rafmacalaba/datause-encoder-data huggingface dataset:
-26-

The **first** performance comparison is for the same enterprise before and after divestiture. It builds on the notion that the enterprise most similar to the divested firm is the enterprise itself before divestiture. Therefore, any changes that may be observed within the firm following divestiture could be largely attributed to the change in ownership. This conclusion would particularly hold when associated with stable macroeconomic conditions and similar sectoral characteristics before and after divestiture.

# Performance ComRarisons and Most Likely Related Explanatorv Variable

|||PERFO<br>|RMANCE C<br>|OMPARISONS<br>||
|---|---|---|---|---|---|
||Same Firm<br>Before/<br>After|Divested/<br>Undivested<br>Firms,Same<br>Sector,Same|Same<br>Firm<br>With/<br>Without|Divested<br>Firms,<br>Diff. Sectors,<br>Same Country|DivestedFirms,<br>Diff. Sectors,<br>Diff. Countries|
|VARIABLES||Country|Divest.|||
||(1)|(2)|(3)|(4)|(5)|
|1. Ownership|x|x|x|||
|2. Sectoral||||x||
|Charact'|B8|||||
|3. Macroecon<br>Conditio|omic<br>ns|x|||x|

4. Transaction (Likely to influence outcomes through its influence on 1-3) The second comparison corresponds to the Bishop and Kay study, cited above, with one basic difference. That is, the comparison proposed here is between divested and undivested firms in the same sector (e.g., in the textile sector, rather than in the textile and electricity sectors) and the same country (e.g., Chile). This comparison would further substantiate the conclusion of
The third measure consists of three dummy variables capturing motivations for going into business. Owners who entered self-employment for the chance of business growth may be more likely to become formal than those who entered self-employment in order to have flexibility to care for family or carry out household tasks.

Additional measures of family background are whether or not their father was a business owner (34 percent say yes), and a childhood poverty index, intended to capture family wealth well before the time of business entry. The childhood poverty index is a principal component based on mother’s education, questions about the type of floor their house had as a child (60 percent had a dirt floor, 15 percent cement, while others had mosaic, tile, or wood); and the frequency with which they didn’t have enough to eat as a child (31 percent say they never went hungry, 54 percent said sometimes, and 15 percent say almost always or always). This index has a correlation of -0.48 with own education, 0.41 with speaking an indigenous language as a child, and -0.15 with log capital stock, suggesting it is measuring family wealth to some extent.

# **3. The registration process in Bolivia in theory and practice**

In order to be fully formal, a firm in Bolivia must register with three different Government agencies, while those with employees are required to register their employees with three additional agencies. The three main steps to formalization are

- 1) Registering with the municipal government ( _Alcaldia_ ) to obtain a municipal business license,

- 2) Registering with the tax authorities ( _Servicios de Impuestos Nacional (SIN)_ ) to get a tax identification number (NIT), and

- 3) Registering in the registry of commerce ( _Fundempresa_ ).

Firms with employees must take the further steps of registering them for health benefits with the National health system (CNS), for social security with the Pension fund (AFP), and registering them with the Labor Ministry.

However, out of our sample of 469 firms, only 13 have completed the three steps listed here, and only 5 also have workers registered with the three additional agencies. In

- 7 -
# Table of Contents

|I.|Intmduction...................................|........<br>1|
|---|---|---|
|H.|The owing lmportanceofOECD Markets................|.............<br>2|
|m.|PSUExports byRepublics............................|............8|
|IV.|TarffBarersFacingFSUExports<br>.<br>...................|..............<br>12|
|V.|Trade Barrier Escaation and FSU Commodity Exports..........|...........16|
|VI.|No<-tariff Barriers Facing FSU Exporters .........<br>.................|....<br>18|
|VII.|Trade Barriers Facing Individual Republics .........<br>..............|......<br>21|
|VII.|Concluding Commets.............................|.............<br>24|
||References .....|28|
||Staitsdcal Appendix ............................................|30|

U
_Environmental standards_ are our final category. Here we look at the stock of _ISO 14000_ certifications per capita and the number of _UNEP_ conventions signed, complemented with the Food and Agriculture Organization’s data on _pesticide_ use per 1000, WDI data on the share of _renewables_ in electricity production and _water_ withdrawals relative to internal resources, and WHO data on _air pollution_ measured as particulate matter with diameter of 2.5 μm or less in urban areas.

# **4. Results**

## **4.1. Overall Results**

Focusing first on GVC integration, table 1 shows the results for the full sample of 61 OECD countries covering the years 1995, 2000, 2005, and 2008–11 (columns 1 and 2) and those for the full sample of 40 WIOD countries covering the period 1995–2011 (columns 3 and 4).<sup>8</sup> The production factors labor and capital significantly increase domestic value added. The same applies to both our trade-related variables foreign value added for domestic processing ( _FVADP_ ) and—most importantly—our two GVC measures of backward ( _FVAX_ ) and forward links ( _DVAR_ ). In other words, GVC integration helps increase a country’s domestic value added and thus fosters economic upgrading

**Table 1. Overall impact of GVC participation on economic upgrading**

|VARIABLES|(1)<br>_DVA_|(2)<br>_DVA_|(3)<br>_DVA_|(4)<br>_DVA_|
|---|---|---|---|---|
||**OECD**||**WIOD**||
|_FVAX_|0.0685***||0.0428***||
||(0.0091)||(0.0129)||
|_FVADP_|0.252***|0.232***|0.124***|0.142***|
||(0.0268)|(0.0257)|(0.0170)|(0.0207)|
|_DVAR_||0.217***||0.157***|
|||(0.0160)||(0.0377)|
|_Employment_|-|-|0.322***|0.273***|
||-|-|(0.0598)|(0.0497)|
|_Capital Stock_|-|-|0.295***|0.257***|
||-|-|(0.0454)|(0.0464)|
|_Constant_|5.869***|5.179***|2.663***|2.564|
||(0.180)|(0.183)|(0.451)|(0.417)|
|Observations|7,276|7,284|10,455|10,514|
|R-squared|0.852|0.870|0.885|0.894|

Sources: OECD ICIO database and WIOD. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms. WIOD sample values in constant terms.

Focusing on the OECD sample, a 10 percent increase of GVC integration as a buyer increases domestic value added by 0.7 percent (column 1). Using the WIOD sample, we similarly observe that a 10 percent increase of GVC integration as a buyer leads to domestic value added gains of 0.4 percent (column 3). This is in line with our results in the next sections where robustness exercises using WIOD largely confirm the estimates obtained with OECD data.

GVC integration as a seller also substantially increases domestic value added (columns 2 and 4). Note that the elasticity is higher than those of all other control variables, while it was smaller when using the

> 8 For the country and industry coverage of the data set, see appendixes 2 to 4.

18
Policy ReseaRch WoRking PaPeR 4426

# **Abstract**

Using 40 years of historical rainfall data, this paper estimates a distribution for payouts on rainfall insurance policies offered to farmers in the State of Andhra Pradesh, India, in 2006. The authors find that the contracts primarily protect households against extreme tail events; half the expected value of indemnities paid by the insurance are generated by only 2 percent of rainfall realizations. Contract payouts are significantly correlated cross-sectionally, and also inversely associated with real GDP growth. The paper discusses the implications of these findings for the potential benefits of insurance to households, the risks facing a financial institution underwriting rainfall insurance contracts, and pricing.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the group to study microinsurance products. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xgine@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
In the rest of this paper we analyze the impact of this complex intervention and the process of change it attempted to initiate. In the next section we outline the analytical design of the quantitative and qualitative analysis. In section 3, we briefly report the quantitative findings demonstrating the lack of measured impact from the intervention. The fourth section analyzes the qualitative analysis, and the fifth section concludes the paper and summarizes the findings.

# <u>Analytic Design</u>

# <u>Quantitative Analysis</u>

The quantitative sample was chosen from among the poorest districts of Karnataka State: Gulbarga, Raichur, Bidar, Davengere and Chitradurga. From these districts the 20 poorest talukas (sub-districts known as blocks in most parts of India) were chosen for the study because they were also selected as “backward” by the Government of India and therefore had access to a large infusion of public funds from the MNREGA and BRGF. To keep administrative differences between taluka’s constant, two or three GPs (depending on the taluka’s population) were randomly chosen from each taluka to be assigned to the treatment sample, and the same number chosen from the same taluka as control GPs. This resulted in a total sample of 100 GPs, with 50 randomly assigned for treatment. To avoid the possibility of spillover effects, control GPs were constrained in the sampling process to be located at least one GP away from the 2 treatment .

While the intervention was targeted at the GP level, two villages from each GP were selected for the survey. The first village was the GP headquarters, typically the largest village in the GP, and the second village chosen at random from the other villages in the GP (a GP typically has between 3-5 villages). Within each village twenty households were chosen at random for the household survey, and 2 village leaders, the GP president and the vice-president, were also interviewed. This resulted in sample size of 100 treatment and 100 control villages 3 selected from 50 treatment and 50 control GPs, and 4000 households.

The baseline survey was administered in October-November 2007. Our aim was to complete the survey before the initial visits to the villages by KSIRD in late November 2007. However, in some villages, due

> 2 Our ethnographic work found no evidence of spillovers from the intervention.

> 3 Selection is such that villages with higher population have a proportionally higher probability of being selected.

7
establish the guidelines to be followed so that the setting of standards is not performed with protectionist intentions. These agreements regulate voluntary standards, technical regulations and conformity assessment activities. They do not cover services and do not apply to government procurement activities. They are obligatory for all WTO members, though are not yet strongly enforced.

The SPS Agreement covers all measures whose purpose is to protect: human or animal health from food-borne risks; human health from animal- or plant-carried diseases; or animals and plants from pests or diseases. The TBT Agreement covers all technical regulations, voluntary standards and the procedures to ensure that these are met, except when these are sanitary or phytosanitary measures as defined by the SPS Agreement.<sup>4</sup> The six main principles of the TBT and SPS rules are:

- 1- Non-discrimination requirement.

- 2- Proportionality requirement: regulations must not be more burdensome than necessary.

- 3- Regional and national standards should be based, to the greatest extent possible, on international standards.

- 4- Sanitary regulations must be based on available scientific evidence.

5- Countries are encouraged to recognize each other’s standards and testing procedures as equivalent – i.e. if two countries have different ways to fulfill the same objective, they should accept each other’s ways of fulfilling it.

- 6- Enhance the exchange of information among the member governments and increase transparency in the standards-setting process.

With regard to transparency and information systems, countries are required under the WTO TBT arrangement to establish enquiry points or centers of information to answer queries and provide relevant documentation in a timely and cost-effective manner. Most of the RTAs in the Americas have established enquiry points and encourage member countries to make notifications concerning standards-related measures (Kotschwar 2001).

The TBT and SPS disputes are becoming more prominent in WTO disputes. During the first five years of operation of the WTO’s Dispute Settlement Understanding (DSU), 25 cases (13 percent of the total) referenced standards-related provisions (Kotschwar 2001).

# **2 DIFFERENT APPROACHES TO THE UPGRADING OF STANDARDS AND RECOGNITION OF CONFORMITY ASSESSMENT PROCEDURES**

This section will first examine different approaches to standards upgrading and will then examine different approaches to the recognition of conformity assessment procedures.

> 4 Taken from http://www.wto.org/english/tratop_e/sps_e/spsund_e.htm

8
||<br>Measures of<br>**Capital flows**<br>**Trade flows**|Table 1.<br>global i<br>**Transportand co**|ntegration<br>**mmunications costs **|<br>**(constantUS $)**|
|---|---|---|---|---|
||**Foreign**<br>**assets/world GDP**<br>(in percent)<br>**Trade/GDP**<br>(in percent)|**Sea freight**<br>(average ocean freight and p<br>charges per ton)|ort<br>**Air transport**<br>(average revenue per<br>passenger mile)|**Telephone call**<br>( 3min NY/London)|
|**1820**|-<br>2a|-|-|-|
|**1870**|6.9<br>10a|-|-|-|
|**1890**|-<br>12b|-|-|-|
|**1900**|18.6<br>-|-|-|-|
|**1914**|17.5<br>18ab|-|-|-|
|**1920**|-<br>-|95|-|-|
|**1930**|8.4<br>18a|60|0.68|245|
|**1940**|-<br>-|63|0.46|189|
|**1945**|4.9<br>-|-|-|-|
|**1950**|-<br>14a|34|0.3|53|
|**1960**|6.4<br>16b|27|0.24|46|
|**1970**|-<br>22.4a-20b|27|0.16|32|
|**1980**|17.7<br>-|24|0.1|5|
|**1990**|-<br>26ab|29|0.11|3|
|**1995**|56.8<br>-|-|-|-|
|**Source**|Crafts (2000)<br>a.Maddison (1995)<br>b.Crafts (2000)|UNDP (1999)|UNDP (1999)|UNDP (1999)|

The first great wave of modern globalization ran from about 1870 to 1914. It was spurred by the development of steam shipping and by an Anglo-French trade agreement. In this period the world reached levels of economic integration comparable in many ways to those of today. The volume of trade, relative to world income, nearly doubled from 10% in 1870 to 18% on the eve of World War I (Table 1). There were also large capital flows to rapidly developing parts of the Americas, and the ownership of foreign assets (mostly Europeans owning assets in other countries) more than doubled in this period from 7% of world income to 18%. Probably the most distinctive feature of this era of globalization was mass migration. Nearly 10% of the world’s population relocated permanently in this era. Much of this was migration from poor parts of Europe to the Americas. But there was also considerable migration out of China and India (much of it forced migration in the latter case). It is important to keep in mind that while global indicators showed considerable integration in the 1870-1914 period, this was also the 5
**Table A5. Validation of Outcomes Reported by Principals Using Administrative Data and Household Surveys**

||Outcome data source|Survey Data<br>(WBSCPS)|Survey Data,<br>employment<br>rate|Administrative<br>Data|HH Survey<br>(21-25 years old)|HH Survey<br>(25-29 years old)|HH Survey<br>(21-29 years old)|
|---|---|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|(5)|(6)|
||Graduation rate (%)|62.19||30.30||||
||Dropout rate (%)|14.90||||||
|Brazil|Extra time to graduate (%)|11.99||||||
||Employment rate (%)|73.52|69.65|65.6|76.00|82.00|80.00|
||Wages (USD,PPP 2019)|10,730.00||10,006.40*|10,155.00|13,440.00|12,472.00|
||Graduation rate (%)|58.41||61.00||||
||Dropout rate (%)|14.25||23.00||||
|Colombia|Extra time to graduate (%)|24.04||25.00||||
||Employment rate (%)|57.25|65.42|63.00|75.00|78.00|77.00|
||Wages (USD,PPP 2019)|10,313.00||12,192.00|8,892.00|10,212.00|9,684.00|
||Graduation rate (%)|58.49||||||
||Dropoutrate (%)|14.95||||||
|Dominican<br>Republic|Extra time to graduate (%)<br>Employment rate (%)|30.97<br>52.50|65.42|||||
||Wages (USD,PPP 2019)|11,275.00||||||
||Graduation rate (%)|54.71||||||
||Dropoutrate (%)|12.45||||||
|Ecuador|Extra time to graduate (%)|18.52||||||
||Employment rate (%)|38.56|53.40|40.18|48.00|63.00|57.00|
||Wages (USD,PPP 2019)|11,910.00||10,900.00|<br>10,222.00|<br>13,689.00|<br>12,511.00|
||Graduation rate (%)|64.06||||||
|Peru|Dropoutrate (%)<br>Extra time to graduate (%)|12.82<br>8.95||||||
||Employment rate (%)|42.32|56.40|40.00|38.00|46.00|43.00|
||Wages (USD,PPP 2019)|7,481.00||12,167.00|<br>7,545.00|<br>8,829.00|<br>8,229.00|

65
**Figure 10a. Brazil, NIM and I&R (1995-2009)**

<!-- Start of picture text --> Figure 10b. U.S., NIM and I&R (1995-2009)<br><!-- End of picture text --> <!-- Start of picture text --> 18 5<br>16 4.5<br>14 4<br>3.5<br>12<br>3<br>10<br>2.5<br>8<br>2<br>6<br>1.5<br>4<br>1<br>2<br>0.5<br>0 0<br>1995 1997 1999 2001 2003 2005 2007 2009 1995 1997 1999 2001 2003 2005 2007 2009<br>NIM I&R NIM I&R<br>Percentage points<br>Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data Our results in 2009 are robust since for micro-factors we have an R<sup>2</sup> of .76 and macro-factors are significant at the 5 percent level. In 1995, 1997 and 2001 in Brazil we observe a negative spread for macro-factors. These are odd results but can be interpreted as an underestimation of macro-factors and overestimation of micro-factors when banks make lending and borrowing decisions. The decisions made by banks in those years were not in line with the fundamentals of macro and micro-factors. Interestingly in 1998-1999 Brazil faced a crisis and again in 2002, just before the Lula administration came to power. These crises happened one year after we see massive underestimation of macro-factors.

**Figure 11a. Brazil, NIM and macro**

**Figure 11b. U.S.A., macro as a percent of NIM**

<!-- Start of picture text --> 5 120%<br>4 100%<br>3 80%<br>2<br>60%<br>1<br>40%<br>0<br>20%<br>1995 1997 1999 2001 2003 2005 2007 2009<br>-1<br>0%<br>-2 1995 1997 1999 2001 2003 2005 2007 2009<br>-20%<br>-3<br>-40%<br>-4<br>-60%<br>-5<br>Macro Brazil Macro US Macro/NIM Brazil (%) Macro/NIM US (%)<br>Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data Macro signals should be the most important factor in banks’ NIM, in a well-functioning competitive banking market. Banks should be mainly taking into consideration what is happening in the general economy and not simply what their competitors are doing. Additionally governments should refrain 24
Table 5. Parental Consanguinity and Likelihood to be Moderately Stunted, Age 5-13

||Ordin|aryLeast Sq|uares||IV-LIML|Estimator|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Parents are Related|-0.006|||-0.007|0.267|0.225|
||(0.049)|||(0.049)|(0.177)|(0.249)|
|F-coefficient of Inbreeding||0.109<br>(0.387)|||||
|Parents are 1st Cousins|||0.016<br>(0.049)||||
|Parents are 2nd Cousins|||0.038<br>(0.069)||||
|Age|0.028***|0.028***|0.028***|0.027***|0.025***|0.024***|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.007)|
|Male|-0.053|-0.053|-0.053|-0.049|-0.057|-0.051|
||(0.034)|(0.034)|(0.034)|(0.033)|(0.036)|(0.035)|
|Wealth Index|0.000|0.002|0.002|-0.008|0.022|-0.008|
||(0.030)|(0.030)|(0.030)|(0.028)|(0.033)|(0.027)|
|Dummy for Sindh Province|0.064|0.067|0.067||0.114||
||(0.074)|(0.074)|(0.075)||(0.097)||
|Dummy for KPK Province|0.128|0.132|0.133||0.180||
||(0.181)|(0.180)|(0.179)||(0.163)||
|Father's Years of Education|0.004|0.004|0.004|0.008|0.002|0.006|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|Father's Years of Education*Sindh|-0.008|-0.008|-0.008|-0.014*|-0.005|-0.010|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.009)|(0.009)|
|Father's Years of Education*KPK|-0.011|-0.011|-0.011|-0.006|-0.007|-0.001|
||(0.019)|(0.019)|(0.019)|(0.012)|(0.017)|(0.013)|
|District Fixed Effects|No|No|No|Yes|No|Yes|
|**Test statistics**|||||||
|Hansen J statistic|||||0.227|1.35|
|p-value|||||0.634|0.244|
|F-statistic, Instruments|||||8.27|5.30|
|F-probability|||||0.001|0.007|
|CLR Test|||||2.07|0.99|
|p-value|||||0.171|0.351|
|95% Confidence Interval, Based on|||||[0.01,|[0.01,|
|<br>CLR Test|||||0.61]|2.73]|
|Number of observations|1285|1285|1285|1285|1285|1285|

Notes: Village-level cluster robust standard errors are reported in parenthesis. Columns (4) and (6) control for factors related to village location with district fixed effects. The Hansen J statistic tests for over-identification. We report cluster-corrected F statistics to test for weak instruments and then implement the conditional likelihood ratio (CLR) test developed by Moreira (2003) to test for weak instrument bias. The CLR test is a test of the hypothesis that the instruments and the coefficient on the endogenous variable are jointly zero. We next show the 95% confidence interval suggested by the CLR test. As implemented, CLR test is generalized for clustered dependence in error terms using the minimum distance approach by Finlay and Magnusson (2009).

24
<!-- Start of picture text --> N<br>| NJ N<br>« f yo<br>yo fi<br>Legend LegendBEB -30% to -15%<br>HEHEBB 76%77.12%79% 10t0 to 31%72% 70% HEHEBLegend0%5% 10to 5%10% HHHMHD 215%0%5%toto0%5% -5%<br><!-- End of picture text -->