rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4361

# **Abstract**

This paper explores the impact of the emergence of China and India on foreign capital stocks in other economies. Using bilateral data from 1990-2003 and drawing from the knowledge-capital model of the multinational enterprises to control for fundamental determinants of foreign capital stocks across countries, the evidence suggests that the impact of foreign capital in China and India on other countries’ foreign capital stocks has been positive. This finding is robust to the use of ordinary least squares, Poisson, and negative binomial estimators; to the inclusion of time and countrypair fixed effects; to the inclusion of natural-resource endowments; and to the use of the sum of foreign capital stocks in Hong Kong (China) and mainland China instead of using only the latter’s foreign capital stocks. There is surprisingly weak evidence of substitution in manufacturing foreign capital stocks away from Central America and Mexico in favor of China, and from the Southern Cone countries to India, but these findings are not robust to the use of alternative estimation techniques.

This paper—a product of the Office of the Chief Economist for Latin America and the Caribbean—is part of a larger effort in the department to understand the effects of the growth of China and India on Latin Ameircan and Caribbean economies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dlederman@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
a rectangle around them with his mouse. Based on the points selected by the official, a computer algorithm calculates the boundary and number of days for the resulting closure. The computer algorithm is not publicly available, but government officials have described it in workshops and personal conversations. Closure area increases in the total tons caught by the group of sets (Instituto Humboldt and SNP, 2017; Instituto Humboldt, SNP and CeDePesca, 2018). Closures last three, four, or five days.<sup>11</sup> Higher percentage juvenile values result in longer closures. The official can make manual adjustments to the closure generated by the computer algorithm, for example to ensure the closure covers all the sets selected by the official (Instituto Humboldt and SNP, 2017; Instituto Humboldt, SNP and CeDePesca, 2018).

Vessels are not allowed to fish inside active closures (inside closed areas during the threeto-five-day closure periods). To the extent that vessels conceal fishing inside active closures, this detection avoidance would accentuate my main result that the policy backfires.<sup>12</sup> The regulator announces closures on their website and sends emails and WhatsApp messages to firms (PRODUCE, 2020 _b_ ). Firms communicate closures to vessels at sea using radio, and vessels enter the coordinates of closed areas into their onboard navigation systems. Some fishing companies also monitor the locations of their vessels in relation to active closed areas and call vessels on the radio when they are near an active closure. 43% of closure announcements create multiple closures; the average announcement creates 1.58 closures.

I downloaded all temporary spatial closures announcements from the regulator’s website. The regulator declared 410 closures in the North-Central zone during my study period (Figure

> 11I estimate the effects of closures declared by the regulator, PRODUCE. The scientific agency, Instituto del Mar del Per ́u (IMARPE), can declare closures of up to 10 days. These closures can apply to all fishing grounds (i.e., Peru’s entire Exclusive Economic Zone).

> 12The regulator uses real-time vessel movement data to monitor fishing. The regulator defines fishing as moving slower than two knots at a non-constant heading for more than one hour and penalizes vessels that move in this way inside an active closure (PRODUCE, 2016 _a_ ). This movement indicates fishing because all fishing in the Peruvian anchoveta fishery is “purse seine fishing”, which involves dragging a net in a circle around a group of anchoveta. Vessels’ GPS transponders can be physically disabled but not manipulated; the transponder is inside a closed, metal box and it transmits data to the regulator automatically every 10 minutes. If the vessel’s transponder does not transmit data to the regulator for more than two hours, for any reason, the regulator penalizes the vessel owner (e.g., PRODUCE, 2017 _b_ ).

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TABLE 17 Impulse Responses of Potential Growth to Banking Crises and Epidemics

|**Definition of potential**||**Banking cri**|**ses**||**Epidemics**|||
|---|---|---|---|---|---|---|---|
|<br>**output**|**h**|**World**|**AEs**|**EMDEs**|**World**|**AEs**|**EMDEs**|
||0|-0.574***|-0.538*|-0.763**|-0.731***|-0.846***|-0.68***|
||1|-1.605***|-1.508**|-1.865***|-0.796***|-1.035***|-0.649***|
|**Production-function**|2|-1.75***|-1.979***|-1.402***|-0.77***|-0.911***|-0.655***|
|**approach**|3|-1.467***|-1.958***|-0.451|-0.872***|-1.057***|-0.77**|
||4|-1.286***|-1.929***|0.031|-1.083***|-1.126***|-1.062***|
||5|-1.169**|-1.908***|0.416|-0.866***|-0.849**|-0.895***|
||0|-0.349**|-0.406**|-0.209|-0.229**|-0.247|-0.214|
||1|-0.746***|-0.981***|-0.119|-0.021|-0.198|0.12|
|**Multivariate filter**|2|-0.724**|-1.25***|0.743|0.195|0.169|0.215|
||3|-0.27|-0.81**|1.176**|0.305|0.531*|0.127|
||4|0.127|-0.279|1.183*|0.232|0.63**|-0.081|
||5|0.4|0.052|1.339*|0.335|0.874**|-0.121|
||0|-0.025|-0.044|-0.019|-0.421***|-0.173|-0.525***|
||1|-0.08|0.065|-0.155|-0.334***|-0.287***|-0.358**|
|**Expectations (WEO)**|2|0.028|-0.035|0.076|-0.313*|-0.176|-0.374|
||3|0.276|0.088|0.394|-0.479***|-0.175|-0.609***|
||4|0.174|0.141|0.199|-0.519***|-0.19|-0.661***|
||5|0.142|0.071|0.199|-0.623***|-0.208|-0.808***|
||0|-0.573***|-0.736***|-0.278|-0.664***|-0.792***|-0.564***|
||1|-1.399***|-1.731***|-0.806**|0.139*|0.133|0.146|
|**Unobserved component**|2|-0.364**|-0.67***|0.18|0.075|0.083|0.066|
|**model**|3|-0.133|-0.48***|0.488***|-0.075|-0.059|-0.085|
||4|-0.356**|-0.796***|0.43**|-0.198|-0.028|-0.335*|
|Sources: Laeven and<br>Note: *** indicates s<br>|5<br>Vale<br>igni<br>|-0.299**<br>ncia 2018<br>ficance at<br>|-0.553***<br>; World<br>the 1 p<br>|0.152<br>Bank.<br>ercent level,<br>|0.005<br>** at t<br>|0.191<br>he 5 perc<br>|-0.156<br>ent level,<br>|

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. Sample includes unbalanced panel of 33 advanced economies and 98 EMDEs for 1981-2020.

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facilities is considered a promising method to stimulate diversification beyond agriculture, which is considered a promising pathway for development by the Ethiopian government (PASDEP, 2005).

This paper is organized as follows. The next section reviews related literature and presents our hypotheses. Section three describes the data, presents summary statistics and nonparametric analysis of the non-farm enterprise data. Our empirical framework and results for modeling productivity and growth are discussed in section four. A final section concludes.

# **2 Related Literature**

The idea that geographic concentration of economic activity and market integration can enable more efficient production is well-established (for overviews of related literature, see World Bank 2009, Krugman 1998, Venables 2008). To begin with, geographic concentration typically leads to larger markets, which may enable firms to operate at a larger scale and to capitalize on internal economies of scale (Romer, 1987, Krugman, 1991). The empirical evidence for the existence of increasing returns to scale at the firm-level is weak, however; studies using manufacturing data generally cannot reject the hypothesis of constant returns to scale, nor that of homotheticity (see e.g. Söderbom and Teal, 2004). Tybout (2000) argues that there are no large unexploited scale economies in the manufacturing sectors of most developing countries and points out that market size may nevertheless be an important determinant of the scale at which firms operates. Consistent with the idea that larger markets allow firms to operate at a larger scale, Fafchamps and Shilpi (2003) find that the number and size of non-farm enterprises in rural communities in Nepal are positively correlated with proximity to markets. Similarly, Haggblade et al. (2007) argue that growth of the rural nonfarm enterprises can only be achieved by promoting the production of “tradeables” as

-5-
There are other fields of specialization that stand out. In agricultural economics, the Bank ranks second (just behind the Department of Economics at MIT) while in the field of transition economies, the Bank also ranks second, just behind the Department of Economics at the University of Chicago. For research on international trade the Bank again gets the number two slot (just behind MIT). In the field of banking it comes in at third place (behind the IMF and the Federal Reserve Bank of New York). In research on microfinance, it also comes in at third.

The regional emphasis of the Bank’s work is also evident in the RePEc rankings, broadly consistent with our earlier observations based on Tables 1 and 2. The Bank ranks second for research on South and East Asia, but gets the number one slot for research on China. For research on Central and Western Asia, the Bank ranks third. For Africa the Bank again gets the number two slot (just behind MIT); the same is true for research on international trade. Curiously, the Bank only gets the number 12 ranking for research on “Central and South America.” (And here the Bank is only slightly ahead of the Central Bank of Chile.) The ranking variables used by RePEc are likely to be affected positively by the size of the institution. The Bank as a whole benefits in its RePEc ranking by its size; there are 248 authors affiliated to the Bank who are registered in RePEc. By contrast the average number of authors in the top 20 institutions listed in Table 10 is 98. Even so, the Bank is not the largest institution in Table 10, which is the NBER (with 376 registered authors). “Economics Research, World Bank” has 115 authors registered, and five institutions in Table 10 are larger than that.

Note, however, that the average citation rate per article is not affected (directly) by the size of the institution. The citations per article received by the Bank’s authors in economics puts the Bank in the top 10 amongst the comparator universities, and when we focus on development articles the Bank rises to near the top.

RePEc also produces rankings for individual authors. The Bank has a number of staff who figure prominently in these rankings both across all fields and (especially) in the fields of the Bank’s concentration. (A number of ex-Bank staff also figure prominently.) The Bank currently has 12 authors with h-indices (based on the RePEc data base) of 12 or higher and all these are under “Economic Research, World Bank.” This is typical of the universities and other institutions in the list of the “top 20” in Table 10. There are 19 staff in the Department of Economics at Harvard, that have an h-index of 19 or higher, and there are 25 affiliates of the NBER with h-indices of 25 or higher. But all the rest have indices in the range 9-13.

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# **4.4 Counterfactual Productivity Gains across GDP per Capita**

As discussed previously, distortions in both Industry and Services in 2010s are higher in poor countries (Figure 5). Moreover, the negative relationship between distortions in either sectors and GDP per capita is significant for the 84 countries for which the estimates of distortions are available. The countries in the sample cover a breadth of world income distribution and there is an enormous variation in GDP per capita across the sample. The country at the 90<sup>th</sup> percentile of world GDP per capita distribution is around 15 times richer than the country located at the 10<sup>th</sup> percentile with the gap in GDP per capita exceeding 30 multiples for the 95<sup>th</sup> and 5<sup>th</sup> percentile countries.

Given that distortions are lower in rich countries, it is interesting to analyze what productivity gains can be realized by poor countries if they experienced a convergence in productivity gaps so that the distortions in these countries resembled those in the developed world. Moreover, as all of these countries have a significant share of workforce in agriculture, a drastic reduction in distortions could cause a drastic outflow from agriculture to other sectors. Based on such a motivation, I use the estimated distortions in the United States in 2010s as a benchmark for distortions in the developed world.

I present the result of this exercise in figure 10 where the log of implied growth for a country is plotted against the log of its GDP per capita in 2010 in 2005 International dollars terms. As seen in the figure, the relationship between GDP per capita and the implied growth from changing distortions to US levels is negative and is statistically significant at 1%. The median implied growth for countries having GDP per capita below 4,000 International dollars is much higher (12.5%) compared to the median of countries having GDP per capita above 16,000 International dollars (1%). Implied growth is particularly large in China, Guinea and Puerto Rico where the growth breaches the 50% mark. Implied growth exceeds 25% for another set of seven countries. It is interesting to note that for almost a third of the sample the implied growth is negative implying that the net distortions in these countries is lower compared to the net distortions in the United States. The majority of these countries belong to the Europe and Central Asia region. However, the magnitude of impact is marginal with the maximum contraction being just over 5%.

Distortions are present in all countries even for the most recent decade. This is also true for countries that have seen a steady decline in distortions over time. As a second benchmark of counterfactual changes in distortions, I consider the case where the distortions are completely eliminated. Like in the previous exercise, I present the findings in figure 11 by plotting the log of implied growth such obtained against the log of GDP per capita in 2010.

As distortions are present in all countries in 2010s, a move to no distortions leads to an efficient 17
# 1 Introduction

Corporate debt in many emerging markets and developing economies (EMDEs) has risen significantly since the global financial crisis<sup>1</sup> (IMF 2015), raising concerns about financial stability and spillover risks to the real sector. This paper introduces the Corporate Vulnerability Index (CVI), a novel country monitoring framework that tracks financial conditions of the non-financial corporate sector in EMDEs. Using readily available balance-sheet information of listed nonfinancial firms, the CVI is based on seven indicators<sup>2</sup> which capture four key dimensions of firms’ financial vulnerabilities: debt service capacity, leverage, rollover risk, and economic performance.

In recent years, a growing literature has attempted to quantify corporate financial vulnerabilities in EMDEs (Financial Stability Board, 2015; IMF, 2015, 2016a, 2017; IIF 2015 and 2017; World Bank, 2016; Beltran et al, 2016). While most of these studies assess vulnerabilities in terms of the interest coverage ratio (ICR) and leverage, some also consider corporate vulnerability along other dimensions, such as maturity mismatches (e.g. Gonzalez-Miranda, 2012; Rodrigues Bastos et al, 2015; Alfaro et al, 2017), and find that firms have changed their leverage and maturity structure between 2000 and 2013 to take advantage of benign global financial conditions. However, this literature focuses on a relatively small sample of firms in a select number of countries.

Our paper is related and contributes to the literature in several respects. First, we propose a novel vulnerability index which extends the widely-used concept of “Debt at Risk” (see for example IMF, 2016a). Debt at risk is the total amount of debt in a country (or industry) associated with firms which are deemed financially vulnerable, typically for firms with an ICR below a threshold. Our contribution is to apply this concept across multiple financial vulnerability indicators since firms can be financially vulnerable across multiple dimensions at the same time. The CVI appears to have leading indicator qualities; an increase in the CVI tends to be associated with a future economic recession and an increase in unemployment. Our findings also suggest

> 1 See World Bank (2016), IMF (2015), IIF (2015), IS (2015) and Geneva Report (2015).

> 2 The seven indicators are: Interest Coverage Ratio (ICR), Leverage ratio, Net debt to EBIT ratio, Current liabilities to Long-term liabilities ratio, Quick ratio, Return on Assets (ROA), and Market to Book ratio.

2
# Source: LocalBitcoins.com API

LocalBitcoins.com does charge a commission (averaging 1%) on both buy and sell trades, but this is generally paid directly by the market maker and does not affect the Bitcoin trade quantity or price reported, and therefore does not interfere with our matching algorithm.<sup>14</sup> With this preamble, we now turn to the algorithm we use to identify crypto vehicle trades. Taking into account the large size of the data set, even with trade sizes documented out to eight digits, there is still a possibility of two identical-sized trades randomly appearing close together, especially as some trade sizes appear somewhat more frequently than others. The goal of our identification methodology is to arrive at an algorithm that identifies crypto vehicle transactions (both domestic and international) with a 95% confidence level. We aim to estimate the probability that two matching trades represent a crypto-currency vehicle trade, if they occur within, say, a five-hour window. The choice of window gives rise to the usual Type I and Type II errors trade-off. The shorter the window, the more currency vehicle trades we miss, the longer the window the more likely we are counting a random reoccurrence of an eight-digit match as and “in and out” vehicle trade. Our main results will turn out to be quite robust to the window choice; this is in part because the probabilistic approach directly controls for changes in the time window.

Our algorithm is constructed to generate an unbiased estimate of the share of trades on LocalBitcoins that are Crypto Vehicle Trades, while controlling for potential false discoveries Let _S_ be the set of all _I_ individual trades in our dataset, _i_ , each of which has a trade-size, _xi_ . Distinct trade sizes, denoted by , are assumed be an element of kk xx (1) 1 }, KK XX= {xx , ... , xx

> 14 The 1% transaction fee (2% to both buy and sell) makes LocalBitcoins.com a relatively unattractive vehicle for arbitrageurs, and in any event they would most likely be larger market makers whose trades our algorithm misses anyway because sizes would no longer match because of fees.

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**Table 32. Reactions to political risks: Government breach of contract**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0801|-0.0550|||-0.103|-0.0667|||
||(0.0788)|(0.0797)|||(0.108)|(0.114)|||
|Export % of sales|5.66e-05|0.00103|6.50e-05|0.000962|5.49e-05|0.00152|0.000190|0.00155|
||(0.00103)|(0.00108)|(0.00105)|(0.00108)|(0.00139)|(0.00154)|(0.00144)|(0.00158)|
|Share of sourcing via imports|0.000149|0.00150|0.000365|0.00170|0.000217|0.00215|0.000533|0.00264|
||(0.00112)|(0.00112)|(0.00116)|(0.00115)|(0.00152)|(0.00159)|(0.00162)|(0.00167)|
|High-income source country|-0.0350|-0.0176|-0.0178|-0.0212|-0.0442|-0.0218|-0.0185|-0.0370|
||(0.105)|(0.104)|(0.105)|(0.104)|(0.136)|(0.142)|(0.139)|(0.145)|
|>250 employees|0.132|0.101|0.118|0.0822|0.174|0.135|0.144|0.105|
||(0.100)|(0.0990)|(0.0984)|(0.0968)|(0.140)|(0.143)|(0.139)|(0.142)|
|Cumulative inv. >US$10M|-0.0110|-0.0251|0.0126|-0.0139|-0.0248|-0.0321|0.0250|-0.0134|
||(0.0971)|(0.0965)|(0.0996)|(0.0977)|(0.138)|(0.140)|(0.145)|(0.146)|
|Years in country|-0.00499**|-0.00524**|-0.00496**|-0.00500**|-0.00618*|-0.00666**|-0.00656**|-0.00692**|
||(0.00253)|(0.00252)|(0.00251)|(0.00250)|(0.00321)|(0.00335)|(0.00326)|(0.00342)|
|Level of foreign ownership (%)|-0.000871|-0.000254|-0.000614|-0.000224|-0.00113|-0.000551|-0.000884|-0.000483|
||(0.00157)|(0.00169)|(0.00157)|(0.00165)|(0.00205)|(0.00232)|(0.00213)|(0.00234)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.005|0.069|0.028|0.091|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Breach of contract by the government]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

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