rafmacalaba/datause-encoder-data huggingface dataset:
However, the characteristics of the balanced and the full sample (i.e., any firm that responded to a BEEPS survey in 2002, 2005, or 2008-9) are similar. One difference is that firms in the full sample come from countries with larger populations and that grew faster, while those in the balanced panel were more likely to be privatized and to be foreign owned (see Table 1). The full sample tips more heavily toward manufacturing firms, while the balanced panel tips more heavily toward trade. Finally, the balanced panel has a higher share of firms from countries with a pegged rather than a floating exchange rate regime. In practice, however, (unreported) regression results for the full sample were generally similar to those for the balanced panel.

# **B. Financial Crisis Surveys**

The Financial Crisis Survey was implemented by the World Bank in June and July 2009 in six countries: Bulgaria, Hungary, Latvia, Lithuania, Romania and Turkey. Firms that were part of the 2008-9 BEEPS were re-interviewed to see whether they were still operating and, if so, how they were coping with the crisis. Of the 2,501 firms in manufacturing, services, and retail trade that had been interviewed during the third round of the BEEPS survey between April 2008 and March 2009, 1686 were re-interviewed during the FCS.<sup>18</sup> The 2,501 firms in the third round of the BEEPS survey can be divided into four groups: (i) those that were still operating and were interviewed in the FCS; (ii) those that were still operating but were not interviewed during the FCS because enough firms had been interviewed, an interview could not be scheduled, or the firm manager refused to participate; (iii) firms that could not be located; and (iv) firms that had closed (see Table 2). We follow Ramalho et al. (2010) and classify firms as no longer operating if they fell into categories (iii) or (iv), or if they were in category (i) but reported that they had filed for insolvency or bankruptcy.

Although effort was made to collect reliable contact information to allow for the collection of panel data in BEEPS, it is possible that some firms could not be located because

> 18 See World Bank (2009) for a description of the sample.  In Turkey, only the manufacturing subsample was targeted for follow-up in the financial crisis survey (Ramalho et al., 2010).  Non-targeted firms (i.e., firms for which no information was collected on their operating status at the time of the financial crisis survey) are excluded from the sample by necessity.

13
# **Table A9:  European Bank for Reconstruction and Development Transition Report (EBR)**

<!-- Start of picture text --> Data Provider European Bank for Reconstruction and Development<br>Description Multilateral development bank based in London, United Kingdom<br>Website www.ebrd.org<br>Data Source Transition Report<br>Type Expert Assessment<br>Respondents EBRD staff<br>Frequency Annual since 1996<br>Coverage Transition economies<br>Public Access Yes<br>Description The Transition Report includes scores on a 5-point scale for eight Transition Indicators<br>measuring progress towards market economy status. Scores are based on a checklist of<br>underlying criteria and reflect the views of EBRD staff.<br>2007 2006 2005 2004 2003 2002 2000 1998 1996<br>Voice and Accountability<br>NA .. .. .. .. .. .. .. .. ..<br>Political Stability and Absence of Violence<br>NA .. .. .. .. .. .. .. .. ..<br>Government Effectiveness<br>NA .. .. .. .. .. .. .. .. ..<br>Regulatory Quality<br>Price liberalisation X X X X X X X X X<br>Trade & foreign exchange system X X X X X X X X X<br>Competition policy X X X X X X X X X<br>Rule of Law<br>NA .. .. .. .. .. .. .. .. ..<br>Control of Corruption<br>NA .. .. .. .. .. .. .. .. ..<br>Country coverage 29 29 27 27 27 27 26 26 27<br>Year of Publication 2007 2006 2005 2004 2003 2002 2000 1998 1996<br><!-- End of picture text --> 47
student until entry into the student’s current school, **_s_**<sup>_P_</sup> _i,j_<sup>, a vector of inputs received over</sup> the student’s time at the present school, **_s_**<sup>**_C_**</sup> **_i,j_**<sup>, and the student’s endowed mental capacity,</sup> _ui,j_ .

Further, it is assumed that a student’s exposure to the current school’s characteristics, **_s_**<sup>**_C_**</sup> **_i,j_**<sup>**,**is a function of the vector of school characteristics,</sup><sup>**_s_**</sup><sup>_E_</sup> _,j_<sup>, grade level,</sup><sup>_g_</sup> _i,j_<sup>, and the</sup> number of years the student has attended the school, _ai,j_ .

We allow the possibility that natural ability, _ui,j_ , may be influenced by the student’s gender; natural ability is a function of gender, denoted by the binary variable for female, _fi,j_ , and an unobserved component, _e_<sup>_u_</sup> _i,j_<sup>.</sup> In the PISA dataset, much of household and school components are unobserved. For example, nothing is known about a student’s previous schooling or the number of years the student has attended his or her current school, or his or her early childhood experiences. As well, only cursory information about the household and current school are available. However, suppose we could observe all the necessary household and 14
**Table 11 Investment Climate Variables Used in the Analysis**

# Investment Climate Variables

|**Finance**||
|---|---|
|External Fin|% of investments financed externally|
|Collateral|Value of collateral required as a % of loan value or value of line of credit|
|Sales on Credit|% of sales of goods or services that are sold on credit|
|Overdraft (y/n)|% of firms with an overdraft facility|
|Purch on Credit (y/n)|% of firms purchasing material or service on credit|
|Purch on Credit|% of purchases of materials that are bought on credit|
|Line of credit (y/n)<br>**Infrastructure**|% of firms with a line of credit or loan from a financial institution|
|Dur PowOut|Duration of power outages (hrs)|
|Loss PowOut|Loss from power outages as a % of annual sales|
|Power out (y/n)|% of firms that had a power outage|
|# PowOut|# of power outages|
|**Regulations**||
|Tax Inspect|# of meetings or inspections with tax officials|
|Time Tax|% of time spent with government regulation|
|Days to Export|# of days to clear exports customs|
|Days to Import|# of days to clear imports customs|
|Inspections|Total number of inspections|
|Days for Inspect|# of days spent by staff members dealing with procedures related to inspections|
|Operating License|# days waited to get an operating license|
|Certificate|# of days waited to get compulsory certificate|
|Permits|# of days waited to get permit|
|Labor Reg|% of firms that find labor regulations major or very severe obstacle for to their<br>current operations|
|**Training and Others**||
|Training (y/n)|% of firms having formal training|
|NonProd Train|% of nonproduction workers trained|
|Prod Train|% of production workers trained|
|Infor Pay|% of sales paid as informal payments|
|CompInformal(y/n)|% of firms competingagainst unregistered or informal firms|

19
# Figure 2. Potential growth in EMDE regions, 2000-10 and 2011-20

<!-- Start of picture text --> A. EAP B. ECA<br>Percent 2011-21 2000-10 Percent 2011-21 2000-10<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>PFA MVF Exp. UCM PFA MVF Exp. UCM<br>C. LAC D. MNA<br>Percent 2011-21 2000-10 Percent 2011-21 2000-10<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>PFA MVF Exp. UCM PFA MVF Exp. UCM<br>E. SAR F. SSA<br>Percent 2011-21 2000-10 Percent 2011-21 2000-10<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>PFA Exp. PFA MVF Exp. UCM<br><!-- End of picture text --> Sources: Haver Analytics; Penn World Tables; UN Population Prospects; World Bank.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. Period average of annual GDP-weighted averages. Samples differ across measures, depending on data availability. PFA = production function approach. MVF = multivariate filter-based. UCM = univariate filter-based (specifically, the HodrickPrescott filter). “Exp.” = estimates based on five-year-ahead World Economic Outlook forecasts. For SAR, insufficient data available for filter-based estimates until 2010. The sample includes 28 economies; 3 countries in EAP (China, Philippines, and Thailand), 5 countries in ECA (Bulgaria, Hungary, Kazakhstan, Poland, and Romania), 10 countries in LAC (Bolivia, Brazil, Chile, Colombia, Costa Rica, Honduras, Mexico, Paraguay, Peru, and Uruguay), 3 countries in MNA (Jordan, Morocco, and Tunisia), 4 countries in SAR (Bangladesh, India, Pakistan, and Sri Lanka), and 3 countries in SSA (Cameroon, Namibia, and South Africa). Note that quantitative estimates may differ from those presented in Figure 3 because of sample differences. Figure 2 ensures sample consistency across measures; Figure 3 ensures sample consistency across time.
robust using alternative values of _θ_ . Table 3 summarizes the parameter values.

Table 3. Benchmark calibration

|Parameter|Value|Description|Source|
|---|---|---|---|
|_τ _<sup>_d_</sup>|[0; 1]|Distortionary infrastructures|WBES|
|_A_|[0; 1]|Public policies Efectiveness|WGI|
|_p_|[0; 1]|Detection probability|WGI|
|_β_|0.96|Real rate of return|Literature|
|_η_|0.283|Capital income share|Literature|
|_γ_|0.567|Labor income share|Literature|
|_δ_|0.08|Investment to output ratio|Literature|
|_λ_|0.1|Annual exit rate|Literature|
|_θ_|3|Evasion cost parameter|Assumption|
|_ce_|1|Entry costs|Literature|
|_cf_|0|Fixed costs|Literature|
|_z_|[1; 3.98]|Distribution of productivity|Relative labor demand|

Notes. See Bah and Fang (2015), Restuccia and Rogerson (2008), Atkeson and Kehoe (2005) and Pavcnik (2002) for parameter values from the literature.

22
2 We chose labor productivity as a key variable, and try to differentiate the mechanical effects of increasing demand on productivity from those of industrial restructuring.

A second crucial issue for the success of transition that we address in this paper is the performance of state enterprises. The key questions concern the ability of the public sector to adjust and the degree of restructuring that has already taken place, if any. Little comparative analysis of the adjustment and performance of public and private enterprises can be found in the literature, mainly because of data availability problem. The few existing studies, to our knowledge, are limited to case-studies. Pinto et al. (1993) analyzed a sample of about 125 public enterprises; they suggest that there may be more cases of viable public firms than expected, and that management rather than ownership may be the crucial factor. Estrin et al. (1994) have considered 15 case-studies of stateowned enterprises, and found that only half of the firms examined would be viable. In this regard, the comprehensiveness, of our data set allows us to fill a gap in the literature.

Finally, we address the issue of size. We investigate whether and how firms' size affects their economic performance, in both the private and the public sector.

The content of this paper is as follows. Section II provides broad evidence on the process of resource reallocation and structural adjustment of the Polish economy during the first five years of the reforms (1990-1994). The third section analyzes the supply response and productivity performance of state-owned and private enterprises, with the use of a large disaggregate panel data set, which covers 88 three-digit manufacturing industries, in the period 1992-93. In the spirit of Stockman (1988), we decompose productivity growth in public and private enterprises of each three-digit industry into components accounted for by aggregate, macroeconomic (mainly demand) factors and those accounted for by structural, industry-specific factors. The fourth section adds evidence based on a panel data set for 1990-93 that allows to control for both ownership and size of industry. Conclusions follow.
impacts on the environment.<sup>8</sup> For example, fishing nets have to be of a specific mesh size (to prevent catch of juvenile species) and their use is often restricted in sensitive habitats—such as beaches, coral reefs, and spawning grounds.

In assessing the potential negative impact of different fishing gears on the environment, we rely on information collected during our focus group survey about how these different gears are actually used, as well as other studies that have taken place in Tanzania. Evidence suggests that there is a lack of compliance with some fishing gear regulations, and some illegal methods and gear continue to be used due to lack of enforcement and public awareness (Jiddawi and Ohman, 2002). Seine nets of small mesh size are commonly used and dragged on beaches and coral reefs, and sometimes even mosquito nets are used (MANREC, 2005; Berachi, 2003). Therefore, we classify gillnets, beach seine nets, and drive nets, as destructive fishing gears despite their legal status, because the way they are used are likely to cause significant environmental damage. This is in addition to illegal fishing gear, such as dynamite and poison, which are also considered destructive fishing gear.<sup>9</sup> Environmentally non-destructive fishing gear, on the other hand, are the types of fishing gear that are selective in catching the targeted species and do not result in large amounts of bycatch or result in negative impacts on marine habitats. We classify scoop nets, cast nets, ring nets, traps, fences, and hook and line fishing, as environmentally non-destructive fishing gear. As with the classification of environmentally destructive fishing gear, determining which types of gear are less destructive and belong in the non-destructive fishing gear category involves some judgment about how these gears are actually used in practice. Most of these gears we classify as non-destructive have traditionally been used by local fishermen. While they may generate some negative environmental impacts if used carelessly or by too many fishermen, the gears by themselves do not generate substantial habitat destruction.

> 8 The FAO’s Fisheries Global Information System contains detailed fishing gear type fact sheets, which includes a description of the fishing gear, how it is used, and an assessment of the gear’s impact on the catch of juveniles and other species and the marine environment.

> 9 We should note that dynamite and poison fishing are likely underreported in our survey. We believe this is case for two reasons. First, households may be less likely to report they use these methods simply because these are known to illegal activities. Second, dynamite use is very seasonal. Marine park police report that use of dynamite fishing increases at specific times of the year—such as when school fees are due or during particular holidays, when households may be in need of extra cash. The timing of our survey and recall period of one month would therefore miss this type of sporadic use.

22
Then there are companies spun out from “township and village” cooperative enterprises. In 2003, there were 22478 TVEs in China according to the _China Statistics Yearbook_ . Some were large and successful, and 48 were even publicly listed (as of 2001). Listed TVEs – like Wangxian Qianchao Co. Ltd, and Shandong Nanshan Industrial Co. Ltd – are probably in situations similar to those of central government controlled firms.

Small and medium sized Chinese companies, however, can be subjected to discretionary partial and complete expropriation, especially in regions where government is of low quality – for example where Party cadres are poorly educated, corrupt, or where the bureaucracy is highly rigid. Such governments can levy surcharge fees arbitrarily and unexpectedly. They can seize firms’ goods and services by purchasing on credit but never paying.

Ultimately, China may well provide a better institutional environment for FDI operations than for domestic operations – large or small, state controlled or privately run. If China, more than other countries, favors FDI in this way, it might well receive an elevated FDI inflow. But this is nothing to brag about, nor is it indicative of a useful “Chinese model”. If this line of argument is correct, the elevated FDI flowing into China reflects foreign firms seizing investment opportunities that would have gone to domestic firms if China had better institutions across the board.

# **_Options on Institutions_**

Another possibility is that foreign corporate investors might see China as likely to undertake further institutional development that will enshrine effective constraints on executive power. From this perspective, China’s FDI inflow might be slightly elevated relative to that predicted given its current checks on executive power, but not given its likely future checks. In a sense, foreigners are investing in China in anticipation of improved institutions. Of course, this is a bet, for Chinese institutions might not improve. But such bets can be sound investments; for their upside potential can greatly outweigh their downside risks. Indeed many corporate investments 25
## **3.3 Descriptive Statistics**

Table 1 presents descriptive statistics for key outcome variables before the 2018 expansion of the Entity List. On average, sanctioned firms (firms on the Entity List from 1997 to 2021) filed notably more patents between 2006 and 2017, both domestically and internationally, compared to other Chinese firms in our sample. We also observe higher filing rates in triadic patents and high-technology patents for sanctioned firms. This indicates that sanctioned firms have a stronger focus on global innovation and have a broader global market presence. This trend also suggests larger R&D capabilities in sanctioned firms compared to their unsanctioned counterparts. These pre-existing differences between sanctioned and non-sanctioned firms motivate our matching approach, discussed in the next section.

We evaluate patent quality based on filings in the USPTO, EPO, and WIPO only, due to their more comprehensive patent citation records. Each firm’s average patent quality per year was calculated as the ratio of citation-adjusted to total unadjusted patent counts. Sanctioned firms exhibit marginally lower patent quality than other Chinese firms, but the difference is not statistically significant. The prior average patent applications are computed as the annual patent filings for each firm before 2002. It reflects a firm’s pre-sample innovation capacity. Sanctioned firms have a slightly higher pre-sample patent application rate, yet their average patent age is shorter than that of unsanctioned firms.

# **4 Results**

In this section, we present our empirical approach and results. We first present our benchmark results DID estimates of U.S. entity list sanctions on the log patents of Chinese firms. The second subsection presents robustness to alternatives to the log patents specification.

10
The Challenging Arithmetic of Poverty in Bangladesh* by Martin Ravallion

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Has Poverty Increased or Decreased in Bangladesh<br>During the 19809?|2|
||Some Recent Evidence|2|
||An Alternative Assessment|5|
|III.|Growth, Inequality, and Poverty|11|
||Methodological Issues<br>Prospects for Poverty Alleviation through Growth|11<br>13|
|IV.|Conclusions|18|
|Appen|dix|21|
|Footn|otes|24|
|Refer|ences|26|
|Table|s|28|
|Figur|es|31|

Backcroind paper for the _World Development Report 1990._ The paper draws on results of Research Project 675-04, "Policy Analysis and Poverty: Applicable Methods and Case Studies", funded through the World Bank's Research Committee, and based in the Agricultural Policies DIvision. The author is grateful to Gaurav Datt for assistance with the calculaticns reported here, and to Gaurav, Raisuddin Ahmed, James Sackey, Lyn Squire, Dominique van de Walle, and participants at the Bangladesh Development Forum 1989 for helpful comments on an earlier draft. Address for correspondence: Martin Razallion, Room N8-045, World Bank, 1818 H Street NW, Washington DC 20433, USA.
**Figure 1: Entrepreneurship around the world**

<!-- Start of picture text --> 6% Business Density Entry Rate Density 5.48%<br>5%<br>4%<br>3% 2.42%<br>2% 1.54% 1.49%<br>1.24%<br>0.74%<br>0.58%<br>1% 0.05% 0.06% 0.24% 0.18% 0.13%<br>0%<br>Africa & Asia Easter EuropeLatin America Developing Industrialized<br>Middle East &<br> Central Asia &<br> Caribbean<br><!-- End of picture text --> Source: 2008 World Bank Group Entrepreneurship Survey.

In addition to these indicators, the 2008 _WBGES_ provides enough historical data to establish the trends in the number of new and total businesses in 100 countries during the eight year-period 2000 to 2008. In this regard, it is interesting to note that developing countries have a higher trend in both the number of total and new businesses, with some of the countries doubling their numbers during the period, whereas industrialized countries show little variation during the same period (Figure 2). Interestingly, the majority of the surveyed countries with a negative business growth were those that suffered a war, civil unrest or remarkable political instability during the eight-year period.<sup>2</sup>

> 2 Such as Lebanon, Bosnia and Herzegovina and Haiti.

7
I POLICY RESEARCH WORKING PAPER 3105

# Abstract

Berger, Hasan, and Klapper contribute to both the finance-growth literature and the community banking literature by testing the effects of the relative health of community banks on economic growth, and investigating potential transmission mechanisms for these effects using data from 1993-2000 on 49 nations. Data from both industrial and developing nations suggest that greater market shares and efficiency ranks of small, private, domestically-owned banks are associated with better economic performance, and that the marginal benefits of higher shares are greater when the banks are more efficient. Only mixed support is found for hypothesized transmission mechanisms through improved financing for small and medium enterprises or greater overall bank credit flows. Data from developing nations are also consistent with favorable economic effects of foreignowned banks, but unfavorable effects from state-owned banks.

This paper-a product of Finance, Development Research Group-is<sup>partofalargereffortinthegrouptostudy</sup> international banking. Copies of the paper are available free from the World Bank,<sup>1818H Street NW, Washington,DC</sup> 20433. Please contact Agnes Yaptenco, room MC3-446, telephone 202-473-1823,<sup>fax202-522-1155,emailaddress</sup> ayaptenco@worldbank.org. Policy Research Working Papers are also posted on the Web<sup>at http://econ.worldbank.org.The</sup> authors may be contacted at aberger@frb.gov, hasan@rpi.edu, or Iklapper@worldbank.org.<sup>August2003.(38 pages)</sup> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage_<sup>_the exchangeof ideas about_</sup> _development issues. An objective of the series is to get the findings out quickly, even if the presentations_<sup>_are less than fully polished. The_</sup> _papers cariy the names of the authors and should be cited accordingly. The findings, interpretations,_<sup>_and conclusions expressed in this_</sup> _paper are entirely those of the authors. They do not necessarily represent the view of the World_<sup>_Bank,its Executive Directors, or the_</sup> _countries they represent._ Produced by the Research Advisory Staff