rafmacalaba/datause-encoder-data huggingface dataset:
# INTRODUCTION

1. The tropical timber market is characterized by a large number of species and also by numerous processed products. In 1945, only 19 species were widely known and traded, while today this number has climbed to about 150. While in the past most of the trade was in the form of logs, recently the share of processed trcpical timber has increased considerably. Table 1 shows the evolution in trade volumes for selected tropical timber products.

Table 1: VOLUME OF TRADE IN TROPICAL TIMBER PRODUCTS

||1969-71<br>1979-81|1986-88|
|---|---|---|
||-----------'000 cubic meters-|------------|
|Logs|36,107<br>37,359|28,967|
|Sawnwood|4,394<br>8,958|9,963|
|Plywood|1,816<br>3,127|7,700|
|Veneer sheets|548<br>500|853|

Source: IECCM and FAO.
1991-2016 (Brown et al., 2017). If this ratio is above 0.6 for a given industry, the industry is classified as high-R&D.<sup>17</sup> This industry classification for the United States is then applied to all other countries. This classification does not vary either across countries or over time. Moreover, it yields marked differences in the asset composition across high- and low-R&D firms in the sample. For example, the ratio of intangible to tangible assets is 0.62 for the median high-R&D firm in the average country, whereas it is 0.09 for the median non-high-R&D firm.

Consistent with the arguments presented above, the estimates show that: (a) equity issuances are associated with faster growth among high-R&D issuing firms than among other issuing firms; and (b) equity issuances have an especially strong, positive link with intangible investments by high-R&D firms. As predicted, these patterns hold for equity financing but not for bond financing (Table 7, Panels B and C). As an example of the first finding, in the year of an equity issuance, the growth rate of employment is 2.45 percentage points faster for high-R&D firms than other issuing firms. On the second finding, our results indicate that intangible investment is, in fact, more responsive to equity issuances than is investment in fixed capital. In the year of an equity issuance, the growth of intangible assets is 3.16 percentage points higher for high-R&D issuers than for the rest of issuers, and this differential increases to 4.35 percentage points during the post-issuance years (Table 7, Panel B).<sup>18</sup> In contrast, the interaction term for tangible fixed assets is not significant in the year of issuance and is quantitatively small in the post-issuance period.

The rapid expansion of intangible assets by high-R&D firms following equity issuances is not reflected in an immediate increase in sales. This result is consistent with the view that, relative to fixed

> 17 This threshold corresponds to the industry at the 75th percentile of the R&D intensity distribution for the U.S. Based on this classification, high-R&D industries correspond to two-digit SICs 28 (chemical and allied products), 35 (industrial machinery and equipment), 36 (electronic and other electric equipment), 38 (instruments and related products), 73 (business services), and 87 (engineering and management services). Most of the firms with an industry classification in business services have a four-digit SIC between 7371 and 7375, which involves activities such as computer programming, computer processing, and information retrieval.

> 18 The estimated differential growth during the year of issuance is approximately 31 percent of the average firm growth in intangibles in our sample.

23
- Tarozzi, A. (2011). Can census data alone signal heterogeneity in the estimation of poverty maps? _Journal of Development Economics_ , **95,** 170–185.

- Tarozzi, A. and Deaton, A. (2009). Using census and survey data to estimate poverty and inequality for small areas. _Review of Economics and Statistics_ , **91,** 773–792.

- Teluguntla, P., Thenkabail, P., Xiong, J., Gumma, M.K., Giri, C., Milesi, C., Ozdogan, M., Congalton, R., Tilton, J., Sankey, T.R., Massey, R., Phalke, A. and Yadav, K. (2015). _Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities_ , Vol. II edn. Land Resources: Monitoring, Modelling, and Mapping, Remote Sensing Handbook edited by Prasad S. Thenkabail. In Press.

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- Van Veelen, M. and van der Weide, R. (2008). A note on different approaches to index number theory. _American Economic Review_ , **98,** 1722–1730.

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58
|**Table A19: IFAD Rural Sector Perf**<br> <br>|**ormance**<br>|**Ass**|**essmen**|**ts (I**|**FD)**|||
|---|---|---|---|---|---|---|---|
|**Data Provider**<br>International Fund for Agricultural Develo|pment|||||||
|<br>**Description**<br>Multilateral development institution head<br>|<br>quartered|in|Rome,|Italy,|financ|ing agricultu|ral|
|investments in developing countries.<br> <br>||||||||
|**Website**<br>www.ifad.org||||||||
|**Data Source**<br>Rural Sector Performance Assessments||||||||
|**Type**<br>Expert assessment||||||||
|**Respondents**<br>IFAD country economists, subject to centr|alized revi|ew||||||
|**Frequency**<br>Annual since 2004||||||||
|**Coverage**<br>IFAD client countries||||||||
|**Public Access**<br>Yes||||||||
|**Description**<br>This source assesses 12 dimensions of the<br>|rural pol<br>|icy e<br>|nvironm<br>|ent o<br>|n a 6-<br>|point scale. T<br>|he<br>|
|assessments are used in IFAD's perfor<br>resources across countries.|mance-ba|sed|allocati|on sy|stem|for distributi|ng|
||2006|2005|2004|2003|2002|2000<br>1998|1996|
|**Voice and Accountability**||||||||
|Policy and legal framework for rural organizations|X|X|X|..|..|..<br>..|..|
|Dialogue between government and rural organizations|X|X|X|..|..|..<br>..|..|
|**Political Stability**||||||||
|NA|..|..|..|..|..|..<br>..|..|
|**Government Effectiveness**||||||||
|Allocation & management of publlic resources for rural<br>development|X|X|X|..|..|..<br>..|..|
|**Regulatory Quality**||||||||
|Enabling conditions for rural financial services development|X|X|X|..|..|..<br>..|..|
|Investment climate for rural businesses|X|X|X|..|..|..<br>..|..|
|Access to agricultural input and produce markets|X|X|X|..|..|..<br>..|..|
|**Rule of Law**||||||||
|Access to land|X|X|X|..|..|..<br>..|..|
|Access to water for agriculture|X|X|X|..|..|..<br>..|..|
|**Control of Corruption**||||||||
|Accountability, transparency and corruption in rural areas|X|X|X|..|..|..<br>..|..|
|**Country Coverage**|**100**|**121**|**124**|**..**|**..**|**..**<br>**..**|**..**|
|**Year of publication**|**2006**|**2005**|**2004**|**..**|**..**|**..**<br>**..**|**..**|

57
governments in the 1980s and 1990s were not particularly transparent and neither are other non-Paris Club creditor governments today. This lack of data on defaults and debt forgiveness is problematic, especially in a world battered by the Covid-19 crisis. Any assessment of distressed developing countries today requires knowledge on what happens to outstanding Chinese loans, which can be very large in volume and difficult to repay due to their comparatively short maturities and high interest rates (see Horn et al. 2021 for details).

This paper draws on a broad range of sources to create an encompassing data set of credit events on Chinese overseas loans to developing and emerging market sovereigns over 20002021, including many ”hidden defaults.” We present information, where available, on the timing and basic terms of debt restructuring operations and provide context on how defaults and debt restructurings on debts owed to China compare with other creditor groups. The analysis incorporates updated existing data sets of defaults and restructurings involving private external creditors (in particular, international loans and bonds) and of debt defaults toward mostly high-income governments organized in the so-called Paris Club. Three findings stand out.

First, we find that restructurings on Chinese debts are more frequent than restructurings involving the private sector or Paris Club official creditors. Since 2008, Chinese creditors arranged at least 71 distressed debt restructurings, more than three times the number of restructurings of private external debts (we record 21 bond and bank debt restructurings) and higher than the total number of Paris Club restructurings with distressed debtors (68 cases) in the same period. The increase of restructurings with China in the past 7 years largely reflects deteriorating economic fundamentals in many EMDEs and the fact that China became the largest creditor for many high-risk countries.

Second, many restructurings with Chinese state-owned creditors are preceded by a lengthy spell of default involving payment arrears, according to data from the World Bank’s Debtor Reporting System (DRS). Yet, the observed scope of arrears in this database is likely the tip of the iceberg because missed payments can go unreported, not just toward China but toward other lenders as well. It is a significant shortcoming in the literature that we lack a systematic, publicly available dataset on the start and end date of sovereign defaults towards official creditors as well as supplier credits. “Hidden defaults” will likely remain a challenge in the years ahead.

Third, we show that Chinese lenders only grant limited debt relief, which can result in lengthy debt overhang spells and “serial restructurings”. China has been a major contributor to the 3
In terms of a gender gap with respect to severe education deprivation at the state level, estimates displayed in Figure 10 shows that there are especially in rural areas in several northern states, cases of girls being about five percentage points more likely to have never been to school than boys. However, the gender gap is much smaller than the rural-urban and north-south gap. Interestingly, Figure 10 displays also cases wherein higher shares of boys are severely education deprived than girls, such as in Taraba, Kebbi and Oyo.

Figure 10: State-Level Education Deprivation by Region and Gender <!-- Start of picture text --> Urban Girl 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Rural Boy 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Urban Girl 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Rural Boy 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Urban Girl 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Rural Boy 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Urban Boy 5-17<br>Rural Boy 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Urban Girl 5-17<br>Rural Girl 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Rural Boy 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Rural Girl 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Rural Girl 5-17<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. For Ebonyi the shares for urban boys and girls are excluded because of too limited sample size. The threshold for exclusion is set at less than 30 observations per demographic group.

23
increasing overall levels of economic growth and employment generation. Microfinance may be able to help some households take advantage of those processes, but nothing so far suggests that it will ever drive them. (Morduch 1999, 1610) That early conclusion has been broadly affirmed by a string of observational studies that reveal modest impacts on average levels of household income and consumption (Roodman 2011, Armendáriz and Morduch 2010), and early randomized controlled trials largely concluded the same (Banerjee et al. 2015c). The most recent studies, though, provide a deeper understanding of the diversity of experiences (some of which are promising).

For a few observers, the evidence that microfinance is not a cure-all brands it as a failure (e.g., Bateman and Chang 2012). Yet that conclusion puts too much weight on the lofty bar set by the early boasts about microfinance’s ability to turbo-charge entrepreneurial finance and boost average household income. At the same time, it also puts too much weight on the negative readings of the first pieces of RCT evidence (six studies packaged together in an issue of the _American Economic Journal: Applied Economics_ ; Banerjee et al. 2015).

Until recently, the main empirical challenge in the impact literature has been dealing with selection bias. Measured impacts will be over-stated if outcomes for microfinance borrowers are compared to outcomes for non-borrowers without fully accounting for the ways that participants may have advantages from the outset. Borrowers, for example, may be more industrious and better connected to market networks, and many of these dimensions are hard to control for in 21
27

# NOTES

* The author would like to thank Johannes Linn, Emmanuel Jimenez, and Bela Balassa for their comments.

1. The World Development Report, 1987 essentially places its emphasis on the role of government as a regulator of economic activity, not as a direct economic agent.

2. Studies which measure the size of government in terms of taxes include Marsden (1983) and Skinner (1987).

3. National income accounting treatment of public enterprises is discussed in Pathirane and Blades(1982).

4. Evidence on the growth in state enterprises can be found in Short (1984). Although heavily qualified, Short provides estimates of the average percentage share of public enterprise output out of GDP at factor costs for industrial countries (9.6%) and daveloping countries (8.6%). The variance around these averages, not surprisingly, is large.

# 5. Saunders and Klau(1985), p. 28.

6. For a critique of Baumol's hypothesis, see Mueller (1987), pp. 120-21, and the papers cited therein.

7. Pluta(1981), Ram(1987) and Beck(1979,1982) advocate and employ constant prices in their estimates of the share of government expenditures out of GDP. Musgrave(1981), pp. 84-87, argues in favor of the use of current price estimates.

8. See Ram (1986a, 1986b, 1987).

9. A longer and more global perspective is given in Webber and Wildavsky(1986). They devote several hundred pages to tax and expenditure issues from the ancient through the medieval world.

10. Peltzman(1980) updates the Peacock and Wiseman(1961) series.

11 Musgrave(1981), p. 83.

12. Beck(1979) makes a similar point for 13 OECD nations, including the U.S., for the period 1950 to 1977. He concludes, "... much, if not most, of the increase in government consumption expenditure has resulted from higher costs, rather than expansion of volume." (p.314)
Perry, Guillermo, William Maloney, Omar Arias, Pablo Fajnzylber, Andrew Mason and Jaime Saavedra. (2007). Informality: Exit and Exclusion. World Bank Latin America and Caribbean Studies: World Bank, Washington D.C.

Rauch, James, (1991). “Modeling the Informal Sector Formally.” _Journal of Development Economics_ 35 (1): 33-47.

Ronconi, Lucas, (2007). “Enforcement and Compliance with Labor Regulations”, Mimeo. UC Berkeley.

Tokman, Victor, (1992). _Beyond Regulation: The Informal Sector in Latin America_ . Boulder: Lynne Rienner Publishers.

29
privatization. On the one hand privatization increases productive efficiency. This provides a first rationale for privatizing, the _efficiency_ argument. On the other hand in profitable increasing returns to scale industries allocative inefficiency combined with the critical budgetary conditions found in most developing countries favor public ownership. It is an effective way to combine the regulation of the industry with a maximal level of taxation. In contrast when profitability is low privatization is a better option. This is the _public revenue_ rationale of privatization which is explored in the present paper. Li, Li, Lui and Wang (2001) based on a data set from privatizations in China test whether government privatizes in order to enhance production efficiency, the ‘efficiency’ argument, or to increase its revenue, the ‘revenue’ argument. They conclude “ _our tests based on the data set from China reject the efficiency theory and yield support for the revenue theory._ ” Warlters (2004) and Auriol and Tuske (2005) also find that macro economic variables, such as the introduction of a VAT system, country risk ratings, or the level of public debt significantly influence the probability of infrastructure privatization. The macro economic rationale works also for developed countries. Bortolotti, Fantini and Siniscalco (2003) analyze panel data for privatization around the world. They consider any type of industry (i.e. competitive and oligopolistic ones) and any type of countries (i.e. rich and poor). They find that privatization is more likely in wealthy democracies with right-wing governments, high debt, liquid stock markets and a legal system that protects shareholders.

A more subtle implication of the model arises while focusing on profitable infrastructure and public utilities. Depending on the value of _λ_ privatization might be the optimal policy in a rich country while it is not necessarily the same in a poor one. Under the paper assumptions the critical value _λ_<sup>�</sup> lies in the range of the opportunity cost of public funds generally retained for developed economies (i.e. _λ_<sup>�</sup> _∈_ [0 _._ 35 _,_ 1 _._ 10] table 1).<sup>31</sup> If the model assumptions are empirically relevant we should observe that the probability to privatize profitable infrastructure and public utilities in developing countries decreases with the opportunity cost of public funds.<sup>32</sup> Warlters (2004) was the first to check empirically

> 31The critical value _λ_ � is the same in figure 1 and 2. Indeed _λ_ � in figure 2 is such that _K RM/P M_ ( _λ_ ) = _<u>V</u>_ 2<sup>.</sup> It is easy to check that this equation is equivalent to equation (14).

> 32The prediction is reversed for advanced economies (see figure 2) as suggested by Bortolotti, Fantini and Siniscalco (2003) where the probability of privatization increases with the level of debt.

32
# **1. Introduction**

Programs catering to the financial well-being of the elderly are coming under strain across the world. The depletion of public coffers during the financial crisis has left governments struggling to cope with rising retirement costs triggered by aging populations, low birth rates, and weakened informal safety nets (Ellis, et al., 2014). That this crisis threatens both the elderly and the young is well-established; it also endangers future generations, who will shoulder a disproportionate share of the costs of caring for older generations directly or indirectly (Gratton, 1996; Fridson, 2015).

Despite rising interest in the safety net for elderly people, there is very limited information on both formal and informal voluntary saving for old age. Drawing on novel microeconomic data, this paper offers a detailed descriptive analysis of saving for old age around the world and across geographic regions. It also features an empirical analysis of the determinants of saving for old age, using detailed individual characteristics, macroeconomic characteristics at the country level, and country pension-system characteristics. To our knowledge this is the first study examining saving for old age around the world using detailed global micro-data.

The 2014 Global Findex data show how and why adults save and borrow and shed light on their financial resilience to unexpected expenses (Demirgüç-Kunt, et al., 2015). In 2014, 56% of adults around the world reported having saved money in the past 12 months. Adults in high-income OECD economies and East Asia and the Pacific were the most likely to have done so, with 71% reporting that they had saved, followed by 60% of adults in SubSaharan Africa. In other regions the figure stands between 30% and 40%. The 2014 Global Findex survey also asked about three specific reasons for saving. While marked differences emerge across regions, almost 25% of adults worldwide reported having saved in the past year for old age. A similar share reported saving for education expenses, while 14% reported saving to start, expand, or operate a business.

The percentage of adults saving for old age varies considerably across economies: 39.7% in high-income OECD countries (hereafter OECD<sup>HI</sup> ); 36.6% in East Asia & Pacific (hereafter EAP); 11.8% in Europe & Central Asia (hereafter ECA); 10.6% in Latin 2
from foreign firms (manufacturing firms only), and whether the firm has an internationallyrecognized quality certification. Paunov (2016) used internationally-recognized quality certifications. Crowley and McCann (2017) used the measures of product and process innovation to capture the incidence of innovation. The ES data indicated that around 34 percent of firms engaged in product innovation. The corresponding rates for process innovation, R&D spending and internationally-recognized quality certificate are 37 percent, 16 percent, and 18 percent respectively. Around 14 percent of manufacturing firms use technology licensed from foreign firms. Online engagement is captured by two variables. One is whether firms use email to engage with clients and suppliers. The other is whether firms have their own website. Around 73 percent of firms use e-mail to engage with suppliers and clients while 44 percent have their own website. Summary statistics and variable descriptions are provided in tables 1 and A2 respectively.

Our main variable of interest is a measurement of public procurement regulatory quality over the whole public procurement lifecycle (��). This measure is taken from Djankov et al. (2017). The public procurement index captures three crucial phases of the public procurement lifecycle - (i) bid preparation, (ii) bid and contract management, and (iii) payment to suppliers. Bid preparation includes the needs assessment and the call for tender. Bid and contract management covers various aspects such as eligibility of foreign firms, availability of online bid submission, the existence and requirements for bid security, bid evaluation criteria, the use of model contracts with standard clauses for awarding a contract, and measures capturing the terms of modifications to the procurement contract. The payment of suppliers indicator captures the number of procedures required to request payments, the timeframes for processing and disbursing payments, and how delayed payments are handled. The overall public procurement index is an amalgamation of all 10