rafmacalaba/datause-encoder-data huggingface dataset:
|**Table A2: S**|**ummary St**|**atistics of the**<br>|**main variabl**<br>|**es**<br>||
|---|---|---|---|---|---|
||**Mean**|**Std.**<br>**deviation**|**Minimum**|**Maximum**|**Observations**|
|Employment Growth|5.049|16.169|-94|98.7|41,203|
|Employment Growth 1|5.295|17.996|-170|251|41,203|
|Overall Corruption (CI avg.)|1.181|1.814|0|27.2|41,203|
|Petty Corruption Depth (CI avg.)|13.766|14.638|0|100|41,117|
|Petty Corruption Incidence (CI avg.)|17.510|16.902|0|100|41,117|
|Financially Constrained (CI avg.)|0.468|0.196|0|1|41,203|
|Initial Employment (logs)|2.447|0.883|0|4.6|41,203|
|Age of firm (logs)|2.578|0.678|0|5.4|41,203|
|Manager Experience (logs)|2.638|0.709|0|4.1|41,203|
|Exports (proportion of sales)|0.052|0.184|0|1|41,203|
|Foreign Ownership (% ownership)|7.799|24.924|0|100|41,203|
|Multi-firm (dummy)|0.184|0.387|0|1|41,203|
|Female Manager (dummy)|0.191|0.393|0|1|41,203|
|Power Outage (logs)|1.361|1.727|0|6.6|41,203|
|Informal Competition (dummy)|0.559|0.496|0|1|41,203|
|Firm Bought Assets (dummy)|0.393|0.488|0|1|41,203|
|Time Tax (%)|9.525|15.626|0|100|41,203|
|Courts: Obstacle Severity|0.947|1.224|0|4|41,203|
|Crime Losses (% of sales)|0.879|4.001|0|100|41,203|
|Taxes: Obstacle Severity|1.776|1.308|0|4|41,203|
|Labor Laws: Obstacle Severity|0.975|1.121|0|4|41,203|

Sample used is the baseline sample used for Overall Corruption (Table 1). CI avg. denotes country-industry cell average excluding the own firm as described in section 2.

52
Policy Research Working Paper 5994

# **Abstract**

Using data from the 2010 Life in Transition Survey, this paper examines the levels of citizens’ satisfaction with public service delivery in Europe and Central Asia and identifies some factors that may help explain variation in utilization and levels of satisfaction with service delivery. It finds satisfaction with public service delivery in Europe and Central Asia to be relatively high, and, despite the adverse economic and social impact of the recent global economic crisis, to have risen since 2006 in most countries in the region. However, the level of satisfaction with public service delivery in Eastern European and Central Asian countries in 2010 remains lower than in Western European comparator countries. Although the Life in Transition Survey does not provide specific objective measures of service delivery quality and efficiency, the data provide three important clues that may help explain why satisfaction is lower in transition countries than in western comparators: (i) relatively higher utilization of public services in Eastern European and Central Asian countries, (ii) relatively higher reported prevalence of unofficial payments, and (iii) relatively underdeveloped mechanisms for grievance redress.

This paper is a product of the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at szaidi5@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
Policy Research Working Paper 10768 A Data-Driven Approach for Early Detection of Food Insecurity in Yemen’s Humanitarian Crisis _Steve Penson Mathijs Lomme Zacharey Carmichael Alemu Manni Sudeep Shrestha Bo Pieter Johannes Andrée_ Poverty and Equity Global Practice Development Data Group Agriculture and Food Global Practice May 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
the U.S. be high? The litigation costs of merely initiating a case as a complainant are not large, and that is the only indicator necessary for our analysis of whether a U.S. trade remedy is challenged at the WTO. Nevertheless, we do proxy for the foreign country’s capacity to incur significant legal costs by using measures of its real GDP per capita, with data derived from World Bank (2004). We feel that this is a reasonable measure and is likely better than one designed to capture the stock of lawyers in the foreign country, given that legal expertise is an internationally traded service. Nevertheless, we also proxy for a country’s legal capacity by using data on the number of delegates the WTO member had sent to the Secretariat in Geneva. The larger are each of these variables, the greater the country’s capacity to absorb legal costs and the more likely it will initiate a dispute, ceteris paribus.

# **3.4 Access to Alternative Retaliatory Instruments**

Our last explanatory variable included in this stage of the estimation is designed to capture the concern that a country may choose _not_ to use the formal dispute settlement process at the WTO to challenge a U.S.-imposed trade remedy because it has access to an alternative (and perhaps preferable) retaliatory instrument, i.e., because it is able to take matters into its own hands and hit the protected U.S. industry with a trade remedy of its own. Therefore, we include another measure which is the share of the U.S. _industry’s_ value of domestic production that is exported to the remedy-affected country, where the industry is the 6-digit NAICS industry that is receiving the U.S. trade remedy protection.<sup>22</sup> The higher is the share of the value of U.S. industry production exported to the foreign country, the _less_ likely is the foreign country to initiate a formal WTO dispute against the U.S. because it can discipline the U.S. industry directly through its own appeal to a trade remedy investigation. The industry level production data is the value of shipments data taken from the _NBER Manufacturing Industry Productivity Database_ for the 1991-1996 period, while for 1997-2002, the data was taken from individual, industry level reports in the U.S. Census Bureaus’ publication, _2002 Economic Census, Manufacturing Industry Series._<sup>23</sup>

> 22 The U.S. 6-digit NAICS export data is also taken from Feenstra et. al (2002). Blonigen and Bown (2003) have found evidence that the capacity for foreign antidumping retaliation threats affects an earlier stage of the antidumping process, i.e., the question of which foreign countries are to be named in an antidumping investigation. Their results suggest that, ceteris paribus, a U.S. industry is less likely to name a foreign country on whom its market it is particularly reliant for its own exports.

> 23 We have also interacted this variable with an indicator for whether the WTO member has an antidumping law, but this makes little difference as virtually all WTO members in the sample of countries hit with a U.S. remedy had an antidumping law in place.

13
# **Governance Drivers of Rural Water Sustainability: Collaboration in Frontline Service Delivery**

_Dikshya Thapa Muhammad Noor Farid Christophe Prevost_ The World Bank JEL Classification: C25, D71, D73, H41, O22, Q25 Key Words: State Embeddedness, Frontline Leadership, Collective Action, Local Governance, Rural Water, Local Infrastructure, Sustainability, Deviance Analysis, Mixed Methods
gap widened, from 7 percentage points in 2014 to 9 percentage points in 2017. In the euro area, the gap stayed at about 20 percentage points between 2011 and 2017.

People save for different purposes. Nearly half of adults in high‐income economies report saving for old age. In 2017, 16 percent of adults in developing economies and 38 percent of adults in the euro area reported doing so. In developing ECA, only 15 percent of adults reported saving for old age.

In 2014 only 4 percent of adults in the developing economies of ECA reported saving to start, operate, or expand a farm or business. The share rose to 8 percent in 2017, still lower than the global average of 14 percent.

# _Informal Borrowing_

In 2017 about half of the world’s adults reported borrowing money the previous year. A larger share (64 percent) did so in high‐income economies, where most borrowers rely on formal credit from financial institutions or credit cards. In developing economies, most borrowers rely on family and friends (figure 11).

## **Figure 11. People in high‐income countries rely on formal sources for borrowing, whereas people in developing countries tend to rely on family or friends**

<!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0%<br>High‐income economies Developing economies<br>Borrowed formally Borrowed semi‐formally<br>Borrowed from family or friends Borrowed from other sources only<br>in 2016<br>Percent of adults who reported borrowing<br><!-- End of picture text -->

### _Source_ : Global Findex database.

_Note_ : People may borrow from multiple sources, but the categories here are constructed to be mutually exclusive. “Borrowed formally” includes all adults who borrowed any money from a financial institution or by using a credit card. “Borrowed semi‐formally” includes all adults who borrowed any money semi‐formally (from a savings club) but not formally. “Borrowed from family or friends” excludes adults who borrowed formally or semi‐formally.

The borrowing pattern in ECA looks different from that in the rest of the world. In 2017, 55 percent of adults in the euro area reported having borrowed money the previous year. Forty‐six percent reported that they borrowed money from a financial institution or by using a credit card; only 12 percent reported borrowing from friends or family. In contrast, in the developing economies of ECA, 44 percent of adults reported borrowing the previous year, with 24 percent reporting borrowing from friends or 22
Table 3: **Robustness checks**

**Panel A. Check for omitted variable bias**

||Full|Exporters|Non-Exporters|Full|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Exporter||||1_._502<sup>_∗∗∗_</sup>|
|||||(0_._547)|
|WfH x digital infra.|0_._852<sup>_∗∗∗_</sup>|1_._335<sup>_∗∗_</sup>|0_._802<sup>_∗∗∗_</sup>|0_._906<sup>_∗∗∗_</sup>|
||(0_._262)|(0_._619)|(0_._300)|(0_._295)|
|WfH x digital infra. x exporter||||_−_0_._146|
|||||(0_._603)|
|Log (Av. sales by country-sector)|0_._614<sup>_∗∗∗_</sup>|0_._433<sup>_∗∗_</sup>|0_._633<sup>_∗∗∗_</sup>|0_._604<sup>_∗∗∗_</sup>|
||(0_._097)|(0_._221)|(0_._108)|(0_._097)|
|Country F.E.|Yes|Yes|Yes|Yes|
|Sector F.E.|Yes|Yes|Yes|Yes|
|R-squared|0_._272|0_._312|0_._249|0_._272|
|Observations|56,548|12_,_710|43_,_838|56_,_548|

The F-stat testing the equality of coefficients in columns 2 and 3 is 0.600, with p value 0.439. **Panel B. Check for human capital as confounding factor**

||Full|Exporters|Non-Exporters|Full|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Exporter||||1_._769<sup>_∗∗∗_</sup>|
|||||(0_._575)|
|WfH x digital infra.|0_._857<sup>_∗∗_</sup>|0_._717|0_._848<sup>_∗∗_</sup>|0_._818<sup>_∗∗_</sup>|
||(0_._363)|(0_._940)|(0_._383)|(0_._377)|
|WfH x digital infra. x exporter||||0_._215|
|||||(0_._663)|
|Country schooling years x Sh. remote workers|0_._046|0_._447<sup>_∗∗_</sup>|_−_0_._003|0_._050|
||(0_._095)|(0_._228)|(0_._103)|(0_._096)|
|Country F.E.|Yes|Yes|Yes|Yes|
|Sector F.E.|Yes|Yes|Yes|Yes|
|R-squared|0_._267|0_._314|0_._243|0_._267|
|Observations|61,995|12_,_490|49_,_505|61_,_995|

The F-stat testing the equality of coefficients in columns 2 and 3 is 0.016, with p value 0.898.

**Panel C. Digitalization as proxy for frontier industry maturity**

||Full|Exporters|Non-Exporters|Full|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Exporter||||1_._804<sup>_∗∗∗_</sup>|
|||||(0_._551)|
|WfH x digital infra.|_−_0_._367|2_._334<sup>_∗∗_</sup>|_−_1_._004<sup>_∗∗_</sup>|_−_0_._394|
||(0_._431)|(1_._086)|(0_._469)|(0_._431)|
|WfH x digital infra. x exporter||||_−_0_._119|
|||||(0_._613)|
|Log(GDP per capita) x Sh. remote workers|1_._319<sup>_∗∗∗_</sup>|_−_0_._654|1_._958<sup>_∗∗∗_</sup>|1_._389<sup>_∗∗∗_</sup>|
||(0_._376)|(0_._785)|(0_._440)|(0_._383)|
|Country F.E.|Yes|Yes|Yes|Yes|
|Sector F.E.|Yes|Yes|Yes|Yes|
|R-squared|0_._262|0_._308|0_._241|0_._262|
|Observations|68,007|13_,_145|54_,_862|68_,_007|

The F-stat testing the equality of coefficients in columns 2 and 3 is 0.769, with p value 0.005. Estimations also control for (log) firm’s size, quarter F.E, and Google’s Mobility index. Estimations are weighted using country weights. Standard errors in parentheses. Significance levels ***, **, and *, correspond to p _<_ 0.01, p _<_ 0.05, p _<_ 0.1, respectively. The amenability of remote work and internet use/penetration do enter the regression individually because the former are absorbed in sector fixed-effects and the latter in country fixed-effects. 12
of fiscal health are estimated and analyzed in Section 6. Section 7 gives the major conclusions and summarizes the data caveats and limitations of the study.

# **2. Urban Agglomerations: Some Characteristics**

The selection of the five UAs, though is partly subject to availability of data on local governments, the objective basis in terms of coverage and diversity of the entire nation is also not missing. First of all, efforts have been taken to make the sample representative of the entire nation as best as possible. Secondly, all the UAs chosen for the study have recorded, over the last decade, a faster pace of urbanization reflected in greater decline in primary sector activities, greater orientation towards manufacturing and services sector and higher literacy rates of population than the national average for Urban India.<sup>2</sup> Table 1 gives a broad overview of the five urban agglomerations chosen for the study in terms of location and number of constituent ULBs.

**Table 1 Urban Agglomerations: An Introduction**

|**Urban**<br>**Agglomeration**|**State**|**District**|**Number of**<br>**ULBs**|
|---|---|---|---|
|Hyderabad|Andhra Pradesh (Southern<br>India)|Hyderabad, Rangareddy|11<sup>3</sup><br>|
|Chennai|Tamil Nadu (Southern tail of<br>ndia)|Chennai, Kancheepuram, Thiruvallur|36<sup>4</sup>|
|Kolkata|West Bengal (Eastern India)|Kolkata, 24 Paraganas (North), 24|41|
|||Paraganas (South), Hugli, Haora, Nadia.||
|Delhi|Delhi (Northern India)|Delhi|3|
|Pune|Maharashtra (West to Central<br>India)|Pune|5|

**Source: Census of India, 2001**

> 2 The comparisons are in terms of the national urban averages subject to availability of data, See NIPFP 2007(a)(b)(c)(d), NIPFP (2008)(a)(b) for details on individual cities; the average literacy rate for urban India is 65% according to Census of India, 2001.

3 In late 2007, all the ULBs in Hyderabad have been merged to one Greater Hyderabad Municipal Corporation (GHMC). Our surveys were conducted before that, so we have considered the 11 ULB budgets separately.

> 4 Our analysis is based on 8 ULBs in Chennai

8
|ET ROAD SEC. DEV. PROG|Michael G. Nelson|
|---|---|
|ET ROAD SEC. DEV. PROG|Michael G. Nelson|
|ET ROAD SEC. DEV. PROG|Michael G. Nelson|
|ET ROAD SEC. DEV. PROG|Michael G. Nelson|
|ET ROAD SEC. DEV. PROG|Michael G. Nelson|
|ET ROAD SEC. DEV. PROG|Michael G. Nelson|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Health Sector Development Program|Gebreselassie S. Okubagzhi|
|Calub Gas Development Project|Alfred B. Gulstone|
|Calub Gas Development Project|Alfred B. Gulstone|
|Calub Gas Development Project|Alfred B. Gulstone|
|Calub Gas Development Project|Alfred B. Gulstone|
|Calub Gas Development Project|Alfred B. Gulstone|
|Calub Gas Development Project|Alfred B. Gulstone|
|Calub Gas Development Project|Alfred B. Gulstone|
|Calub Gas Development Project|Alfred B. Gulstone|
|EmergencyRecoveryand Reconstruction Project(ERRP)|John D. Riverson|
|EmergencyRecoveryand Reconstruction Project(ERRP)|John D. Riverson|
|EmergencyRecoveryand Reconstruction Project(ERRP)|John D. Riverson|
|EmergencyRecoveryand Reconstruction Project(ERRP)|John D. Riverson|
|EmergencyRecoveryand Reconstruction Project(ERRP)|John D. Riverson|
|EmergencyRecoveryand Reconstruction Project(ERRP)|John D. Riverson|
|EmergencyRecoveryand Reconstruction Project(ERRP)|John D. Riverson|
|Water SupplyDevelopment and Rehabilitation Project|Robert J. Roche|
|Water SupplyDevelopment and Rehabilitation Project|Robert J. Roche|
|Water SupplyDevelopment and Rehabilitation Project|Robert J. Roche|
|Water SupplyDevelopment and Rehabilitation Project|Robert J. Roche|
|Water SupplyDevelopment and Rehabilitation Project|Robert J. Roche|
|Water SupplyDevelopment and Rehabilitation Project|Robert J. Roche|
|Water SupplyDevelopment and Rehabilitation Project|Robert J. Roche|
|Water SupplyDevelopment and Rehabilitation Project|Robert J. Roche|
|Water SupplyDevelopment and Rehabilitation Project|Robert J. Roche|
|Recoveryand Rehabilitation Project for Eritrea|Akbar Noman|
|Recoveryand Rehabilitation Project for Eritrea|Akbar Noman|
|Recoveryand Rehabilitation Project for Eritrea|Akbar Noman|
|Recoveryand Rehabilitation Project for Eritrea|Akbar Noman|
|Recoveryand Rehabilitation Project for Eritrea|Akbar Noman|
|Ethiopia Social Rehabilitation and Development Fund|Endashaw Tadesse Gossa|
|Ethiopia Social Rehabilitation and Development Fund|Endashaw Tadesse Gossa|
<mark>WPS3566</mark>

**Trade Preferences and Differential Treatment of Developing Countries: A Selective Survey**<sup>1</sup>

# Bernard Hoekman

(World Bank, Groupe d’Economie Mondiale, Sciences Po, and CEPR) Çaglar Özden (World Bank)

**Summary:** Non-reciprocal trade preferences and provisions in the GATT/WTO that allow developing countries greater leeway to retain or use protectionist policies are two of the central planks of so-called special and differential treatment (SDT) for developing countries in the multilateral trading system. This paper surveys the literature on the rationales, institutional features, and economic effectiveness of SDT. A large literature has emerged on SDT in the last 50 years, by both proponents and opponents. We summarize a number of key contributions on the subject, with a special emphasis on the evaluation of  the impact of SDT, especially preferential market access. The issue of SDT has become very topical again, following a period during  which it was viewed as an outdated concept for the multilateral trading system. We therefore devote attention as well to a number of recent contributions  that discuss (i) whether there is a continued need for SDT, and (ii) how this might be designed from both a development (recipient) objective and from the perspective of the trading system more generally. A major theme of the survey is that most of the issues that are debated today were already being discussed in the 1960s. We conclude that those who questioned the value of unilateral preferences have proven to be prescient.

Keywords: Trade preferences; special and differential treatment JEL: F13, F15 World Bank Policy Research Working Paper 3566, April 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 This paper was written as the Introduction to a volume of readings on trade preferences and special and differential treatment in the GATT/WTO prepared for Edward Elgar. We are grateful to Kym Anderson, Patrick Low, Sheila Page and Chris Stevens for helpful suggestions.
# **Table 16a: Specification with Log of annual sales as dependent variable – Full sample**

|**Full sample**|**(1)-B**|**asic**|**(2) = (1) +**<br>**finan**|**access to**<br>**ce**|**(3) = (2) +**<br>**firms' int**<br>**trade eng**|**degree of**<br>**ernational**<br>**agement***|**(4) = (**<br>**interact**|**3) plus**<br>**ion terms**|
|---|---|---|---|---|---|---|---|---|
|Variables|Firm|Industry|Firm|Industry|Firm|Industry|Firm|Industry|
|Log of past sales|0.675***<br>(0.02)|0.485***<br>(0.04)|0.719***<br>(0.02)|0.490***<br>(0.05)|0.739***<br>(0.04)|0.483***<br>(0.04)|0.740***<br>(0.04)|0.482***<br><br>(0.04)|
|Rank on the ease of Getting<br>Electricity|-0.00191***|0.00604**|-0.00184**|0.00762*|-0.00143**|0.00|0.00631***|0.0102*|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|
|Number of power outages per<br>month|-0.0001|0.0038|-0.0001|0.0059|0.0003|0.0036|0.000533*|0.0034|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|
|Log of Labor Input|0.359***|0.601***|0.314***|0.588***|0.257***|0.508***|0.253***|0.509***|
|Capital Input|(0.03)<br>-0***|(0.05)<br>-0***|(0.03)<br>-0***|(0.06)<br>-0***|(0.04)<br>-0***|(0.07)<br>-0***|(0.04)<br>-0***|(0.06)<br>-0***|
||0.00|<br>0.00|0.00|0.00|<br>0.00|<br>0.00|<br>0.00|<br>0.00|
|Access to finance Dummy|-|-|0.0535**|0.10|0.0685**|0.24|0.0647*|0.25|
||-|-|(0.02)|(0.13)|(0.03)|(0.17)|(0.03)|(0.19)|
|Direct Export 10% Dummy|-<br>-|-<br>-|-<br>-|-<br>-|0.0552<br>(0.05)|0.0148<br>(0.17)|0.0477<br>(0.05)|-0.0013<br>(0.19)|
|Direct Import Dummy|-|-|-|-|0.151***|0.271**|0.155***|0.265*|
||-|-|-|-|(0.03)|(0.14)|(0.03)|(0.14)|
|Two-way Trader Dummy|-|-|-|-|(0.02)|0.10|<br>(0.02)|0.09|
||-<br>|-<br>|-<br>|-<br>|(0.05)<br>|(0.17)<br>|(0.05)<br>|(0.18)<br>|
|Log of real GDP per capita<br>Income Group Dummies|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|
|Country Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Interaction Terms:<br>Industry*Getting Electricity|No|No|No|No|No|No|Yes|Yes|
|Constant|3.784***|5.870***|3.222***|5.791***|3.202***|6.612***|2.159***|5.984***|
||(0.34)|(0.54)|(0.34)|(0.69)|(0.49)|(0.78)|(0.49)|(0.85)|
|Observations|8,767|1,574|7,355|1,112|3,829|816|3,829|816|
|R-squared<br>Robust standard errors in paren<br>* measured by whether the firm|0.89<br>theses *** p<<br>exports directly|0.88<br>0.01, ** p<0.05<br>at least 10% of|0.91<br>, * p<0.1<br>its sales, impor|0.88<br>ts directly at|0.93<br>least 10% of i|0.91<br>ts inputs or do|0.93<br>es both of th|0.91<br>ese.|

47