rafmacalaba/datause-encoder-data huggingface dataset:
product/process, or development of R&D activities). Our analysis also points that the decrease of sales growth rate of younger firms was more severe than older companies. The decomposition exercise confirms that the positive difference of sales growth rate in favor of innovative and in favor of young companies, before the crisis, was reversed afterwards. For innovative companies, we found evidence that the reduction in the “premium” for the ability to innovate – which we interpret as Schumpeter’s “animal spirit” – after the crisis contributed to explain the worse performance of innovative firms. Conversely, in the case of young enterprises, results suggest that their “ability to learn” contributed to mitigate the negative impact of the crisis on sales growth performance. Overall, we interpret these results as indication that the achievement of pre‐crisis growth rates in those countries may be difficult _._ The paper is organized as follows. The next section describes the data used and exposes the empirical methodology applied. Section 3 presents results on the empirical analysis, and Section 4 summarizes the main results.

# **2 – Data and Methodology**

We use a unique panel data for 1,686 firms in six countries (Bulgaria, Hungary, Latvia, Lithuania, Romania, and Turkey) covering manufacturing, retail and other service sectors.<sup>3</sup> This panel results from the matching of two datasets. The first one is the World Bank’s Financial Crisis Survey (EFCS) ‐ implemented in June/July 2009 and designed to capture the effects of the crisis on key elements of the private economy: sales, employment, finances, and expectations about the future. The second one is the World Bank’s Enterprise Survey (ES) carried out in 2008. With an original sample that is stratified by firm size, sector and region and representative of the private nonagricultural formal economy in each country, the ES 2008 provides information on firm characteristics, various performance measures and the business environment, most of them refereed to fiscal year of 2007, the pre‐crisis scenario. The merger between the two datasets was possible since the EFCS covers a subsample of firms drawn from the set of firms interviewed in the ES 2008. As the participation in EFCS was voluntary and the entire original sample of the ES, which covered

> 3Turkey is the exception, where only the original manufacturing subsample was targeted.

3
ensured the robustness of the methodologies used. Rather, we focus on the findings in these papers on the hypotheses we explore, and derive lessons on which approaches to regional development work and which do not.

# <u>4.1 What do cross-national studies say?</u>

The clear evidence from cross-national studies shows that that EU has witnessed cross-national convergence. Poorer countries have been catching up with the richer ones, and Ireland has even surpassed several richer EU countries (see Exhibits 2a and 2b). However, there is limited evidence to show that this catch-up is due to EU structural funds or EU cohesion policy. Beugelsdijk and Eijffinger (2005) show that the EU-15 countries converged in the period 19952001. They find that structural funds have a positive effect on growth. According to them, “...if the change in the rate between the structural funds and the GDP changes by 1 percentage point, the GDP growth will increase by a 0.32 percentage point...” They also find that there is no evidence that “corrupt” countries gain less growth from Structural Funds and interpret this as evidence against moral hazard.

On the other hand, most of the other evidence shows little or no impact of EU regional policy on convergence. Barry (2003) looks at the economic performance of the four cohesion countries – Greece, Ireland, Portugal and Spain – relative to the EU and finds they converged to the EU average during 1960-1973, diverged 1974-1986 and then resumed convergence from 1987 to 2000. According to his analysis, most of the factors that affected convergence or divergence were market factors such as labor market conditions, FDI inflows, monetary and fiscal policies, etc. For example, he says the reasons for convergence during the last period were (1) tighter fiscal and monetary policy for euro entry; (2) labor market reforms in Spain; (3) increased FDI flows in Ireland; (4) administrative reforms in Greece; and (5) liberalization after EU accession in Spain and Portugal. On the issue of EU aid, he says that increased EU aid in the last period has contributed to convergence. However, he does not show any specific analysis in support of this conclusion but points to other studies that looked at the relationship between EU regional aid and convergence. Given this, the preponderance of evidence locates convergence in market opening rather that in EU aid.

16
comprehensive picture of budget execution. Finally, it provides the information to ensure improved efficiency and effectiveness of government financial management. Generally, increased availability of comprehensive financial information on current and past performance assists budgetary control and improved economic forecasting, planning, and budgeting (Diamond and Khemani 2005). Dorotinsky and Matsuda (2001) eloquently map how FMISs can contribute to the high-level PFM objectives of macro-fiscal stability, strategic allocation of resources, and operational efficiency.

1.3 Given the rapid pace in technological change and innovations in the field, there are significant opportunities to reap greater digital dividends. For example, innovations in digital payments could fundamentally change the way governments transfer funds, which has the potential to expedite basic PFM functions and strengthen accountability (Cangiano et al. 2017). While this report embraces the enthusiasm of such opportunities, _–_ it also points out the foundational role of _analog complements_<sup>_3_</sup> the policy and institutional environment necessary to not only enable technological innovations, but also to safeguard against potential risks.

# **An Overview of the World Bank FMIS Engagement**

1.4 The World Bank has invested significant time and resources into FMIS operations. In the last 30 years (since 1985) about US$ 4,079 million has been committed through 134 operations across 74 countries.<sup>4</sup> There has been a clear upwards trend in the engagement following an increased recognition of the potential role of ICT in government. By the turn of the century, about six operations were approved annually averaging about US$33.3 million each. FMIS projects have been prominent in the Governance Global Practice (GP) portfolio. Since 2000, 28 percent of all projects mapped to the Governance GP had at least one FMIS component, making up about 17 percent of the GP’s overall commitment.

> 3 See the World Development Report 2016 (World Bank 2016) for a more expansive discussion of the role of analog complements as facilitating factors for ICT innovations.

> 4 These figures reflect total project amounts. As FMIS are usually part of a larger PFM engagement in most cases not all project funds are dedicated to FMIS related activities. Operations include active, closed, and pipeline projects.

**12**
Figure 11 shows average annual growth in KLEMS labor productivity for each sub-sector across EU-8 countries in 1997-2000 and in 2000-2004. By comparing growth in KLEMS labor productivity across sub-sectors, this figure takes into account changes in sub-sector domestic and international output prices. Three main patterns emerge from the figure. First, there is enormous disparity in labor productivity growth rates across countries in all sub-sectors, as was the case in Figure 10. Second, retail trade, inland transport, post and telecommunications, and financial intermediation are characterized by strong productivity growth in most EU-8 countries in 1997-2004. Third, most sub-sectors exhibit large variation in average annual growth rates of KLEMS labor productivity between 1997-2000 and 2000-2004. Specifically, productivity growth in post and telecommunications and in financial intermediation (computer and related activities) accelerates (decelerates) in 2000-2004 relative to 1997-2000 in most EU-8 countries.

Figure 11 also shows average labor productivity growth rates in EU-15 countries. In all sub-sectors with the exception of post and telecommunications and computer and related activities, average labor productivity growth during 1997-2004 is lower in EU-15 countries than in most EU-8 countries. In fact, a similar type of finding is obtained even when comparing the labor productivity growth rates of more aggregate sub-sectors shown in Figure 10 to those in similar sub-sectors in OECD countries as documented by Wolfl (2003).<sup>22</sup> may reflect a catch-up effect given the large gap in services efficiency that separates ECA countries from other developed countries but also differences in economic performance during the 1997-2004 period, since EU-15 and other OECD countries register small GDP growth rates while some of the ECA countries exhibit strong GDP growth rates, especially since 2001.

The finding of accelerating labor productivity growth in 2000-2004 for the services sector in ECA countries is prima facie evidence in favor of the argument proposed by Ark and Piatkowski (2006) that the second ‘expansionary’ phase of sustained convergence of ECA countries towards EU-15 countries will rely importantly on the services sector. If recent productivity growth rates continue into the future, they will lead to improvements in efficiency of ‘backbone’ services sub-sectors such as

> 22 Recall that the WIIW database does not allow direct benchmarking relative to EU-15 countries.

24
**Figure 3: Relationship between the extent of firm innovation and adoption, and the sources of funding of fixed assets**

<!-- Start of picture text --> 0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>-0.1<br>Innovation Adoption Innovation Adoption Innovation Adoption<br>score score score<br>% Fixed assets funded by % Fixed assets funded by % Fixed assets funded by<br>banks NBFI other<br><!-- End of picture text --> Note: This figure reports the coefficients (denoted as diamonds) and the confidence intervals at the 90 percent level (denoted by the rectangular area) resulting from the estimation of Equation (2). The dichotomous dependent variables are the innovation score and adoption, as described in Table 1. We report the coefficients and the relative confidence intervals for the percentage of fixed assets funded by banks, or non-bank financial institutions (NBFI), or funded by others. All additional controls are as per Equation (2). We also include country and year fixed effects, as done in column (4) Table 4.

The literature stresses the importance of technological opportunity to understand the relationship between a firm characteristics and firm-level innovation. Enterprises in information technology or in other high-tech industries are more likely to innovate, according to the World Bank Enterprise Survey data for East Asia (Cirera and Mason, 2021).<sup>5</sup> We, therefore, take a closer look at the innovation performance of the manufacturing firms based on their technology intensiveness. We use the classification from the United Nations Industrial Development Organization (UNIDO),<sup>6</sup> which is based on the R&D expenditure incurred in the production of manufactured goods. Firms are categorized in two buckets: low-technology and medium-high-technology. In line with Bircan and De Haas (2020), we find that firms in less technologically oriented industrial sectors innovate more while using external finance (Table 5). This is particularly true for firms financed through non-bank financial intermediaries, as denoted by the magnitude of the coefficients.

> 5 Evidence from few countries suggests that innovation is equally likely in knowledge-intensive business services (Pires et al. 2008).

> 6 <u>https://stat.unido.org/content/focus/classification-of-manufacturing-sectors-by-technological-intensity-%2528isic-revision4%2529;jsessionid=4DB1A3A5812144CACC956F4B8137C1CF.</u>

16
Van der Klaauw and Wolpin (2008) who study how the design of U.S. social security rules affects decision-making within a collective household model. As in their model, we allow households to accumulate private savings in addition to pension benefits for consumption in retirement. One important difference is that we focus on gender aspects of pension design, so we incorporate into our model divorce and separation. These events are major financial risks for women with low private and pension savings and low attachment to the labor force. We do not incorporate health or health insurance, in part because Chile has a public health insurance program. Also, we allow only women and not men to work part-time. Another important difference is that our model allows workers to make a choice about being employed in the informal sector, in which they do not contribute to their pensions or make progress towards qualifying for a minimum pension. This is a crucial margin from a public finance point of view in Chile and a majority of countries around the world. Other recent collective models of joint retirement include Blau and Gilleskie (2006), who focus on retirement incentives related to spousal health benefits, and Casanova (2010), who investigates the timing of retirement between two married individuals. Because these papers are not concerned with the difference in financial risk borne by each spouse individually, they model divorce and death through a terminal value, instead of following, as we do, individuals after separation or death has dissolved the household.<sup>7</sup> Many individuals, especially women, spend a significant part of their retirement as widows, so following them after their spouse dies is important to understanding old age poverty.

This paper also builds on a previous study by Joubert (2015) of the relationship between pension design and labor force participation decisions with regard to informal/formal sector work using the same Chilean data that we use. This study extends Joubert (2015) by using a collective household model and by allowing for divorce and fertility, which make the model suitable for studying the differential impact of the 2008 pension reform on men and women.

Lastly, this study is more broadly related to a literature initiated by Auerbach and Kotlikoff (1987) that examines the welfare effects of pension reform using life cycle and

> 7Other notable papers are Gustman and Steinmeier (2000, 2002) who consider a non-cooperative game solution to the household joint decision, and Gallipoli and Turner (2011) who compare non-cooperative and collective models of joint retirement. In the macroeconomic literature, Nakajima (2011) calibrates an equilibrium collective model of joint retirement, focusing on spousal and survivor benefit policies.

5
informal finance: loans from family, friends, money lenders or other informal sources. It is possible that increasing reliance on retained profit represents not a decline in the institutions of formal finance, but an increase in the level of development – a maturation – of the financial sector and/or the business sector itself.

We explore these issues using regression analysis in Tables V.3 and V.4. Table V.3 analyzes the relationship between firm characteristics and financing shares for our benchmark market economies. Explanatory variables (characteristics) include ownership, export activity, location, size, and sector dummies. For consistency of interpretation across BEEPS surveys, bank financing includes state banks; the state financing that is separately identified is non-bank financing, e.g., grants and subsidies. The table shows, not surprisingly, that larger firms borrow more from banks and rely less on retained earnings; for bank financing, the coefficient on log employment is about 0.03, implying that an increase of employment from 50 to 500 would mean an increase in the share of bank financing of about 7 percentage points (=0.03*ln(10)). Foreign-owned firms rely less (about 10 percentage points) on bank financing and correspondingly more on retained earnings, presumably because foreign owners have “deep pockets”; the adjustments by foreign owners probably appear under retained earnings rather than equity because equity injections are made on an irregular/long-term basis. Exporters rely more on external financing (about 5 percentage points for bank borrowing and equity combined).

Table V.4 reports the same regression analysis using the 2005 BEEPS sample of TEs. In this initial formulation, we employ ownership dummies for new private and state-owned firms (the base category is privatized firms) but constrain the coefficients on explanatory variables to be the same across all types of firms. The results are qualitatively similar: larger firms rely more on external financing and less on retained earnings; foreign owned firms rely less on external financing, again presumably because their owners have deep pockets; exporters rely more on external financing. The ownership dummies are suggestive: state-owned firms, ceteris paribus, rely more on state financing and less on bank financing. But here we have a problem, because ceteris may not be paribus: it may 31
# **Table 9: Panel Regressions – Volume of all transactions across different crypto-assets**

- <u>Dependent Variable: Log monthly on chain bitcoin volume (all transaction sizes), US dollars</u>

|Variable|(1)<br>Bitcoin|(2)<br>Ether<br>**All countries**|(3)<br>Stablecoins|(4)<br>Non-stablecoins|
|---|---|---|---|---|
|Inflation (yoy)|-0.286|0.358|-0.115|0.017|
|Change exchange rate (mom)|0.582*|0.934|1.035|0.709*|
|Change Bitcoin price (mom)|0.522***|0.420***|0.089|0.329***|
|Change Ether price (mom)|-0.020|0.087*|0.047|0.111***|
|US 10-year Yield (inflation adjusted)|-0.160***|-0.803***|-0.959***|-0.338***|
|US Inflation Expectation Rate|1.953***|4.311***|2.518***|2.998***|
|VIX|0.007***|0.022***|0.008***|0.012***|
|Change gold price (mom)|-1.106***|-1.165***|-2.182***|-1.139***|
|Pandemic dummy|0.891***|1.427***|0.976***|1.095***|
|N of Observations|3095|3095|3095|3095|
|N of clusters (countries)|136|136|136|136|
|Adjusted R squared|0.634|0.828|0.726|0.752|
|Country-Fixed Effects?|YES|YES|YES|YES|
|Variable|(5)|(6)|(7)|(8)|
||Bitcoin|Ether<br>**EMDEs only**|Stablecoins|Non-stablecoins|
|Inflation (yoy)|-0.402*|0.246|-0.247|-0.115|
|Change exchange rate (mom)|0.608|1.076|1.428*|0.882*|
|Change Bitcoin price (mom)|0.415***|0.286**|-0.073|0.270***|
|Change Ether price (mom)|-0.032|0.157**|0.096|0.105*|
|US 10-year Yield (inflation adjusted)|-0.202***|-0.751***|-0.935***|-0.357***|
|US Inflation Expectation Rate|1.850***|4.089***|2.399***|2.834***|
|VIX|0.003|0.021***|0.005*|0.008***|
|Change gold price (mom)|-1.374***|-1.322***|-2.427***|-1.411***|
|<br>Pandemic dummy|0.960***|1.500***|1.099***|1.149***|
|<br>N of Observations|1837|1837|1837|1837|
|N of clusters (countries)|85|85|85|85|
|Adjusted R squared|0.590|0.780|0.672|0.698|
|Country-Fixed Effects?|YES|YES|YES|YES|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the country level. All independent variables are lagged by one month, except the pandemic dummy. Bitcoin, ether, and gold prices expressed in US dollars. The gold price is expressed per Troy ounce. A constant is estimated, but not reported
Table 3b: Industry-level distribution of female activity in services

||Estimate<br>owne|d count of<br>d establish|female-<br>ments|Share o<br>industryt|f establish<br>hat are fem|ments in<br>ale-owned|Estimat|ed count of<br>employees|female|
|---|---|---|---|---|---|---|---|---|---|
||2001|2006|2010|2001|2006|2010|2001|2006|2010|
|**55** Hotels and restaurants|213,009|201,287|271,978|0.103|0.101|0.099|841,013|889,696|970,847|
|**60** Land transport; pipelines|21,018|27,643|7,204|0.005|0.007|0.001|51,922|53,261|24,952|
|**61** Water transport|2|58|0|0.000|0.003|0.000|511|58|293|
|**63** Supporting/auxiliary transport activities; travel agencies|3,748|2,994|3,279|0.044|0.028|0.020|8,933|13,681|30,970|
|**64** Post and telecommunications|83,704|171,186|60,881|0.143|0.092|0.139|137,654|377,026|97,747|
|**65** Financial intermediation|.|17,739|21,291|.|0.200|0.193|.|40,634|85,296|
|**66** Insurance and pension funding, except social security|.|3,974|4,464|.|0.066|0.086|.|4,522|4,693|
|**67** Activities auxiliary to financial intermediation|.|19,693|28,680|.|0.126|0.092|.|28,900|38,694|
|**70** Real estate activities|2,620|22,206|37,643|0.033|0.110|0.092|5,625|33,088|48,050|
|**71** Renting machinery/equipment & personal/household goods|11,592|12,360|15,035|0.023|0.026|0.034|31,378|30,395|30,185|
|**72** Computers and related activities (for business)|2,082|1,831|4,786|0.129|0.045|0.047|4,746|12,669|21,713|
|**73** Research and development|0|0|0|0.000|0.000|0.000|237|0|0|
|**74** Other business activities|28,801|23,911|70,609|0.050|0.039|0.079|85,987|106,074|242,576|
|**80** Education|294,912|292,002|333,376|0.271|0.318|0.357|970,366|953,301|1,206,736|
|**85** Health and social work|142,865|139,060|82,711|0.110|0.131|0.081|340,811|398,808|407,770|
|**90** Sewage, refuse disposal, sanitation, and similar activities|37,609|20,374|10,203|0.826|0.425|0.292|47,899|35,204|13,094|
|**91** Activities of membership organizations n.e.c.|1,616|1,550|1,278|0.036|0.004|0.004|1,230|11,124|3,307|
|**92** Recreational, cultural and sporting activities|10,916|7,886|8,972|0.031|0.035|0.036|64,188|28,950|27,826|
|**93** Other service activities|237,393|334,514|469,606|0.094|0.139|0.167|1,088,702|1,000,546|894,740|
|Nationwide|1,091,887|1,300,267|1,431,996|0.079|0.088|0.089|3,681,202|4,017,939|4,149,488|

Notes: See Table 1.
WPS3237

# Pre-empting Protectionism in Services: The WTO and Outsourcing

Aaditya Mattoo* Sacha Wunsch** _Abstract_ : Cross-border trade in services is growing rapidly, with both developed and developing countries among the most dynamic exporters. Despite the substantial global benefits from such trade, the adjustment pressures created in importing countries could provoke a protectionist backlash - some signs of which are already visible in procurement and regulatory restrictions. The current negotiations under the Doha Development Agenda offer an opportunity to lock in current openness and preempt protectionism. This note describes how a bold initiative under the General Agreement on Trade in Services (GATS) can help secure openness.

## World Bank Policy Research Working Paper 3237, March 2004

_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._

*World Bank, **Institute for International Economics. Discussions with Rudolf Adlung, Abdel-Hamid Mamdouh and Juan Marchetti were critical to developing the arguments presented here.  The comments of Crawford Falconer, Bernard Hoekman, Alejandor Jara, Deepak Mishra, Julia Nielson, Anirudh Shingal, B.K. Zutshi and other participants in seminars in India and Paris are gratefully acknowledged.