rafmacalaba/datause-encoder-data huggingface dataset:
# **Timeliness and Contract Enforceability in Intermediate Goods Trade**<sup>**1**</sup>

Elisa Gamberoni (The World Bank) Rainer Lanz (Organisation for Economic Co-operation and Development, OECD) Roberta Piermartini (World Trade Organization, WTO)

**Keywords** : aid for trade, trade facilitation, offshoring, export times, quality of infrastructure, quality of institutions, comparative advantage.

**JEL Classifications** : F13, F14, L60.

1 This paper represents the opinions of the authors, and is the product of professional research. It does not necessarily reflect the position or opinions of the WTO or its Members, or the OECD or OECD Member countries, or the World Bank or those of the Executive Directors of The World Bank or the governments they represent, nor the official position of any staff members. We would like to thank Joelle Latina for her assistance, Richard Newfarmer and the participants at the European Trade Study Group (ETSG) conference for their comments. Any remaining errors are the responsibility of the authors. Rainer Lanz, Trade Policy Linkages and Services Division, OECD Trade and Agriculture Directorate, Tel: +33 145 24 81 73, Email: rainer.lanz@oecd.org , 2, rue AndréPascal, 75775 Paris Cedex 16, France. Roberta Piermartini, Economic Research and Statistics Division, WTO, Tel. 0041 22 7395492, Email: roberta.piermartini@wto.org, 154 Rue de Lausanne, Geneva CH-1211, Switzerland.
Yet, it is evident that connections are rarely, if ever, simply binary in nature. Their network properties tend to be important. Indeed, the ubiquity of networks in the social and economic life of humans is now well understood. Networks summarize sets of relationships between agents, often with particular structures. As such, they tend to shape or reflect specific opportunities that link individuals, companies and other entities, such as political parties. Available evidence indicates that network structures vary significantly - some have a large number of ties or edges while others have higher centrality secured by bridges through which those ties or edges pass. Some networks may be more hierarchical than others, maintaining more edges and higher centrality than so-called distributed networks. Dense networks, defined by high clustering and a low number of edges, tend to generate bonding capital that can be useful in leveraging valuable assets or connections and hence in ensuring cooperation.<sup>2</sup> By contrast, more diffuse networks with low clustering and many edges (high degree) tend to be more suited to providing information or access to information; bridging capital, in other words (Granovetter, 1973).<sup>3</sup> A network may also have small world properties where locally dense clusters of nodes are connected to many others through a small number of bridging connections (Humphries and Gurney, 2008). For political connections with firms, dense networks aimed at maximizing cooperation with peer pressure might be expected to predominate.

Despite their evident relevance, curiously little attention has been paid to mapping the format and nature of connections between firms, politicians and political parties and to understanding how different types of networks may affect outcomes, including the benefits. Our paper uses a unique dataset that documents politically exposed persons (PEPs) and – – their links to firms private and state-owned as well as to political parties and other individuals. We also are able to match these individuals through ownership and shareholder status to firm level balance sheet and performance data. Not only does this particularly rich dataset document political connections across countries, but it also allows identification of the types of connections that link individuals, parties and companies at a country level. As such, our main objectives are to understand how connections are actually configured, while also analyzing how different types of connections may have an impact on firms. The existing literature has concentrated on identifying political connections in a binary manner and then exploring that association – sometimes in a causal way – with outcomes. What is particularly original in our paper is that connections are analyzed for their network properties, including the type and location.

The paper is organized as follows. Section 2 reviews the main findings of the large

> 2Dense networks can be composed of few agents closely linked or many agents with small average distances between them.

> 3Bonding and bridging capital are terms employed by Putnam (2000).

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# **Figure 2. Total Debt**

<!-- Start of picture text --> Percent of GDP<br>320 World Advanced economies EMDEs<br>280<br>240<br>200<br>160<br>120<br>80<br>40<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> Sources: Kose et al. (2017, 2020); Mbaye, Moreno-Badia, and Chae (2018).

Note: Includes government and private debt. Data are available until 2020 for up to 191 countries. Nominal GDP weighted averages.

# **Figure 3. Ten-year government bond yields**

<!-- Start of picture text --> Percent United States Germany<br>Japan China<br>20 Brazil Russian Federation<br>Mexico<br>15<br>10<br>5<br>0<br>-5<br>Source: Bloomberg.<br>Jan-70 Sep-74 May-79 Jan-84 Sep-88 May-93 Jan-98 Sep-02 May-07 Jan-12 Sep-16 May-21<br><!-- End of picture text --> 22
So for example, a risk factor variable that takes on three values would be assigned a 1 for the worst situation (for example, household abuse), a 0 for the best situation (for example, no household abuse), and a 0.5 for intermediate (for example, the threat of household abuse).

# **Summary Statistics: Characterizing Chilean and Mexican Youth**

A sizeable share of Chilean and Mexican youth engage in a range of negative behaviors. In both countries, a significant percentage of youth are dropping out of school early and not working after age 18. Nearly 30 percent of Mexican and 14 percent of Chilean youth in the samples dropped out of school prior to completing high school (Table 2). While about 30 percent of Mexican and Chilean youth are inactive after age 18, 12.8 percent of Mexican and 6.7 percent Chilean youth are inactive before age 18. Chileans have earlier, and more risky, sex than Mexicans. While only 13 percent of Mexicans age 15 to 17 report having had their first sexual experience, 27 percent of Chileans in this age group report the same. Half of the sexually active Mexicans use contraception; Chileans are 12 percentage points less likely to use condoms than are Mexicans. 2.5 percent of Mexicans age 15-17 have had their first child while nearly 4 percent of Chileans in the same age group are parents (Table 2). Nearly 30 percent of Mexican youth and 80 percent of Chilean youth report having ever been involved in activities outside of school; this percentage decreases with age. Finally, only 3.4 percent of the sample of youth aged 12-24 in Mexico can justify getting drunk and 1 percent justify drug use.

Many of these behaviors by Mexican and Chilean youth co-occur, as is also reported by young people interviewed across Latin America (IDDI 2006, Dasso 2006, Weiss 2006, World Bank 2006b) and found in empirical work in the US (Bartlett et. al. 2005), Caribbean (World Bank 2003), and Brazil (World Bank 2007). For example, inactive youth disproportionately leave school before completing their secondary education as demonstrated by the high correlation between not dropping out early and not being inactive (0.42 in Mexico and 0.38 in Chile). Risky and early sexual behaviors are also positively correlated with youth inactivity (0.21 in Mexico and 0.30 in Chile), 10
73 private sector has taken over the responsibility for about a third of the production of such services (Kin et al., 2002). (However, some assets (medical facilities) are still owned by public-sector authorities, and rented to private agents.) In itself, this shift to private producers is not necessarily a problem. As in the case of education, and more so, the real problem is rather that the public sector has reduced and decentralized its responsibility for the _financing_ of these services. Only about 125 million individuals in urban areas seem to have comprehensive health-care insurance today,<sup>144</sup> and perhaps about the same number participate in modest cooperative health plans in rural areas (Chow, 2006b). This is the background of the earlier mentioned fact that private financing in China covers about 60 percent of the total health costs, usually taking the form of pocket money. The central government has, however, announced ambitions to expand the coverage both in urban and rural areas.

In spite of the weaknesses of health services, there has been a rapid increase in total health spending – today amounting to 5.3 percent of GDP, which is 2-3 percentage points higher than in countries with a similar level of per capita income in Southeast Asia (except for Vietnam). The apparent paradox of increased spending and stagnating – and in some respects even deteriorating – health services suggests inefficiencies of health care (presumably reflecting deficiencies both in the organization and in the incentive structure). An additional indicator of inefficiencies is frequent reports of moral hazard in health care, for instance, in the form of excessive health investigations of patients (Chow, 2006b) – a rather common problem in a number of countries, in particular when health care is financed by insurance.

A more specific indicator of inefficiency in the Chinese health sector is that about 68 percent of government funding was recently reported to have gone to hospitals rather than health clinics and preventive health, in spite of the fact that many experts regard the latter activities as potentially more important (on the margin) for the overall health situation (UNDP, 2000, p. 3).<sup>145</sup> The inefficiencies in health care are also a result of the uneven

> 144 Blumenthal and Hsiao (2005) report that the share of employees in urban areas with health insurance is 55 percent (in 2003) – a fall from 77 percent in 1993, largely a result of reduced employment in state firms and a rise in private and informal employment (UNDP, 2005, p. 65). In particularly poor areas of the country, many private doctors charge service fees simply because local governments often cannot afford to finance the services.

> 145 Of China’s total health expenditure (in 2002), 50 percent is reported to have been allotted to urban hospitals, and only 7 percent to health centers. It also appears that only about 7 percent was devoted to “public health”
investigate the interest-related side of the business, where NIM is defined as net interest income as a fraction of total assets. We further investigate NIM by dissecting this variable into its two drivers – Interest Income to Assets and Interest Costs to Assets.

Second, we explore _efficiency_ variables. To capture bank efficiency we use the ratios of total Overhead Costs to Assets, and Personnel Costs to Assets. Further, we break the two cost ratios between the underlying quantities and prices by computing the number of Employees per unit of Asset and the related average wages.

Third, we study _asset allocation_ to understand how banks allocate their resources between lending and non-lending activities. We use the Securities to Assets ratio which encapsulates many types of securities but is mostly driven by government securities. As such this ratio also measures private sector crowding out effects to some extent.

Fourth, we examine _asset quality_ and its impact on profitability. Aggregate country level data show that countries that have a large share of state banks also have a high ratio of NPLs to total loans (Figure 6). Ideally, we would use this indicator as a measure of asset quality, but we could not obtain sufficient bank level data on non-performing loans (NPLs), especially for state banks, due to deficiencies in financial disclosure. In order to capture differences in asset quality and its impact on profitability we use the ratio of loan-loss provisions to gross loans. This indicator captures the extent to which banks’ loan portfolios are being contaminated by non-performing loans and having an adverse impact on profitability.

Our explanatory variables include ownership variables which we compiled by using a variety of sources including Bankscope, Bankers’ Almanac and individual bank websites. We classify equity holders as being either public or private, or domestic or foreign). Our public ownership dummy assumes a value of 1 if the bank is majority government-owned and 0 otherwise. Similarly, our foreign ownership dummy has a value 1 if the private bank is majority foreignowned and 0 otherwise. We do not differentiate between foreign banks (i.e. regional versus international banks) because of small samples. By conducting the banks’ and exchange website searches, we also created a listed dummy variable that assumes a value of 1 if the bank is listed on a stock exchange and 0 otherwise.

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**Table 6.** ITT effects on different knowledge outcomes, FREG- and non-FREG samples separately.

|||Overall k<br>|nowledge<br>|||Principles<br>|knowledge<br>|||How-to k<br>|nowledge<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Non-FRE|G sample|FREG|sample|Non-FR|EG sample|FREG|sample|Non-FRE|G sample|FREG|sample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|T1 (extension)|0.013|0.014|0.129***|0.124***|-0.004|0.001|0.089***|0.079***|0.027**|0.027**|0.178***|0.177***|
||(0.014)|(0.013)|(0.025)|(0.021)|(0.017)|(0.016)|(0.029)|(0.026)|(0.013)|(0.012)|(0.027)|(0.024)|
|p-value|0.351|0.284|0.000|0.000|0.803|0.974|0.003|0.002|0.049|0.025|0.000|0.000|
|T2 (extension + video)|0.049***|0.049***|0.149***|0.152***|0.035**|0.042***|0.129***|0.126***|0.052***|0.052***|0.160***|0.166***|
||(0.013)|(0.012)|(0.021)|(0.020)|(0.016)|(0.014)|(0.027)|(0.024)|(0.014)|(0.013)|(0.020)|(0.019)|
|p-value|0.000|0.000|0.000|0.000|0.030|0.004|0.000|0.000|0.000|0.000|0.000|0.000|
|Test T1 >= T2 (p-value)|0.006|0.005|0.201|0.099|0.014|0.006|0.095|0.049|0.048|0.046|0.750|0.689|
|Endline control mean|0.4|64|0.5|10|0.|541|0.5|78|0.3|97|0.|440|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-squared|0.013|0.136|0.148|0.252|0.005|0.102|0.071|0.178|0.017|0.134|0.203|0.307|
|Observations|1,540|1,540|390|390|1,540|1,540|390|390|1,540|1,540|390|390|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ OLS regressions of household heads' knowledge scores, ranging from 0 to 1, and calculated based on the number of correct answers relative to the total number of questions in a respective domain. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and their matched controls. Additional control variables identical to those listed in notes of Table 5. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.
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public finance initiative (PFI) projects involve benchmarking to assess the value for money (NAO, 2001).<sup>14</sup> Another may be PPP contracts with some risks transferred back to the public sector. Payments to government may be largely fixed in particular in concessions. In many PPP transactions, however, governments attempt to mitigate some project risks, especially political, regulatory and macroeconomic risks that are difficult for individual firms to avoid, by providing minimum revenue guarantees or subsidies. Governments can offer a long-term take-or-pay contract for bulk supply facilities to incentivize operators to make continuous efforts (see also Proposition 15 below). The minimum traffic or revenue guarantees also have the partially similar effect to cost-plus arrangements in the sense that incentives for contractors are partly compromised. Regardless of (unobserved) efforts, revenue will be guaranteed.

Apart from auction theory, there are at least two alternative methods of awarding public contracts: (i) direct negotiation and (ii) beauty contest. Milgrom (1985) compares auctions with direct negotiation and explains why the former is more popular than the latter. Note that negotiation is essentially characterized by a bargaining game where the government negotiates one-by-one with a series of sellers that make short-lived offers.15 Among other things, auctions can be the best way to procure even when the government is in a relatively weak bargaining position relative to the potential suppliers and the awarded contract can be resold. To understand why, consider this simple example adapted from Milgrom’s work. Consider a buyer in a weak bargaining position vis-à-vis two potential sellers, firms A and B. Firm A can produce the item being bought at the lowest cost. If the government conducts an auction with a ceiling high enough to ensure that both firms participate, it can expect to buy the good from firm A at approximately the same price that firm B would obtain if it were to

> 14 Though, the usage of such a mechanism has been limited thus far. Only eight of the 54 public procurement authorities have actually made use of it.

> 15 Short-lived offers make it difficult or impossible for the buyer to compare offers. Milgrom (1985) actually focused on a situation in which a seller was dealing with several buyers; the reinterpretation in terms of our context is straightforward.
Rambaldi, A., Rao, D S P., and K R Ganegodage (2010), “Modelling Spatially Correlated Error Structures in the Time-Space Extrapolation of Purchasing Power Parities”, in L. Biggeri and G. Ferrari (eds.), _Price Indexes in Time and Space: Methods and Practice,_ Springer-Verlag, Berlin, 63-96.

Rao, D.S.P., A. Rambaldi and H. Doran, (2015), “Extrapolation of Purchasing Power Parities Using Multiple Benchmarks and Auxiliary Information: a New Approach”, _Review of Income and Wealth_ , 56, Special Issue, S59-S98.

Ravallion, M. (2013a), “Price Levels and Economic Growth: Making Sense of Revisions to Data on Real Incomes”, _Review of Income and Wealth_ , 59(4), 593-613.

Ravallion, M. (2013b), “Reply to Robert Inklaar”, _Review of Income and Wealth_ , 59 (4), 623628.

Ravallion, M. (2014), “ _An Exploration of the International Comparison Program's New Global Economic Landscape_ ”, NBER Working Paper No. 20338, A copy of this paper is available on http://www.nber.org/papers/w20338.pdf.

Reddy, S. and T. Pogge (2007), “How Not to Count the Poor,” in S. Anand and J. Stiglitz (eds.), _Measuring Global Poverty_ , Oxford University Press, Oxford, 2007.

Rogoff, K. (1996), “The Purchasing Power Parity Puzzle”, _Journal of Economic Literature_ , 34(2), 647-668.

Samuelson, P. (1964), “Theoretical Notes on Trade Problems”, _Review of Economics and Statistics_ , 46(2), 145-154.

World Bank (2008), _Global Purchasing Power Parities and Real Expenditures: 2005 International Comparison Program_ , World Bank, Washington DC.

World Bank (2013), _Measuring the Real Size of the World Economy: The Framework, Methodology, and Results of the International Comparison Program—ICP_ , World Bank, Washington DC.

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Table 2: Estimates of the wage ratios of observably equivalent workers (male, urban, 35 years old) comparing late arrivers working in the US versus their country of birth at various levels of schooling

|Column|I|II|III|IV|V|VI|
|---|---|---|---|---|---|---|
|Specification<br>Schooling|Category<br>9-12|Category<br>5-8|Category<br>13-16|Mincer<br>Geom.<br>Avg. 9-12|Raw wage<br>ratios, no<br>controls|Annual<br>dollar gain in<br>column I|
|**Average**|**4.36**|**4.86**|**4.15**|<br>**4.06**|**7.27**|**$14,999**|
|**Median**|**3.93**|**3.87**|**3.05**|**3.44**|**6.20**|**$15,339**|
|**Correlation with col. I**|**1.00**|**0.92**|**0.92**|**0.99**|**0.82**|**―**|
|Nigeria|13.59|11.97|12.63|11.72|13.45|$18,068|
|<br>Haiti|11.11|13.76|8.38|10.81|23.50|$18,459|
|Egypt|9.98|12.60|13.29|8.77|11.98|$19,788|
|Yemen|8.89|9.03|10.77|8.71|14.07|$18,344|
|Ghana|8.37|9.21|7.24|7.45|9.37|$21,053|
|Sierra Leone|7.61|8.80|4.98|7.10|8.35|$19,436|
|Indonesia|5.82|7.52|5.35|6.09|9.52|$17,971|
|Cameroon|6.28|6.88|5.78|5.91|10.06|$18,883|
|Vietnam|5.54|4.66|6.13|5.84|10.29|$16,994|
|Venezuela|4.86|5.51|5.07|4.91|8.89|$16,337|
|Pakistan|5.18|4.40|5.61|4.69|12.58|$15,381|
|India|5.21|5.12|5.96|4.46|10.88|$16,827|
|Bangladesh|4.92|4.12|3.69|4.45|6.89|$15,031|
|Ethiopia|4.08|8.65|2.73|4.30|13.08|$17,308|
|Ecuador (median)|3.87|4.63|3.56|4.15|7.29|$14,520|
|Jordan|4.82|5.89|4.15|4.10|6.21|$17,643|
|Cambodia|4.72|3.04|6.92|4.05|9.16|$18,547|
|Sri Lanka|4.98|3.99|4.21|4.04|9.90|$17,446|
|Bolivia (median)|3.98|4.52|4.07|3.97|5.78|$16,458|
|<br>Uganda|4.67|8.89|3.02|3.96|7.71|$17,925|
|<br>Philippines|3.99|5.74|3.09|3.80|6.18|$16,882|
|<br>Nepal|4.59|4.52|8.13|3.08|13.48|$14,846|
|<br>Guyana|2.94|2.58|3.19|2.92|4.81|$16,537|
|<br>Brazil|2.99|3.75|2.21|2.91|5.03|$15,193|
|Chile|2.85|3.31|2.36|2.79|3.09|$15,297|
|Panama|2.94|2.71|2.87|2.77|3.87|$15,084|
|Jamaica|2.97|3.64|2.48|2.76|3.20|$16,881|
|Peru|3.11|3.44|2.38|2.73|4.43|$14,248|
|Thailand|2.37|3.22|1.84|2.50|4.79|$12,992|
|Turkey|2.16|2.60|2.17|2.46|3.15|$11,814|
|<br>Uruguay|2.22|2.78|2.05|2.23|3.22|$14,307|
|<br>Colombia|2.13|2.66|1.93|2.16|4.42|$10,505|
|Guatemala|2.09|2.47|1.87|2.14|6.25|$10,782|
|Nicaragua|2.42|2.25|1.88|2.13|4.96|$11447|
|<br>Morocco|<br>2.00|1.25|2.01|2.00|<br>3.48|,<br>$10009|
|<br>Mexico|<br>1.99|2.45|1.60|1.94|<br>3.82|,<br>$9,180|
|South Africa|2.16|2.80|1.40|1.79|2.83|$13,939|
|Argentina|2.13|2.38|1.85|1.74|2.40|$12,420|
|<br>Belize|1.53|1.79|1.38|1.69|3.58|$8,597|
|Paraguay|2.00|1.52|1.31|1.64|3.20|$14,024|
|Costa Rica|1.56|1.74|1.55|1.61|2.85|$7,548|
|Dominican Rep.|1.37|1.48|1.38|1.36|3.32|$4,991|

Sorted in descending order by column IV, for comparability with Table 3. “No controls” means that the regression includes only country and wage-unit dummies. “Mincer” is standard Mincer specification. “Category” uses dummies for five education levels and nine age levels. Both specifications include interaction terms allowing different returns to schooling acquired abroad. The numerator is the predicted wage for “US-born” (US-born US-residents), and the denominator is predicted wage for “Foreign-born, late arrival” (foreign-born US-residents who arrived at age 20+).

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