 100).

<!-- Start of picture text --> 40<br>Grinblatt et al.<br>35<br>Significant at ten percent<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>Asian Crisis Russian Crisis Fund E<br>-10<br>Jul-96 Jan-97 Jul-97 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02<br> Time Fixed Effects from Turnover Regression<br><!-- End of picture text -->
# **A.2 Implementation and quality control**

To ensure the accuracy in the responses and the comparability of the data collected across countries we use a standardized process for implementation across all countries. We apply the same questionnaire administered through face-to-face interviews with CAPI (computerassisted personal interviews) in all countries. A multidisciplinary literature has emphasized that face-to-face is often more accurate than alternative modes.<sup>56</sup> Face-to-face mode, despite being more resource intensive, ensures more accurate responses, especially for a long questionnaire such as FAT. The average interview time can vary between 35 minutes to one hour.

We conducted a standard training in each country with enumerators, supervisors, and managers leading the data implementation. The training was led by team members directly involved on the elaboration of the questionnaire. The three days training consisted of one general presentation about the project, covering the main motivation, relevance, coverage, and protocols that should be used to approach the interviewees and the review of the full questionnaire (question by question). The training material included pictures of each technology mentioned in the survey both in general and sector-specific business functions, which was shared with enumerators. After going over the full questionnaire and clarifying any questions that emerged, the participants of the training went through a mock interview using Computer Assisted Personal Interviewing (CAPI), under the supervision of our team.

A pilot of the questionnaire was implemented in each country with firms out of the sample. After the pilot, our teams had the opportunity to discuss with the managers implementing the questionnaires to clarify any potential question over the implementation process. A similar check happened after 10% of data collection.

The same terms of reference to the organizations that implement the survey across all countries. These included the requirement that both the organizations, as well as the main team of interviewers, supervisors, and managers, had ample experience on collecting firmlevel data in their respective country and follow similar procedures for implementing the survey.

The questionnaire was implemented at the establishment level. In the sample, 86% of our observations refer to single establishment firms. In the case of multi-establishment firms, the questionnaire was applied to the specific unit of production that was randomly selected.

The protocol for the implementation of the survey required that the survey should be

> 56For example, Holbrook, Green and Krosnick (2003) use data from three experiments in the US and show that telephone respondents are less likely to cooperate and more likely to present themselves in socially desirable ways. Jackle, Roberts and Lynn (2006) show in a designed experiment that evaluate the differences between the two modes of data collection show also that telephone respondents are more likely to give socially desirable responses, which in our context is likely to result in an upward bias of technology use.

56
reveals certain stylized facts on the nature of the heterogeneous ability-schooling interaction. In particular, the hypothesized pattern is for high income countries to be associated with increasing returns by quantile (complementarity of ability and education) while for low income countries (which have less developed and possibly distorted labor markets) the tendency will be for decreasing returns (substitutability of ability and education). An implicit assumption in interpreting the results by quantile as decreasing (or increasing) returns is that the distribution of earnings for each level of schooling remains constant.

Table 1: Data Sources and Year of Survey

|_Country_|_Survey_|_Year _|
|---|---|---|
|Cambodia|Socio-economic Survey of Households|2003-5|
|China|Economic, Population, Nutrition and Health Survey|2000|
|Indonesia|_Survei Sosial Economi Nasional (Susenas)_|2003|
|Mongolia|Living Standards Measurement Survey|2002|
|Philippines|Annual Poverty Indicator Survey (APIS)|1999|
|Singapore|Labor Force Survey (LFS)|1998|
|Thailand|Socioeconomic Survey|2002|
|Vietnam|Survey of Households|2001-2|
|Argentina|_Encuesta Permanente a Hogares (INDEC)_|2003|
|Bolivia|_Mejoramiento de las Encuestas y Mediciones de las Condiciones de Vida (MECOVI)_|2002|
|Brazil|_Pesquisa Nacional Por Amostra De Domicilios (PNAD)_|2002|
|Chile|_Encuesta de Caracterización Socioeconómica Nacional_|2003|
|Colombia|_Encuesta de Calidad de Vida_|2003|
|Guatemala|_Encuesta Nacional sobre Condiciones de Vida (ENCOVI)_|2000|
|Mexico|_Encuesta Nacional de Ingresos y Gastos de los Hogares_|2002|
|Venezuela|_Encuesta de Hogarespor Muestro_|2002|

# **Descriptions**

Educational attainment in East Asia (Table 2) for those aged 25-65 years and employed in the formal sector, measured by years of schooling, ranges between 7.4 and 11.3 years (for Cambodia and China) for males with an 8 country average 9.8 years, and between 5 and 13.2 years (for Cambodia and Philippines) with an 8 country average of 9.9 for females. The proportion individuals employed for wages 25-65 years with tertiary education exhibits considerable variation, with Mongolia and the Philippines registering the highest proportion of tertiary education graduates for both men and women, while the lowest proportion is found in Cambodia (for both men and women), Indonesia and Thailand (males) and Singapore (females) – when Polytechnic diplomas are excluded. About 23 percent of working males and 30 percent of working females in East Asia has tertiary qualifications.

10
**7**

# Table 2 Annual Costs and Benefits of Social Regulation in the United States in 1988 **(in Billions** of 1988 Dollars)

|_Regulated Sector_|_Costs_|_Benefits_|_Sources_|
|---|---|---|---|
|Environment|55.4-77.6|16.5-135.8<br>(58.4)a|Hazilla & Kopp (1990);<br>Freeman (1990); Portney<br>(1990)|
|Highway Safety|6.4-9.0|25.4-45.7|Crandall (1986)|
|Occupational Safety and<br>Health (OSHA)|8.5-9.0|negligible|Crandall (1988); Denison<br>(1979); Viscusi (1983)|
|Nuclear Power|5.3-7.6|na|DOE policy study (1979<br>_(reported in_Litan &<br>Nordhaus (1983))|
|Drugs|< 1.5-3.0|na|Peltzman (1973)|
|Equal Employment<br>Opportunity (EEO)<br>l_|0.9<br>___________________|na<br> ____________________|Weidenbaum & DeFina<br>(1978); Litan & Nordhaus<br>____(1983)|
|Consumer Product<br>Safety<br>_|> .034<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _|na<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _|U.S. Federal Budget, FY<br>1990 (administrative costs<br>_ _o n ly)|
|Total|$78.0-107.1|$41.9-181.5||

## na not available Point estimate is in parentheses.

b Indicates primary source of estimates.

Source: Hahn and Hird (1991)

|Annual Costs of Fe<br>(in B|Table<br>deral Regul<br>illions of 19|3<br>ation in the Un<br>91 Dollars)|ited States||
|---|---|---|---|---|
|_Regulations_|_1977_|_1988_|_1991_|_2000_|
|Environmental Regulation|42|87|115|178|
|Other Social Regulation|29|30|36|61|
|Economic Regulation-Efficiency|120|73|73|73|
|Process Regulation|122|153|189|221|
|_Subtotal of Costs_|_313_|_343_|_413_|_533_|
|Economic Regulation-Transfers|228|130|130|130|
|Total Costs|**540**|**473**|_542_|662|

Source: Hopkins (1992)
and administrative data for poverty mapping in off census years. Fourth, we expand the validations from Corral et al. (2022) and illustrate how Fay-Herriot models are still the preferred approach for off-census years among traditional small area estimation methods. Finally, we present evidence on how, despite different performance across models, all models are beneficial for targeting. While some models may outperform others in their potential poverty reduction when relying on estimates for targeting, even the worst performing models achieve decent poverty reduction, but may come at a considerable cost.

The results found here should be caveated by the fact that they are dependent on the Mexican Intercensal data used. Results and model performance may differ considerably in a different country or using different data. It is quite possible that in less-developed economies the geo-referenced covariates perform better than what is observed here, but there is no guarantee this will be the case. Validating the usefulness of a particular type of data is not straight forward as it may not work as well everywhere, it is for this reason that comparisons here are made in a more holistic manner. When comparing the performance of geo-referenced covariates to other type of data one should use the same method, as is done here. On the one hand, we show how using census aggregates at the municipality level relying on gradient boosting comes very close to the best performing method’s estimates. On the other hand, using the same method with GIS covariates we show how it falls short of competing with a model where only census aggregates are used.

The paper opens considerable avenues for future research. One avenue is related to methodology. Specifically, how the noise of machine learning methods can be accurately estimated since the noise measures presented here rely on having the true poverty rates which is not possible in the vast majority of contexts. Second, the results suggest that most methods would do a solid job for spatial targeting type exercises. However, a key question is whether or not this will hold elsewhere and under what circumstances will one method outperform others.

31
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 18: Individual Average Earnings - Tradable Sector Worker Sample - 1990-2009 <!-- Start of picture text -->
1.1<br>0.9<br>0.7<br>0.5<br>0.3<br>0.1<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>-0.1<br>-0.3<br><!-- End of picture text -->

Each point reflects an individual regression coefficient where the dependent variable is the average earnings from 1990 to the year listed on the x-axis as a multiple of the worker’s average pre-liberalization (1986-89) earnings, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 19901995 (described in the text), with various controls (described in the text). Positive estimates imply larger declines in average formal earnings for workers initially employed in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

57
While the lists were quite a useful first step, they nevertheless still generated few additional details that could be used by an analyst to help determine whether such a measure was an economically important cause of a reduction to its country’s exports.

A second example was the World Bank’s continued sponsorship of the Global Antidumping Database, including a quarterly updating initiative begun in March 2009 that provided detailed information on new trade remedy investigations and trade barriers being imposed under antidumping, countervailing duty, and safeguard provisions.<sup>15</sup> The main distinction between the Global Antidumping Database and other initiatives was the level of detail. For example, by including information on Harmonized System (HS) tariff line product codes and the names of firms subject to the new trade restrictions (as well as those behind the investigating petitions), analysts could match the information on effect on the rights and obligations of Members, nor does it have any legal implication with respect to the conformity of any measure noted in the report with any WTO Agreement or any provision thereof. This report is without prejudice to Members' negotiating positions in the Doha Round. It is a preparatory contribution to the report by the Director-General that is called for in Paragraph G of the TPRM mandate and that aims to assist the TPRB to undertake an annual overview of developments in the international trading environment which are having an impact on the multilateral trading system.’ 15 In the interest of full disclosure, the author was the initiator of the Global Antidumping Database project in 2004 and has managed the project since its inception, with financial resources provided from the World Bank. Some seed funding for the project in 2004 came from Brandeis University. Since 2005, the Internetbased and freely available Global Antidumping Database has provided detailed, tariff-line, and firm-level data on national use of trade remedies such as antidumping, safeguards, and countervailing measures. In 2009, the initiative was significantly expanded to include additional policies and countries (which were using such policies more frequently in light of the global economic crisis), as well updating the information updating more often – i.e., quarterly. See Bown (2010) for more details.

15
When we consider all countries, Table 5 shows that the combined effect of R&D expenditure and New Businesses Registered (NBR) has a positive and significant effect to reduce the Technical inefficiency (in the Cobb-Douglass specification).

We observe a very different behavior between countries with MediumLow-Tech level and countries with High-Medium Tech level (using a TransLog production function). In countries with High-Medium Tech level, the R&D expenses have a positive and significant effect on technical efficiency (in the short and long term). In contrast, in countries with Medium-Low Tech level, the R&D expenses have a negative and significant effect on technical efficiency (in the short term).

When we use a Cobb-Douglass production function, in countries with High-Medium Tech level, the R&D expenses have a positive and significant effect on technical efficiency (only in the short term).

Finally, since earlier GEM reports demonstrated a systematic, U-shaped relationship between a country’s level of economic development and its level and type of entrepreneurial activity, there might be a potential reverse-causality issue. To address this issue, in applying a second step SFA procedure, we performed the Granger causality test which showed a statistically significant positive value of the Wald test on all coefficients of distributed lags<sup>16</sup> but only in the TransLog specification.

# 4.2. Efficiency and Dynamic Convergence Analysis

The issue here is to analyze the trend and dynamics of the distribution of technical efficiency, along the lines suggested by Quah (1993, 1997), that is, changes in the form of the distribution and distributional dynamic within that distribution,<sup>17</sup> based on the estimation of kernel density functions as proposed by Lucy, Aykroyd and Pollard (2002). The results are shown in Figure 2 and illustrate the trends in convergence (divergence) and persistence (mobility) in the level of technical efficiency attained by the countries during the period 2002-2005.

> 16The ratio between R&D expenses and the annual number of new firms - in the first OLS regression -  and a non- statistically significant value of the Wald test on all coefficients of distributed lags of country’s technical efficiency - in the second OLS regression.

> 17 Quah (1997) argues that convergence coalitions, or clubs, can form endogenously across all countries, and the different convergence dynamics will depend on the initial distribution of country characteristics.

15