rafmacalaba/datause-encoder-data huggingface dataset:
specification estimates how exogenous changes in agricultural revenue affect the likelihood that a household consumes a particular food group. The second specification estimates how exogenous changes in agricultural revenue through production shocks affect the share of a food group a household consumes. In this version, the calorie consumption share from each food group are the dependent variables. We focus on the effects of agricultural revenue on the probability of food group consumption and the share of total calories by food group because only our previous agricultural revenue specification passed all instrumental variable tests.

The marginal effect estimates from the probit are presented in the top panel of Table 9. The marginal effect estimates suggest that higher agricultural revenue is associated with a higher probability that a household will consume tubers, fruits, vegetables, fish, and meat and poultry. However, only the specifications for vegetables and fish pass all three IV tests. The estimated effect for the other food groups must therefore be viewed with some caution. The results suggest that as agricultural revenue increases, large effects on diet composition are unlikely as the probability of eating any single food group increases with similar likelihood.

However, diet composition could also change with respect to the share of calories consumed from any single food group. The results from the food group calorie consumption shares are presented in the bottom panel of Table 9. The results suggest that increased agricultural revenue was associated with a greater share of tubers being consumed but a lower share of fish, beverages, and grains. However, only the tuber and beverage specifications pass all three IV tests. The estimates indicate that a 10 percent increase in agricultural revenue will result in the household consuming 5.2 percent more tuber calories and 5.9 percent fewer beverage calories as a share of total calories consumed. This could suggest that as households increase agricultural revenue, beverage consumption is replaced by healthier tuber consumption.

22
|P110693|2010|2019|6/13/2019|15 Active|Vietnam|
|---|---|---|---|---|---|
|P110693|2010|2020|12/12/2019|16 Active|Vietnam|
|P110693|2010|2020|6/25/2020|17 Active|Vietnam|
|P110722|2008|2010|8/2/2009|1 Closed|Djibouti|
|P110722|2008|2010|5/28/2010|2 Closed|Djibouti|
|P110722|2008|2012|2/17/2012|3 Closed|Djibouti|
|P110723|2012|2013|11/26/2012|3 Closed|Liberia|
|P110723|2012|2013|6/8/2013|4 Closed|Liberia|
|P110723|2012|2014|1/4/2014|5 Closed|Liberia|
|P110723|2012|2015|11/30/2014|6 Closed|Liberia|
|P110723|2012|2016|8/19/2015|7 Closed|Liberia|
|P110723|2012|2016|3/22/2016|8 Closed|Liberia|
|P110723|2012|2017|11/22/2016|9 Closed|Liberia|
|P110723|2012|2017|6/7/2017|10 Closed|Liberia|
|P110725|2008|2010|8/6/2009|1 Closed|Morocco|
|P110725|2008|2010|6/7/2010|2 Closed|Morocco|
|P110725|2008|2011|6/21/2011|3 Closed|Morocco|
|P110728|2008|2009||0 Closed|Cote d'Ivoire|
|P110728|2008|2010||0 Closed|Cote d'Ivoire|
|P110728|2008|2011|12/22/2010|7 Closed|Cote d'Ivoire|
|P110728|2008|2012|8/10/2011|8 Closed|Cote d'Ivoire|
|P110728|2008|2012|5/14/2012|9 Closed|Cote d'Ivoire|
|P110728|2008|2013|11/24/2012|10 Closed|Cote d'Ivoire|
|P110728|2008|2013|5/17/2013|11 Closed|Cote d'Ivoire|
|P110728|2008|2014|11/24/2013|12 Closed|Cote d'Ivoire|
|P110728|2008|2014|6/2/2014|13 Closed|Cote d'Ivoire|
|P110728|2008|2015|8/30/2014|14 Closed|Cote d'Ivoire|
|P110728|2008|2015|12/30/2014|15 Closed|Cote d'Ivoire|
|P110728|2008|2015|6/19/2015|16 Closed|Cote d'Ivoire|
|P110728|2008|2016|1/19/2016|17 Closed|Cote d'Ivoire|
|P110728|2008|2016|4/6/2016|18 Closed|Cote d'Ivoire|
|P110733|2010|2010||0 Closed|Yemen,Republic of|
|P110733|2010|2011|12/10/2010|1 Closed|Yemen,Republic of|
|P110733|2010|2011|1/9/2011|2 Closed|Yemen,Republic of|
|P110733|2010|2012|7/30/2011|3 Closed|Yemen,Republic of|
|P110733|2010|2012|12/10/2011|4 Closed|Yemen,Republic of|
|P110733|2010|2012|5/11/2012|5 Closed|Yemen,Republic of|
|P110733|2010|2013|1/6/2013|6 Closed|Yemen,Republic of|
|P110733|2010|2013|2/25/2013|7 Closed|Yemen,Republic of|
|P110733|2010|2013|6/5/2013|8 Closed|Yemen,Republic of|
|P110733|2010|2014|11/18/2013|9 Closed|Yemen,Republic of|
|P110733|2010|2014|5/10/2014|10 Closed|Yemen,Republic of|
|P110733|2010|2015|11/4/2014|11 Closed|Yemen,Republic of|
|P110733|2010|2015|12/23/2014|12 Closed|Yemen,Republic of|
|P110733|2010|2015|6/5/2015|13 Closed|Yemen,Republic of|
|P110733|2010|2016|12/16/2015|14 Closed|Yemen,Republic of|
|P110733|2010|2016|6/16/2016|15 Closed|Yemen,Republic of|
Employers in manufacturing and services industries express similar preference rankings for specific skill types; socio-emotional and higher-order cognitive skills emerge strongly for employers in both industries, with the exception of Vietnam. Our sample includes four studies that disaggregate employer demand by industry. The Indonesia, Vietnam, and Philippines studies disaggregate by manufacturing sector and by (non-education) services sector. The LAC study (Bassi et al. 2012) considers five sectors.

The manufacturing sectors demand a range of skills across the world. LAC and Filipino employers prioritize socio-emotional skills. In the Philippines, independent work (15 percent) and team work (13.5 percent) are ranked highest (diGropello 2010).<sup>45</sup> In LAC, 44 percent of manufacturing employers state that socio-emotional skills are most important (Bassi et al. 2012).<sup>46</sup> In contrast, Indonesian manufacturers prioritize cognitive skills; 72 percent identify higher-order cognitive skills as a priority for managers and 42 percent state that basic cognitive skills are the most important skills for workers (diGropello 2011) (Figure 4). These employers give a second place rank to basic cognitive skills for managers (54 percent) and higher-order cognitive skills for workers (42 percent). Notably, socioemotional skills are ranked the same as basic cognitive skills for Indonesian manufacturing sector managers. Showing yet a different trend, Vietnamese manufacturing employers top rank technical skills (16 percent), followed by punctuality (World Bank 2008).

Service sector employers demonstrate a similar heterogeneity among preferred skills sets. LAC service sector employers prioritize socio-emotional skills (53-58 percent) by a much higher margin than do LAC manufacturing employers (Bassi et al. 2012). Filipino service sector employers prioritize independent work (13 percent) and communications (11 percent) (diGropello 2010). Indonesian employers most value higher-order cognitive skills (72 percent) and basic cognitive skills among managers and workers in the service sector, respectively (Figure 4). Although behavioral skills in managers were also top ranked (72 percent) by Indonesian service sector employers. Conversely, Vietnamese service sector employers most value job-related skills (25 percent), followed by drive, initiative and teamwork (World Bank 2008).

The priority skills within a country are identical across industries; in other words, the preferred skill set in manufacturing is also the preferred skill set in services in all countries in the sample.

> 45 The Philippines study ranks basic skills, higher-order cognitive, and socio-emotional skills on one scale and ranks technical skills on  a separate scale.

> 46 Although technical skills are ranked last by these employers, it is valued by twice as many employers in the manufacturing sector as compared to the service sector.  And, the gap in technical skills is identified by 14 percent of manufacturing sector employers versus 1-4 percent of service sector employers.

20
World Table 5.6.

3. The strength of the enforcement system. The strength of the enforcement system is proxied by an average of three subjective indicators reported in International Country Risk Guide (ICRG) for the period 1990-92. The three indicators considered are quality of the bureaucracy, corruption in government, and rule of law. For the indicator _quality of the bureaucracy,_ high scores indicate "autonomy from political pressure" and "strength and expertise to govern without drastic changes in policy or interruptions in government services", also existence of an "established mechanism for recruiting and training." For _corruption in government,_ lower scores indicate "high government officials are likely to demand special payments" and "illegal payments are generally expected throughout lower levels of government" in the form of "bribes connected with import and export licenses, exchange controls, tax assessment, police protection, or loans". _Rule of law_ "reflects the degree to which the citizens of a country are willing to accept the established institutions to make and implement laws and adjudicate disputes." Higher scores indicate "sound political institutions, a strong court system, and provisions for an orderly succession of power." Lower scores indicate "a tradition of depending on physical force or illegal means to settle claims." ICRG is a publication of Political Risk Services of Syracuse, NY and is cited in Knack and Keefer (1994).

Indicatorg:

1. Value-added tax evasion rate. Data for this variable correspond to one of the years in the period 1990-93 for each country in the sample. The VAT evasion rate is calculated as one minus the tax compliance rate, where the tax compliance rate is calculated as

-44-
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enterprise in which the individual works.<sup>2</sup> But for this very reason these worker based approaches do not have the detailed enterprise level information needed for further firm based analysis of noncompliance.

However, for the Factories Act (1948) in India, there is also an indirect method for determining noncompliance which does not broach illegality directly. Rather, the National Sample Survey Organisation (NSSO) surveys the universe of enterprises which are not registered under this Act and, inter-alia, asks about number of workers and electricity usage, which together define the criterion for required registration under the Act. Using this data source, therefore, noncompliance can quantified. These quinquennial NSSO surveys have of course been used in the past for a number of purposes, but not in any systematic way to explore magnitudes and patterns of noncompliance.<sup>3</sup> In this paper we provide recent estimates for noncompliance and explore some of its patterns. We find noncompliance to be significant. Indeed, as many firms are not complying as are complying. Further, we find the number of noncompliant firms is much larger than the number of firms adjusting out of the regulation. We explore the main trends and patterns of noncompliance and highlight issues for analytical and policy research.

The plan of the paper is as follows. Section 2 reviews the theory of responses to regulation including noncompliance, and provides the frame for our investigation. Section 3 describes the Indian institutional setting and our data set, and presents the main trends on compliers, evaders, avoiders and outsiders to India’s Factories Act. Section 4 focuses on noncompliance and explores some key patterns of noncompliance in India. Section 5 concludes with a discussion of policy implications and highlights areas for further analytical and policy research.

> 2 For an application of this method to South Africa see Bhorat, Kanbur and Mayet (2012), and for an application to Chile see Kanbur, Ronconi and Wedenoja (2013).

> 3 For example, Bedi and Banerjee (2007) and Nagraj (2002).

3
- 16 -

# **C.** Risk management and hedging

Overview. Eurodollar contracts are extremely useful hedging devices precisely because they can be chained together into strips that behave like longer-term assets and liabilities, as illustrated above. Eurodollar contracts on the CME extend out to seven years. Trading activity in the outer years --on whicl. open interest is still substantial-- is in fact dominated by hedge users, especially of swap portfolios. Broadly speaking, a hedge consists of a proportional amount of the futures contracts and of the underlying. Futures-based hedges are relatively static --i.e., they do not require much dynamic adjustment-- because of the relatively stable relationship between the futures and the underlying.

Hedging mis-matches. The exactness of a eurodollar-based hedge has to do, aside from the optimality of trade construction, with how closely the rate underlying eurodollar futues (i.e., 3-month LIBOR) corresponds with the rate being hedge. There are two particularly prevalent types of mis-matches:

- _timing_ mis-match: reset or maturity dates of the hedged asset or liability versus futures expiration dates.

- _basis_ mis-match: the nature of the rate underlying the hedged asset or liability versus 3- month LIBOR

Devising the hede ratio. The number of futures contracts used to hedge (a particular or a portfolio of) fnancial instruments is called the _hedge ratio,_ which is determined by:

# = Hedge ratio scale factor x basis point value factor x volatility factor

- _Scale factor:_ it is the ratio of the notional or principal amount of the asset being hedged to the futures contract size.

- _Basis point value factor:_ it is the ratio of the change in the dollar value of the hedged asset to the change in the dollar value of the futures contract _for a one basis point change in the interest rate._ In other words, it measures the relative sensitivity of the hedged asset and the hedge instrunent to changes in the interest rate environment.

- _Volatility factor:_ it takes account of the possibility that the yields on the hedged asset and the futures contract do not move one-for-one or exactly together. That is, it measures the
Lastly, looking at world- and country-specific GDP rather than trade, reenforces the message of resilience. Clearly, the world economy took a major hit with the COVID shock in 2020. But most countries rebounded. World GDP contracted in real terms by -3.2 percent in 2020, but increased to 5.9 percent in 2021. The United States economy grew on par with global growth in 2021, and the recovery was even faster in several regions according to the World Bank (2023).<sup>21</sup> Even though the pandemic is still not over and many of its effects will be felt for many years to come, the world economy is far from broken. Global growth in 2022 is estimated at 2.9 percent. This is indeed a slowdown relative to the rapid recovery of 2021, but still greater than the 2019 rate of 2.5 percent (World Bank 2021). So, once again, based on the criterion “bend but not break,” the world economy proved resilient during the pandemic.

In sum, despite the prominence of resilience concerns in the public debate in the past three years, the evidence to date provides no support either for the view that global supply chains were not resilient during the pandemic or that the world economy would have been more resilient if there had been less dependence on foreign inputs and trade.

**Invasion of Ukraine and Energy Crisis:** The alleged effects of trade on advanced economies’ labor markets and inequality as well as COVID-19 supplied many arguments against free trade and globalization, but none of them seemed to cause a reversal of attitudes and longterm trends by itself. Nevertheless, these developments might have prepared the ground for what has turned out to be a game changer in the debate and policy on globalization: the quest for resilience, this time not to economic shocks or natural disasters, but to geopolitical turmoil, and the emergence of national security as a major reason for rethinking globalism.  This is turn fed new demands for reshoring as well as for a rather new concept in trade policy: “friendshoring,” i.e., trading only with, or predominantly with, friendly nations.

The catalyst for this new phase in the de-globalization movement was the invasion of Ukraine by Russia in February 2022 that was followed by a major energy crisis. At this point, the vulnerability of Europe to relying on a single source (i.e., Russia) for a large share of its energy imports became apparent. The evidence on supply chain resilience during COVID-19 provided by Khanna, Morales and Pandalai-Nayar for Indian firms as well as by our results for US firms suggest that the private sector had taken steps to cope with exogenous shocks, at least in those

> 21 In 2021, real GDP growth in the United States was 5.9 percent, but 7.9 percent in South Asia 7.2 percent in East Asia and the Pacific, 6.8 percent in Eastern Europe and Central Asia.

27
and Practices, edited by C. Monga and J. Y. Lin. Oxford, U.K.: Oxford University Press.

Steinwender, C., and P. Shu. 2018. “The Impact of Trade Liberalization on Firm Productivity and Innovation.” NBER Working Paper 24715, National Bureau of Economic Research, Cambridge, MA.

Strzelecki, P., J. Growiec, and R. Wyszy ń ski. 2020. “The Contribution of Immigration from Ukraine to Economic Growth in Poland.” NBP Working Paper 322, National Bank of Poland, Warsaw.

Trejos, S., and G. Barboza. 2015. “Dynamic Estimation of the Relationship Between Trade Openness and Output Growth in Asia.” Journal of Asian Economics 36 (February): 110-25.

UNCTAD (United Nations Conference on Trade and Development). 2021a. World Investment Report 2021. Investing in Sustainable Recovery. New York: United Nations.

UNCTAD (United Nations Conference on Trade and Development). 2021b. Technology and Innovation Report 2021. Catching Technological Waves Innovation with Equity. New York: United Nations.

UNCTAD (United Nations Conference on Trade and Development). 2021c. Reaping the Potential Benefits of the African Continental Free Trade Area for Inclusive Growth. New York: United Nations.

UNCTAD (United Nations Conference on Trade and Development). 2022. Investment Trends Monitor. Issue 40. Geneva: United Nations Conference on Trade and Development.

UNESCO (The United Nations Educational, Scientific and Cultural Organization). 2019. “Combining Data on Out-of-school Children, Completion and Learning to Offer a More Comprehensive View on SDG 4.” Information Paper 61, UNESCO Institute for Statistics, Montreal, Canada.

UNESCO and UNICEF (United Nations Educational, Scientific and Cultural Organization; United Nations International Children’s Emergency Fund). 2021. Situation Analysis on the Effects of and Responses to COVID-19 on the Education Sector in South Asia. Sub-regional Report. October.

UNESCO, UNICEF, and World Bank (United Nations Educational, Scientific and Cultural Organization; United Nations International Children’s Emergency Fund). 2021. COVID-19 Learning Losses: Rebuilding Quality Learning for All in the Middle East and North Africa. Paris: UNESCO.

World Bank, UNESCO, and UNICEF (United Nations Educational, Scientific and Cultural Organization; United Nations International Children’s Emergency Fund). 2021. The State of the Global Education Crisis: A Path to Recovery. Washington DC: World Bank; Paris: UNESCO and UNICEF.

UNHCR (United Nations High Commissioner for Refugees). 2022. “Ukraine Situation: Flash Update #8.” UNHCR Regional Bureau for Europe. https://data2.unhcr.org/en/documents/details/92011.
# **Table 1:  Summary Statistics**

|**Trade Data**|
|---|

|||1992|1995|1997|1999|2001|2003|2005|
|---|---|---|---|---|---|---|---|---|
|China's Total Exports|$USbil|84.94|136.50|160.34|163.81|211.19|334.53|525.49|
|China's Total Processing Exports|$USbil|39.92|67.92|87.59|93.23|117.04|184.56|287.24|
||share (%)|0.47|0.50|0.55|0.57|0.55|0.55|0.55|
|China's Exports to US (Chinese Data)|$USbil|8.59|22.67|28.70|35.25|43.08|70.59|112.34|
||share (%)|0.10|0.17|0.18|0.22|0.20|0.21|0.21|
|China's Exports to US(US Data)|$USbil|25.73|41.79|54.87|68.73|81.17|116.32|167.91|

# **Table 2: Gini Coefficient for China's Exports**

|**Period**|**All**|**Top 70%**|**Top 100**|
|---|---|---|---|
|1992|0.85|0.46|0.35|
|2005|0.86|0.55|0.50|

18
**Table 10: Credit to the Public Sector, Before and After Privatization**

|Bank|Credit to Public<br>Sector, Pre-<br>Privatization<br>(Millions of Pesos)|Credit to Public<br>Sector, Post-<br>Privatization<br>(Millions of Pesos)|Credit to Public<br>Sector, Pre-<br>Privatization<br>(% of Total)|Credit to Public<br>Sector, Post-<br>Privatization<br>(% of Total)|Bad Credits as<br>a Percentage of<br>Total<br>1996|
|---|---|---|---|---|---|
|Chaco|379,892 (1994)|223 (1996)|46.5|0.4|13.8|
|Corrientes|378,469 (1992)|1,458 (1996)|90.9|15.6|35.2|
|Entre Rios|115,389 (1994)|108,281 (1996)|17.0|23.8|21.7|
|Formosa|54,065 (1994)|0 (1996)|20.7|0.0|0.0|
|La Rioja|379,083 (1991)|0 (1996)|95.8|0.0|28.4|
|Misiones|164,681 (1994)|38,234 (1996)|33.6|58.6|5.0|
|Rio Negro|158,419 (1992)|7,555 (1996)|18.8|9.2|9.1|
|Salta|33,227 (1995)|15 (1996)|19.5|0.0|0.0|

Source: B.C.R.A.

To summarize, the available data indicate that privatized provincial banks are far more similar to Argentina's ten largest private banks than they are to public provincial banks. First, they appear to operate as profitably and efficiently as the private banks as indicated by their ratios of operating income to costs. In addition, their composition of operating income has shifted slightly towards net financial income as opposed to income from services. Although the private banks continue to generate a higher share of their income through financial income, the privatized provincial banks are gaining ground. The improved situation of the privatized provincial banks is also reflected in their portfolio quality. Their percentage of bad credits is lower than for public provincial banks. These improvements in portfolio quality, moreover, have coincided with a decreased emphasis on public credit. All of this augers well for the future solvency of these banks. More importantly, perhaps, the data suggest that credit allocation is improving as a result of privatization. Although the social welfare effects of improved credit allocation are difficult to quantify, these may be privatization's most important legacy.

# V CONCLUSIONS

The evidence from Argentina strongly suggests that, from a fiscal perspective, the decision to maintain a public provincial bank is a costly one. Loss rates for public provincial banks in Argentina were much higher than for other banks, implying that the future fiscal costs 24