rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

Since the Uruguay Round Agreement in 1994, global value chains (GVCs) in the agrifood sector have expanded significantly. Driven by regulatory changes, technological advancements, increased foreign direct investment (FDI), and shifting consumption patterns, these chains involve multiple firms across international locations, each specializing in specific tasks (Gereffi et al., 2005). Major corporations often orchestrate these networks, leveraging global differences in production costs and technological expertise. This model has thrived under policies aimed at reducing trade and investment frictions, illustrating the profound impact of globalization on production and trade, with about half of global trade nowadays consisting of products traversing multiple borders from start to finish (WorldBank, 2020; World Trade Organization, 2023).

However, anecdotal evidence suggests that recent events, including the US-China Trade War, the COVID-19 pandemic, the Suez Canal blockage, the invasion of Ukraine, and severe weather events pose potential major disruptions (Antràs, 2020; Garicano et al., 2022; Alfaro and Chor, 2023). These challenges have already started reshaping GVCs, including in agri-food, altering the significance of different players within the industry. For instance, during the COVID-19 crisis, many intermediaries in the fresh produce value chains exited the market, while large growers, manufacturers, and supermarket chains gained prominence. Firms that had previously diversified geographically and invested in creating backward and forward linkages with other critical industries (e.g. by creating e-commerce sales channels or investing in logistics adjustments and technological solutions) demonstrated greater resilience and adaptability.

These observations suggest the need for a detailed analysis of value chains, the role of large corporations within them, and most importantly, an exploration into how different types of firm-to-firm linkages may either expose some firms to fatal shocks or enable others to diversify and enhance their resilience. To investigate the aforementioned questions, we utilize the FactSet database along with the list of agri-food-related US Fortune 1
1 of table 3. Figure 4 illustrates the same, but important conclusion: a large self-employed sector is not _ipso facto_ evidence of distortions, but that the opportunity cost of self-employment is lower in poorer countries.

The relative youth of Latin America's population also explains much of the variance with the spread across the sample accounting for 8.8% of the higher share in self-employment. The education variable that measures the degree to which firms must bear the burden of financing overall education would account for roughly 7.5 percentage point difference. Real interest rates are also surprisingly important. Peru's very high self-employed sector (46%) and high real interest rates (67%) are clearly dominating the relationship although even dropping this outlier yields a significant coefficient. The difference between real irnterest rates of under 5% as in the OECD vs often above 30% in Latin America is worth over 6%. Once again, the importance of ensuring macro-economic stability, and reducing risk to bringr down interest rates seems clear.

In general, these effects dwarf the impact of any of the three labor market variables. Across the range observed, social security taxes on employers explain relatively little of the size of the sector, 3.2 percentage points in the share of self-employment.

The same exercise was repeated with the smaller sample arising from using the Marquez protection index, Ic-i e. With fewer observations, the apparent collinearity of the productivity variable and education variables makes identifying the "correct" parsimonious form difficult (Idle). However, in all cases, the employment protection index has the impact of increasing the size of the self-employed sector. But, again, the difference between the highly protective Bolivia, Honduras, Mexico, Peru or Spain compared to the unprotective US or UK is worth only about 1.35 percentage points in the share of self-employment, a fairly small impact. The results are 17
14 a consistent set of values for reducing health risks. The results are summarized in Figure 2, which provides an overview of the distribution of net benefits of 54 final regulations. The left side of the figure shows the number of rules with net costs that fall in various categories. The right side of the figure shows the number of rules with net benefits that fall in various categories. The figure illustrates that average benefit for a rule with net benefits exceeds the average cost for a rule with net costs.

Several conclusions emerge from his analysis. First, using government agency data, it would appear that there is a present value of about US$280 billion (1994 dollars) in net benefits to government regulation in those areas since 1990. Yet over half the final rules would not pass a benefit-cost test, even when we use government agencies' numbers. Aggregate net benefits are positive because many of the rules that do pass have substantial benefits. Eliminating final rules that would not pass a benefit-cost test could increase the present value of net benefits by more than US$115 billion.

There are reasons, however, _not_ to take the agency numbers at face value. Both theory and empirical evidence suggest that agencies are likely to overstate substantially the aggregate numbers for net benefits. Agencies with a single objective (e.g., protecting the environment improving safety in the workplace) have an incentive to overstate the benefits of their program relative to the costs so that they can better meet the demands of interest groups.

Another measure of the impact of regulations is how many lives a regulation is likely to save. Interestingly, a review of several final and proposed regulations reveals the amount spent for each premature death that would be avoided because of the existence of the regulation varies over eight orders of magnitude - from roughly US$100,000 to over US$5 trillion (1990 dollars) (Morrall, 1986)! This suggests that regulations could be developed that would prevent many more premature deaths while still saving consumers
**3. Descriptive Analysis of Overall Urbanization Patterns**

# **3.1.** **_Aggregate differences across countries_**

Table 1 presents summary statistics that describe key urbanization and other characteristics across SSA countries, while table 2 identifies the countries that have the three largest and smallest values for each variable presented in table 1. Before commenting on the urbanization characteristics, it is worth noting that SSA countries vary considerably in population, area, and average (overall) population density. Hence, population at the 90<sup>th</sup> percentile of the distribution across countries is 46.8 times that at the 10<sup>th</sup> percentile, while the corresponding ratios for land area and population density are 273.5 and 22.4 (table 1). The particularly large variation for area reflects the fact that SSA consists of both geographically

> 16 The 14 countries are: Botswana, Cabo Verde, Chad, Eritrea, Ethiopia, Kenya, Mali, Mauritania, Mozambique, Namibia, Niger, Somalia, South Africa, and Eswatini.

> 17 We use the “UN adjusted” version of this data in which the national population counts from summing across grid cells match national population counts as reported by the United Nations.

> 18 See https://ui.adsabs.harvard.edu/abs/2019AGUFMIN11D0688H/abstract.

> 19 http://due.esrin.esa.int/page_globcover.php.

> 20 See https://icesat.gsfc.nasa.gov/icesat/tools/SRTM30_Documentation.html for more information.

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**

# _II.1 Household survey and agricultural census_

The two data sources used are: The Vietnam Household Living Standard Survey (VHLSS) for 2006 and the 50 percent sample of the Rural Agriculture and Fishery Census (ARFC) for 2006. Both data sets have been collected by the General Statistic Office of Vietnam (GSO).

The VHLSS 2006 includes 9189 households (with 39071 individuals), of which 2250 are urban and 6939 rural households. The collected information on household characteristics includes: income, expenditure, employment status, education level, housing condition, fixed assets owned by household. The survey is designed to be representative at the regional level. This means that the survey is not able to guarantee consistent poverty estimates at lower levels of aggregation (such as at the province level).

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|WB PinkSheet|1960-2016|||X|||
|Natural Gas|0.89|6|0.53|IFS/BJS(2013)|1985–2008|||||X|
|Copper|0.89|6|0.53|IFS/BJS(2013)|1957–2008|||X|||
|Gold|0.89|6|0.53|IFS/BJS(2013)|1970–2008|||||X|
|Cofee (Robusta)|0.89|6|0.53|WB PinkSheet|1960-2016|||||X|
|Soy Beans|0.87|5|0.48|WB PinkSheet|1960-2016||X||||
|Bananas|0.80|3|0.35|WB PinkSheet|1960-2016|X|||||
|Cofee (Arabica)|0.77|2.6|0.31|WB PinkSheet|1960-2016||||X||
|Sugar|0.74|2.3|0.28|IFS/BJS(2013)|-||||X||

> 36The estimates in Table 7 do not include a time trend, because (if taken literally) this would imply that _real_ log prices would go to _∞_ or zero in the (very) long run, neither of which are feasible.

> 37This is unsurprising as Cashin _et al_ (2000) argue that least squares estimates of persistence are downward biased.
headquarters (Budd et al. 2005); thirdly, the wage premium can be a compensation for higher labor demand volatility and lower job security (Fabbri et al. 2003, Bernard and Sjöholm 2003).

Individuals might be motivated to improve their educational credentials to join foreign firms. Foreign firms indeed are more likely to hire individuals with higher educational attainment. After controlling for gender, age and age squared, marital status, ethnic minority dummy, industry and city fixed effects, the probability of being employed by a foreign firm is 2.6 percentage points higher for college graduates, and 5.1 percentage points higher for people with at least a high school diploma.

To see whether there is a foreign wage premium in China and whether it is higher for skilled workers, we use the 2005 mini census data to run a simple cross-section regression below: lnincomepic = αForeignpic + βSkilledpic + γForeign× Skilledpic + Xpic′ ρ+ μi + θc + εpic (4) lnincomepic represents monthly income in RMB yuan of person p in industry i, city c in natural logarithm. Foreign is a dummy variable that takes the value of 1 if the person is employed in a foreign25
decisions for the family—for example, as the head of household—his or her departure may compel another member, such as the spouse or parents, to assume this responsibility. This, in turn,

> 14 The 1998 KMS data show that 38.1 percent of households with international migrants had no member earning wages in Kerala, compared with 20 percent of households with domestic migrants.

> 15 A confounding issue could be the _anticipation_ of declining remittances over time, as a domestic migrant establishes stronger roots in his or her new domicile. This may lead the recipient household to treat current remittances as temporary flows, therefore raising their propensity to invest it early on.

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benefits exist and are often generous. Its exclusion as an alternative to self-employment may bias results. For OECD countries with traditional unemployment benefits, the variable takes the value of the duration of unemployment benefits which Nickell (1997) found this to be the most important variable for explaining levels of unemployment. For Latin America and other countries, we calculate the standard severance pay package given the mean tenure (or predicted if unavailable).

Youth (Youth): We include one demographic measure as well, the share of the working population found between the ages of 16 and 20. The model implicitly assumes homogeneous work forces across countries. This is clearly not true as the share of young workers is much higher in Latin America than in the OECD countries. This variable is most relevant to the turnover regressions where traditionally young workers have higher rates of turnover as they shop around for careers. But it may also have a similar interpretation in the self-employment regressions.

Latin Dummy (LA): We include a dummy for being from the Latin American region. In theory, this may capture any difference between OECD economies and the region, including labor legislation."<sup>5</sup> Ideally, we would like to eliminate the significance of this variable by including the labor market variables that it may be proxying for.

# **_3.2 Results_**

The results must be interpreted with caution. First, we have at best 40 observations and in our most courageous moments, only 17. This in some cases can make the results sensitive to the countries included. Second, the data are not uniform. M[ost of the OECD variables were gleaned from presumably consistent publications of that organization while the Latin variables were individually extracted from not necessarily consistent survey data. The LAC dummy may pick up these data discrepancies.

Despite these potential pit-falls, the regressions prove surprisingly robust and consistent _15_ We included a squared income term as well but it was never significant. This does not, however preclude more exotic non-linear
most likely supplied from an electrical grid. It assumes that small off-grid systems do not emit enough light to be captured by satellites but that larger isolated power networks do. We crosschecked the numbers obtained with country estimates of electrified population from the World Bank.<sup>6</sup> Figure A2 in the Appendix shows the share of the population with access to electricity.

We also collected information on transmission grids based on past efforts to harmonize data for infrastructure from primary sources and recent mapping strategies to infer the electricity grids based on satellite data. For past data, we used electricity grids from the Africa Infrastructure Country Diagnostic (AICD), which collected primary data covering network service infrastructure from 2001 to 2006 in 24 African countries (Foster and Briceno-Garmendia, 2010). To complement these data, we relied on a recent effort by the World Bank; Facebook; and other institutions (the KTH Royal Institute of Technology, the Energy Sector Management Assistance Program [ESMAP], World Resources Institute, and the University of Massachusetts Amherst) to use remote sensing, machine learning, and big data to map connected populations and the systems that support them. This group created an algorithm for estimating the location of medium-voltage infrastructure based on nighttime lights and the location of roads assuming that medium-voltage lines are more likely to follow (or be followed by) main roads.<sup>7</sup> Figure A22 in the Appendix shows the grid for the Lake Chad region using the AICD grid and using the most recent grid.

Internet infrastructure is proxied by access to the fiber broadband backbone network. We obtained data for all Africa for 2009–20 from Africa Bandwidth Maps, which provides the exact location of fiber nodes along the backbone network. We constructed a proxy for access to the fiber backbone that is equal to one if there is a node of the backbone in the location of interest. Each node has a year attribute, which allows us to build a panel for access to the backbone. We assume that access
POLICY RESEARCH WORKING PAPER 1939

# Summary findings

The literature on speculative attacks has been given new impetus by the collapse of the European currency arrangements beginning in 1992, by the Mexican peso crisis and after-effects in 1994, and most recently by speculative attacks across Asia.

One strand of this literature stresses the importance of imbalances in stocks of monetary and financial aggregates rather than traditional "flow" factors, arguing that massive, volatile capital flows have become a dominant feature of the global landscape, and that exchange-rate levels and current accounts have not proved convincing as proximate causes of crises.

Galindo and Maloney test two popular asset-based - models of speculative attacks Krugman and _-_ Rotemberg (1992) and Calvo and Mendoza (1995) especialLy their emphasis on the second moments of monetary aggregates.

Analyzing monthly panels of appropriate countries in three regions, they find evidence for the importance of money/reserve ratios predicted by both models, and their variance as predicted by Calvo and Mendoza.

But the variance of velocity does not appear to be important, casting some doubt on the KrugmanRotemberg target zone framework and the interpretation of the Calvo-Mendoza results.

- This paper a product of the Poverty and Economic Management Unit of the Latin America and the Caribbean Regionis part of a larger effort in the region to understand the determinants of macroeconomic instability. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Marta Cervantes, room I8-095, telephone 202-473-7794, fax 202-522-0054, Internet address mcervantes@worldbank.org. William Maloney may be contacted at wmaloney@worldbank.org. June 1998. (27 pages)

_The Policy Research Working Paper Series disseminates the findings of wore 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
This version: 15 Feb 2024 28 Standards of living are significantly better, but respondents feel trapped by the endless cycle of monthly instalments that must be paid to maintain such standards. This is similar to the detailed qualitative analysis of financial diaries conducted in Bangladesh, Ethiopia and India (Collins et al., 2009) and the United States (Morduch and Schneider, 2017). Luxuries of the past have become present-day necessities. Increased education levels and jobs diversification is muted by tight competition in a market saturated with university-educated job seekers. Infrastructure has improved, but not without negative impacts. Participants long for what their parents’ generation had - fewer needs, more immovable assets, and simpler pleasures of good food and clean air. These points are highlighted below in the voices of the Malaysians who shared their experiences in the FGDs.

Across regions, ethnicities and income classes, participants explained that their lives were not “in balance,” because incomes are not enough to pay for monthly expenses and maintain a certain standard of living, captured poignantly in the quote below.

_At month’s end, I feel like going insane because I never have enough to settle all basic payments. Need to pay the house mortgage, car loan, children’s education and needs, water bill, electricity bill...To survive, we take an advance salary to spend for the rest of the month and roll the expenses. Salary is not enough to meet the daily basic needs for today’s living standard._ [Multiple respondents, Indian, Selangor] 28
, there is probably an even higher share of Nigerians that have poor food access according to recommended guidelines.

Second, the share of households that have poor food access using the FIES is significantly higher than that of the FCS. Approximately 57.3 percent of Nigerians are moderately food insecure or worse according the FIES, and approximately 25.9 percent

> 23For example, the 2009/10 HNLSS data were marked by implausible seasonal movements in consumption and contradictions of Engel’s Law. These issues were eliminated in the 2018/19 NLSS. For further discussion of data issues in the 2009/10 HNLSS, see World Bank (2016).

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g. unemployment spells, there are no equivalent sources of censoring.

> 7One of the few data sets we are aware of that has panel spell data, the 1987 National Medical Expenditure Survey for the United States, includes only spells that lead to medical care use or work absenteeism. Gilleskie (1998) recognizes the selection problem but can only address it by estimating the probabilities of unobserved health events parametrically. With administrative data e.g. from insurance claims (Manning et al., 1987; Finkelstein et al., 2012), illness spells can only be studied indirectly, by grouping claims into treatment episodes, see Stoddart and Barer (1981); Hornbeck et al. (1985); Keeler et al. (1988); Santos Silva and Windmeijer (2001). A public health literature studies the covariates of the delay in seeking care for specific diseases (see Storla et al. (2008) and Nguyen et al. (2010) for reviews).

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more materialized migration plans, we compute two indices aggregating different variables for each family of outcomes following Kling, Liebman, and Katz (2007): (i) knowledge about migration process index and (ii) preparation to migrate index.<sup>22</sup>

> 22 The knowledge about the migration process index is constructed using these variables: don’t know where to look for information on work abroad (reverse coded); don’t know where to register for work abroad, if interested (reverse coded); don’t know which documents are required to work abroad (reverse coded); don’t know the risks associated with working abroad without all the necessary documents (reverse coded); know whether migrant friends/acquaintances faced any problem in the process of migration; don’t know what type of problem their friends/acquaintances  faced (reverse coded).

> Preparation to migrate index is constructed using these variables: completed a health test before migrating in the past; don’t know about health test required by the official migration process (reverse coded); completed a training before migrating in the past; don’t know about training required by the official migration process (reverse coded); don’t know the content of the training (reverse coded); participated in pre-departure sessions; don’t know about any pre-departure sessions required by the official migration process (reverse coded).

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c **0 c) -0** 2 i rn

**0** X **I**

2

# **Table 1. Degrees of Informality (continued)**

||Formal Sector<br>Enterprises|Taxicabs|Kiosks|Boot repair<br>("black market"<br>and street)|Selling used<br>parts/tools<br>(tech. mkt)|Selling<br>individual<br>cigarettes, soft-<br>drinks or<br>chewing gum<br>(street)|
|---|---|---|---|---|---|---|
|Percent making contributions to<br>health or social insurance from<br>informal income*|- 100%|51%|54%|37%|62%|24%|
|Percent covered by social<br>insurance*|_100%_|40%|34%|13%|18%|_5%_|
|Percent covered by health<br>insurance*|_-100%_|_51%_|_53%_|36%|61%|24%|
|Mem: Percent of participants with<br>vocational or higher education*|na|63%|60%|33%|46%|40%|
|Mem: Percentage Male/Female|na|95 /_5_|39 / 61|93 / 7|85 / 15|20 / 80|

_Source:_ Informal sector surveys conducted by the author.

**_*_** excludes moonlighters; items in italics are reasonable _a priori_ assumptions.