rafmacalaba/datause-encoder-data huggingface dataset:
The data suggest a strong relationship between the implementation of a modern business registry and a significant increase in the number of new business registered. All three countries witnessed an increase larger than 20% in the number of new business registration after the full implementation of their modernization plans. It is also noteworthy that in Jordan and Guatemala, the growth of new firms begins before the implementation of the reform, usually about 4 years earlier when the modernization plan was announced and initiated.

**Figure 14: Number of New Businesses Incorporated**

<!-- Start of picture text --> Guatemala Sri Lanka Jordan Year of implementation of EBR<br>40000 9000<br>35000 8000<br>7000<br>30000<br>6000<br>25000<br>5000<br>20000<br>4000<br>15000<br>3000<br>10000<br>2000<br>5000 1000<br>0 0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database, 2007.

For example, Guatemala began its modernization plan in 1996, achieving e-registration and e-distribution in 1999 (see Box 5). Jordan, following a 1997 law, created a new entity in charge of business registration and entrepreneurship promotion that fully implemented the electronic registration in 2002. Sri Lanka, on the other hand, partially implemented its electronic business registration in 2001, in order to prepare for the new Companies Act of 2007.

In addition, in several countries the business registry has played a central role in private sector development strategies. Instead of being a passive actor, the registries have in many cases been entrusted with the task of fostering entrepreneurship through a variety of activities. Among others tasks, they provide an advisory role training potential entrepreneurs, are in charge of the dissemination of information, promote foreign investment, reduce bureaucratic barriers, etc.

30
in the 1990s cohort. Evidence on India, by far the largest country in the region, suggests that intergenerational mobility remained largely unchanged over the three birth cohorts. This is striking because following extensive economic reforms including dramatic trade liberalization and domestic deregulation in 1991, India reaped impressive economic growth and poverty reduction in the decades of 1990s and 2000s during which the children of the 1980s and 1990s birth cohorts went to school.<sup>37</sup> The evidence thus suggests that the gains in growth and poverty reduction failed to translate into better educational opportunities for the children of liberalization in India.<sup>38</sup> As noted earlier, Sub-Saharan Africa as a region also did not experience any substantial improvements over the three decades. Again, the average estimates conceal substantial country level diversity in mobility experiences. We observe some of the most dramatic declines in intergenerational educational mobility in this region. For example, sibling correlation in Mozambique increased from 0.52 in the 1970s cohort to 0.68 in the 1990s cohort, and in Nigeria from 0.64 (1970s) to 0.74 (1990s). There are also a number of countries in this region that experienced substantial improvements. For example, sibling correlation in Uganda declined from 0.65 (1970s) to 0.55 (1990s), and in Tanzania, from 0.56 (1970s) to 0.48 (1990s). Out of 27 countries in this region for which we have estimates for both the 1970s and the 1990s cohorts, 16 countries registered improvements, while 10 experienced a setback in intergenerational educational mobility.

> 37Based on Indian government ocial poverty line, the proportion of poor people in rural areas declined from 47 percent in 1983 to 28 percent in 2004-2005. The corresponding decline in urban India is from 42 percent in 1983 to 26 percent in 2004=2005. See Bank (2011).

> 38This evidence of no signicant improvements in educational opportunities in India is in contrast to the evidence of substantial improvements based on the estimates of intergenerational regression coecient (IGRC) in educational attainment reported by Azam and Bhatt (2015), Jalan and Murgai (2008), Kishan (2018), and Maitra and Sharma (2010). However, this conclusion is consistent with the analysis of Emran and Shilpi (2015) which uses two rounds of DHS (called NFHS in India) surveys (1992/93 and 2006) and focuses on the 16-27 year old children in the survey year. Using sibling correlation and intergenerational correlation (IGC), they show that there has been almost no change in educational opportunities from 1992/93 to 2006. Since IGRC and IGC are partial measures and cannot take into account many factors shared by the siblings, one can make a plausible argument in favor of the conclusions based on sibling correlation estimates. We will discuss later the changes in the share of intergenerational correlation (IGC) over time in our sibling correlation estimates. Please see section 6 below.

21
In a second exercise, we use estimates from the pre-Great Recession model to then show the expected trade policy response during 2008:Q4-2010:Q4 given the realized macroeconomic shocks. The historical models for the US and EU in particular predicted a sharp increase in import protection taking place beginning in 2009:Q2 and lasting through 2010:Q1, peaking in 2009:Q3. While the increase in import protection during the Great Recession was not as large as the potential additional 15 percentage point coverage of imports that some historical models predicted, arguably the fear of this realization contributed to the pre-emptive “anti-protectionism” G20 declaration of November 2008 and a number of initiatives to actively monitor trade policy (by the WTO, World Bank, and Global Trade Alert) beginning in early 2009.

Third, we re-estimate the models on data from the Great Recession period in order to explain deviations to the realized protectionist response away from historical behavior. We do not find robust evidence of a change to the countercyclical relationship between _domestic_ unemployment and new import protection. However, we do provide evidence that these policy-imposing economies “switched” from their historical behavior; during the Great Recession they shifted new import protection away from those trading partners that were contracting and toward those experiencing relatively stronger economic growth. This evidence, in addition to the sharp and persistent real depreciations of the US dollar and euro after their initial run-up in 2009, were particularly important contributors to the relatively low levels of import protection that arose given that so many trading partners were themselves experiencing periods of severe economic contraction during 2008-2010.

Finally, our results contribute to a substantial literature that has evolved to explain the role of trade agreements and the use of temporary trade barriers under the WTO. Our paper identifies the importance of bilateral channels through which new trade barriers arise, and how these channels differed in 2008-2010 relative to periods before the Great Recession. Nevertheless, even this research is just one additional step in understanding the role of trade agreements, including the potential limits to what trade agreements can accomplish in maintaining trade policy cooperation across countries. Indeed, economies like the United States and European Union are no longer the “biggest” users of temporary trade barrier policies. Whether the major new emerging economy users of these policies – India, China, Brazil, Argentina, and Turkey, for example – respond similarly to macroeconomic shocks remains an open question.

26
|Table 10: Impact of Trade Exposure on Produc|tivity: Tanzania|
|---|---|
|Plant-Level TFP<br>|Plant-Level TFP<br>|
|Exporter<br>0.539<sup>*</sup>|0.526<sup>*</sup>|
|(0.253)|(0.262)|
|Exporter*2009<br>-0.489|-0.497|
|(0.394)|(0.397)|
|Exporter*2010<br>-0.197|-0.192|
|(0.398)|(0.399)|
|Exporter*2011<br>0.252|0.251|
|(0.381)|(0.381)|
|Exporter*2012<br>-0.299|-0.286|
|(0.384)|(0.390)|
|Exiting firm<br>-0.0891|-0.101|
|(0.136)|(0.150)|
|Exporter*Exiting Firm|0.0699<br>(0.362)|
|Year Fixed Effects<br>Yes|Yes|
|Industry Fixed Effects<br>Yes|Yes|
|Observations<br>789<br>|789|
|_R_<sup>2</sup><br>0.095|0.095|

Standard errors in parentheses * _p_ < 0.05, ** _p_ < 0.01, *** _p_ < 0.001 33
Chen, Zhihong, Ying Ge, and Huiwen Lai Chi Wan (2013), “Globalization and Gender Wage Inequality in China,” _World Development_ , 44: 256-266.

Chioda, Laura, Rodrigo Garcia-Verdú, and Ana María Muñoz Boudet (2011), _Work and Family: Latin American Women in Search of a New Balance._ Office of the Chief Economist and Poverty Gender Group, LAC. Washington, DC: World Bank Cho, S-Y. (2010), "International Human Rights Treaty to Change Social Patterns – The Convention on the Elimination of All Forms of Discrimination against Women," _Center for European, Governance and Economic Development Research. Discussion paper no. 93_ Clark, Roger, Thomas W. Ramsbey, and Emily S. Adler (1991), “Culture, Gender, and Labor Force Participation: A Cross-National Study,” _Gender and Society_ , 5:47-66.

Cohen, Wesley M. and Steven Klepper (1996), “A Reprise of Size and R&D,” _Economic Journal_ , 106(437): 925-951.

Coleman, S. (2007), “The Role of Human and Financial Capital in the Profitability and Growth of Women-Owned Small Firms,” _Journal of Small Business Management,_ 45(3):303-319.

Contreras, Dante and Gonzalo Plaza (2010), “Cultural Factors in Women's Labor Force Participation in Chile,” _Feminist Economics_ , 16(2): 27-46.

Cooray, A., Gaddis, I. and Wacker, K. M. 2012. Globalization and Female Labor Force Participation in Developing Countries: An Empirical (Re-) Assessment. Courant Research Center, Discussion Paper 129. University of Göttingen.

Díaz-Mayans, M. A. and Sánchez R. (2008), “Firm Size and Productivity in Spain: A Stochastic Frontier Analysis, _Small Business Economics,_ 30(3): 315-323 **.** Do, Quy-Toan, A. Levchenko and C. Raddatz (2011), “Engendering Trade,” Policy Research Working Paper No. 5777, World Bank, Washington DC.

Dollar, David and Aart Kraay (2003), “ **Institutions, trade, and growth,”** _Journal of Monetary Economics_ , Volume 50, Issue 1, January 2003, Pages 133–162.

Dollar, David and Roberta Gatti (1999), “Gender Inequality, Income, and Growth: Are Good Times Good for Women?” Working Paper Series No. 1, Policy Research Report on Gender and Development, World Bank, USA.

Duflo, Esther (2003), “Grandmothers and Granddaughters: Old Age Pension and Intra-Household Allocation in South Africa,” _World Bank Economic Review_ , 17(1): 1-25.

Duflo, Esther (2005), “Gender Equality in Development,” BREAD Policy Paper No. 11, 2005.

33
**Figure 5. Cross-border flows and extensive margin of syndicated loans**

<!-- Start of picture text --> Panel A: LAC to North Panel B: LAC to South<br>7 4.0% 1.4 0.30%<br>6 3.5% 1.2 0.25%<br>3.0%<br>5 1.0<br>0.20%<br>2.5%<br>4 0.8<br>2.0% 0.15%<br>3 0.6<br>1.5%<br>0.10%<br>2 0.4<br>1.0%<br>0.05%<br>1 0.5% 0.2<br>0 0.0% 0.0 0.00%<br>Panel C: North to LAC Panel D: South to LAC<br>120 16% 10 1.2%<br>9<br>14%<br>100 1.0%<br>8<br>12%<br>7<br>80 0.8%<br>10% 6<br>60 8% 5 0.6%<br>4<br>6%<br>40 0.4%<br>3<br>4%<br>2<br>20 0.2%<br>2% 1<br>0 0% 0 0.0%<br>Panel E: LAC to LAC<br>4 3.0%<br>2.5%<br>3<br>2.0%<br>2 1.5%<br>1.0%<br>1<br>0.5%<br>0 0.0%<br>% of Active Connections Total Flows<br>% of Active Connections % of Active Connections<br>Value (2011 Constant U.S. Dollars Billions) Value (2011 Constant U.S. Dollars Billions)<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>% of Active Connections % of Active Connections<br>Value (2011 Constant U.S. Dollars Billions) Value (2011 Constant U.S. Dollars Billions)<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>% of Active Connections<br>Value (2011 Constant U.S. Dollars Billions)<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> This figure shows the evolution of the extensive margin for syndicated loans and the evolution of the total value of these connections. The extensive margin is represented by the percentage of active connections, that is, the number of country-pairs that have a positive flow in each year over the number of country-pairs with a positive or zero flow in the last year of the sample. North countries are the G7 and Western Europe. South countries are all the others (excluding LAC countries). Offshore centers are excluded from the sample. Flows are expressed in 2011 constant U.S. dollars.
Table 5

||Determ<br>Cha<br>|inants of<br>nge in the log|Wage Incre<br>De<br>of average l|ases in Sen<br>pendent varia<br>abor costs pe|egal<br>ble:<br>r worker in f|ormal sector|firms|
|---|---|---|---|---|---|---|---|
|Explanatory variables|(A)|(B)|(C)|(D)|(E)|(F)|(G)|
|Change inthelog of|0.9149 **|||0.6586**|0.6007**||0.5760**|
|consumer prices|(4.070)|||(2.313)|(2.234)||(2.087)|
|Change in the log of||0.5938**||0.3014||0.6541|0.1497|
|minimum wages||(3.647)||(1.465)||(0.886)|(0.404)|
|Change in the log of|||0.7219**||0.6149**|0.4737 **|0.5877 **|
|average government wages|||(3.288)||(2.746)|(4.385)|(2.512)|
|Change in the terms|0.8658**|0.3077|0.8483 **|0.5640|1.0715 **|0.8264 **|1.0522 **|
|of trade (lagged)|(2.540)|(0.8025)|(2.077)|(1.417)|(2.558)|(2.019)|(2.493)|
|Change in output at|1.3480 **|0.2334|0.0690|0.8140|0.6825|-0.0152|0.6180|
|constant prices (lagged)|(2.437)|(0.380)|(0.106)|(1.230)|(0.969)|(-0.023)|(0.855)|
|Independent|-0.0186|0.0390|0.0217|-0.0048|0.0511**|0.0142|-0.0250**|
|term|(-0.629)|(1.591)|(0.806)|(-0.156)|(3.306)|(0.502)|(-0.740)|
|Adjusted R<sup>2</sup>|0.030|0.026|0.019|0.032|0.027|0.019|0.025|
|F test|8.17|7.07|4.42|6.68|4.59|3.51|3.70|
|Number of<br>observations|686|686|518|686|518|518|518|

Note: Data on average labor costs per worker are from the _Banque de Donnees Economiques et Financieres,_ which is composed of about one thousand medium and large firms. There is one observation per industry per year, over period 1975-1994. Values in parentheses are "t" statistics. Significant coefficients at the _5_ and 1 percent level are indicated by one and two asterisks respectively.

22
also an upward sloping curve, but it is more striking than in the case of core NTBs (the number of countries is also much smaller as noti fi cations to the WTO of only 24 countries were used in Hoekman, Ng and Olarreaga (2004), which is our data source). Moreover, the contribution of core NTBs and agricultural domestic support to the overall level of protection (that includes tariffs) also increases with GDP per capita (see Figure 4). Thus as countries become richer the relative trade restrictiveness of NTBs becomes more visible. However, the overall level of protection Tn,c still decreases with GDP per capita, mainly driven by average tariff levels than tend to be signi fi cantly lower as countries grow richer.

The variation across goods (tariff lines) is also very large. The average level of AVEs in agricultural products is 20 percent compared to 8 percent for manufacturing goods. The overall level of protection (including tariffs) is also much higher for agriculture (38 percent versus 17 percent). The highest average AVE of NTBs at the 2 digit level of the HS is found for dairy products (HS 4) with an average of 38 percent (an average tariff of 29 percent bring the average level of protection for dairy products in the world to 67 percent). The lowest average AVE of NTB at the 2 digit level of the HS is found for tin and products thereof (HS 80) with an average of 3 percent (and an average level of overall protection of 10 percent once tariffs are included). Also, the contribution of NTBs to the overall level of protection is higher in agriculture.

One question that one may ask is whether these different instruments of protection (tariffs, core NTBs and agricultural domestic support) are complements or substitutes in terms of their trade restrictiveness. In order to answer this question, we run a simple within country regression of tariffs on AVE of core NTBs and AVE of agricultural domestic support using country dummies. The results are reported in Table 2. They suggest that (within countries) tariffs do tend to increase with both AVE of core NTBs and agricultural domestic support, reinforcing each other, rather than substituting for each other.

It is difficult to provide external tests for our estimates as exercises providing AVEs of NTBs at the tariff line level are nonexistent to our knowledge. However, we can compare country averages provided by Bradford (2003), which estimates AVE using price differentials for Australia, Canada, Japan, United States and 5 European countries (Belgium, Germany, Italy, Netherlands and the United Kingdom). These AVEs are computed using price differential between retail prices and import prices, after correcting for transport, taxes and other distributions costs. By de fi nition 18
- 20 -

<!-- Start of picture text --> Figure 9. Overall and agricultural electricity consumption<br>(In MWh)<br>30<br>25 y = 0.453 x + 2.750<br>(0.063) (0.576)<br>20<br>R 2 = 0.409<br>15<br>10<br>5<br>0<br>0 5 10 15 20 25 30 35<br>Agricultural electricity consumption per worker active in<br>agriculture sector<br>economy per capita<br>Electricity consumption of the<br><!-- End of picture text --> Source: Author's calculation based on _Energy Statistics_ and _WDI_ .

In addition to the above four variables, the traditional general infrastructure variables are also taken from _World Development Indicators_ : road density, teledensity, electricity consumption per capita and water access. Table 5 summarizes the data availability of our infrastructure variables. When the lagged value is not available, our methodologies cannot be applied. This is unavoidable because available infrastructure data are quite limited.

<u>Table 5. Some available infrastructure variables</u>

||Lagged<br>(before2000)<br>|Current<br>(2001-05)<br>|
|---|---|---|
|Road density|√|√<br>|
|Rural access index||√|
|Teledensity|√|√|
|Rural teledensity|√||
|Electricity consumption|√|√|
|Agri. power consumption|Limited|Limited|
|Water access|√|√|
|Ruralwateraccess|√|√|

**Other variables.** Weather data are provided from the National Oceanic and Atmospheric Administration (NOAA) database, the Global Historical Climatology Network (GHCN) Version 2. We create four variables to control regional heterogeneity among our sample countries: average deviation of summer/winter temperature from the long-term trend in 19902000, and average deviation of summer/winter precipitation from the long-term trend in
internationalized, either measured by the ownership structure, locational characteristics, or by export propensity.

**Table 14: Characteristics of Survey Companies**

||High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|_Ownership_<br>|||||
|Gov. have a financial stake|33|40|33|36|
|Foreign company have a financial stake|6.7|25|-|12|
|Foreign controlled|13|20|6.7|14|
|_Location_|||||
|Firm has facilities elsewhere|67|70|53|64|
|Headquarters is outside Beijing*|13|10|13|12|
|_Export Propensity_|||||
|Firms exporting|33|35|20|30|
|_Shanghai_|||||
|_Ownership_|||||
|Gov. have a financial stake|29|-|30|24|
|Foreign company have a financial stake|10|-|5|6|
|Foreign controlled|62|100|65|70|
|_Location_|||||
|Firm has facilities elsewhere|71|67|75|72|
|Headquarters is outside Shanghai*|50|-|25|12|
|_Export Propensity_|||||
|Firms exporting|62|11|70|56|

_Note_ : *As to foreign controlled companies, the headquarter refers to regional headquarter in China The size of firms surveyed is shown in Table 15. Most of the interviewees refused to provide information on their sales revenue, while 46 interviewees in Beijing and 44 in Shanghai answered the question by giving the number of employees in the company. The percentage reflects the proportion to the respondents. Overall, the sample includes representative shares of large, medium and small firms in all three sectors. The majority of the firms surveyed are in the small-medium category, in terms of number of employees. The sample of large firms is greater in Shanghai than in Beijing, which is representative of the situation in the two cities.

The sampled firms both in Beijing and Shanghai are on the whole quite young, about half having been established less than 5 years ago, while few firms have been in existence for more than 20 years (Table 16). This is more marked in the case of the respondent firms in the knowledge intensive business services, in line with the fact that the service sector started to grow in importance in the urban economy only in recent years.

28
**Table 3a: The distribution of ANC attendees in the 2000 DHS vs. women in the 2001 sentinel survey by age group/gender, %**

|||**_DHS_**||||**_Sentinel Su_**|**_rvey_**|||**_weight_**||
|---|---|---|---|---|---|---|---|---|---|---|---|
||<br>**Edu**|**cation leve**|**l**||<br>**Edu**|**cation leve**|**l**||<br>**Edu**|**cation lev**|**el**|
|**Age group**|no education|primary|secondary+|Total|no education|primary|secondary+|Total|no education|primary|secondary+|
|15-19|2.18|11.52|1.17|14.87|1.91|15.71|3.77|21.39|1.14|0.73|0.31|
|20-24|6.60|22.82|4.12|33.54|5.16|23.88|8.93|37.97|1.28|0.96|0.46|
|25-29|7.89|15.37|2.10|25.36|4.80|14.26|3.53|22.59|1.64|1.08|0.59|
|30-34|4.74|7.29|0.32|12.35|3.59|6.78|1.21|11.58|1.32|1.08|0.26|
|35-39|4.24|4.54|0.21|8.99|2.18|2.65|0.29|5.12|1.94|1.71|0.72|
|40-44|1.81|1.49|0.00|3.30|0.77|0.49|0.01|1.27|2.35|3.04|1.00|
|45-49|0.98|0.62|0.00|1.59|0.04|0.04|0.00|0.08|1.00|1.00|1.00|
|Total|28.44|63.65|7.91|100.00|18.44|63.81|17.75|100.00||||

Note: The sample sizes are: 2664 (2000 DHS); 7285 (2001 sentinel survey); only women age 15 to 49 ANC attenders (DHS) are women who had a birth in the last year and who had a prenatal care from a professional source (doctor/nurse/midwife); All tabulations using DHS data are weighted For cells with less than 20 observations we do not reweight the data (i.e., the weight of unity is assigned)

**Table 3b: The distribution of all women in the 2000 DHS vs. women in the 2001 sentinel survey by age group/gender, %**

|||**_DHS_**||||**_Sentinel Su_**|**_rvey_**|||**_weight_**||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Edu**|**cation leve**|**l**||**Edu**|**cation leve**|**l**||**Edu**|**cation lev**|**el**|
|**Age group**|no education|primary|secondary+|Total|no education|primary|secondary+|Total|no education|primary|secondary+|
|15-19|1.66|16.94|3.09|21.69|1.91|15.71|3.77|21.39|0.87|1.08|0.82|
|20-24|4.15|14.23|3.99|22.37|5.16|23.88|8.93|37.97|0.80|0.60|0.45|
|25-29|5.45|10.85|1.86|18.16|4.80|14.26|3.53|22.59|1.14|0.76|0.53|
|30-34|4.13|6.89|0.82|11.85|3.59|6.78|1.21|11.58|1.15|1.02|0.68|
|35-39|4.33|5.76|0.69|10.77|2.18|2.65|0.29|5.12|1.99|2.17|2.38|
|40-44|3.75|3.82|0.40|7.97|0.77|0.49|0.01|1.27|4.87|7.80|1.00|
|45-49|3.57|3.36|0.26|7.19|0.04|0.04|0.00|0.08|1.00|1.00|1.00|
|Total|27.04|61.86|11.11|100.00|18.44|63.81|17.75|100.00||||

Note: The sample sizes are: 13220 (2000 DHS); 7285 (2001 sentinel survey); only women age 15 to 49 All tabulations using DHS data are weighted For cells with less than 20 observations we do not reweight the data (i.e., the weight of unity is assigned) 24