rafmacalaba/datause-encoder-data huggingface dataset:
important. Six million full-time workers were employed in the fisheries sector in China in 2021 (Bureau of Fisheries et al., 2022). The government particularly encourages distant water fishing, taking place on the high seas or in the EEZs of other countries, because it contributes to China’s food security, “Marine Power Strategy”, and its “Go Global” and “One Belt One Road” directives (Bureau of Fisheries, 2017; General Administration of Customs and Ministry of Agriculture, 2000; Zeng, 2022).

Chinese fisheries fuel subsidies began in 2006 as a complement to China’s economy-wide, partial liberalization of oil prices. In order to reduce the cost pressure caused by higher fuel prices, the government provided subsidies for vulnerable groups and industries, including fishers and fishing companies (National Development and Reform Commission, 2006). The Chinese government reforms its distant water fuel subsidy policy every five years as part of its Five-Year Plans,<sup>3</sup> and it has separate fuel subsidy policies for distant water fishing vessels and for vessels that fish in its EEZ.<sup>4</sup> Vessels must be “flagged to” (registered in) China to receive fuel subsidies. The distant water fuel subsidy policies between 2006 and 2015 were mainly based on fuel prices and consumption (Department of Industrial Policy and Regulation, Ministry of Agriculture, 2021). Instead of achieving its original intent of protecting fishers when oil prices are high, the government came to view this subsidy formula, which awarded subsidies proportionally to fuel consumption, as leading to undesirably high levels of subsidy expenditure, fuel consumption, and fishing (Ministry of Finance, 2016).

China reformed its fuel subsidy policy in 2016 with this concern in mind. We study the effects of the distant water fuel subsidy policy between 2016 and 2020 because that policy features numerous thresholds at which fuel subsidies discontinuously increase with vessel size (Ministry of Agriculture, 2016). Distant water fuel subsidy policies between 2006 and 2015, and from 2021 onward, do not feature discontinuities according to vessel size (Department of Industrial Policy and Regulation, Ministry of Agriculture, 2021; Ministry of Agriculture 3 2006 was the first year of the 11th Five-Year Plan. There have been four iterations of distant water fuel subsidies so far: 2006 to 2010, 2011 to 2015, 2016 to 2020, and 2021 to 2025, corresponding to China’s 11th, 12th, 13th, and 14th Five-Year Plan periods.

> 4In 2011, the most recent year in which EEZ fuel subsidies data are available, fuel subsidies to vessels that fish in China’s EEZ totaled $3.2 billion, 20.63 billion yuan (Bureau of Fisheries, 2012; China Foreign Exchange Trade System, 2021). By comparison, total distant water fuel subsidies in 2011 were $415 million, or 2.68 billion yuan (Bureau of Fisheries, 2012; China Foreign Exchange Trade System, 2021).

6
# PRE Working Paper Series

|_Ih_|Author||Contact<br>fo<br>_Qr_p=r|
|---|---|---|---|
|WPS675 Are Buybacks Back? Menu-Driven<br>Debt-Reduction<br>Schemes<br>with<br>Heterogenous<br>Creditors|Ishac Diwan<br>Mark M. Spiegel|May 1991|S. King-Watson<br>33730|
|WPS676 Higher Education<br>in the Republic<br>of Yemen: The University<br>of Sana'a|Viswanathan<br>Selvaratnam<br>Omnpom<br>L. Regel|May 1991|C. Cristobal<br>33640|
|WPS677 On Economic<br>Transformation<br>in East-<br>Central Europe: A Historical<br>and<br>International<br>Perspective|Andres Solimano|May 1991|E. Khine<br>39361|
|WPS678 Economic<br>Growth: A Review of the<br>Theoretical<br>and Empirical Literature|David Renelt|May 1991|R. Luz<br>34303|
|WPS679 Poverty Alleviation in Mexico|Santiago Levy|May 1991|M. Stroude<br>38831|
|WPS680 On Hunger and Public Action|Martin Ravallion|May 1991|C. Spooner<br>30464|
|WPS681 Political-Economy<br>Arguments<br>for<br>UniformTariff|Arvind Panagarlya<br>Dani Rodrk|May 1991|K. Cabana<br>37947|
|WPS682 Intertemporal<br>Substitution,<br>Risk<br>Aversion,<br>and Private Savings in<br>Mexico|Patricio Arrau<br>Sweder vanWijnbergen|May 1991|S. King-Watson<br>31047|
|WPS68a Vocational<br>Schooling, Occupationa<br>Matching,<br>and Labor Market Eaming<br>in Israel|l Shoshana<br>Neuman<br>s Adrian Ziderman|May 1991|C. Cristobal<br>3640|
|WPS684 The Value of Intra-household<br>Survey<br>Data for Age-based<br>Nutritonal<br>Targeting|Lawrence<br>Haddad<br>Ravi Kanbur|May 1991|J. Sweeney<br>31021|
|WPS685 Children and Intra-household<br>Inequality: A Theoretical<br>Analysis|Ravi Kanbur|May 9IPTI'|J.SweenOy<br>31021|
|WPS686 Lending<br>for Learning: Twenty Years<br>ofWorld Bank Support<br>for Basic<br>Education|Adriaan Verspoor|Ma<br>'|C. Cristobal<br>33640|
# **1. Introduction**

The transition process from school to the work place, and how to increase its efficiency, is a puzzle for researchers and policymakers. Youth unemployment rates across the world are more than double those of adults, which is often interpreted as a lack of decent work opportunities for youth (ILO, 2008). Short-term informal employment and high turnover are viewed as exploitative experiences that are inherent to modern labor markets that youth face (Weller, 2007; Schkolnik, 2005).

The scarce evidence of how young workers make their transitions into the labor market suggests a different story. For example, Bosch and Maloney (2010) find that informal jobs in Latin America are used as a stepping-stone in the school to work transition providing young workers with essential skills that allow them to advance on their employment path. In a similar way, high turnover among European youth demonstrates a prolonged job search process in which young entrants search for available opportunities, revealing to themselves and their employers their strengths and preferences for jobs (Topel and Ward, 1992).

This paper maps out young people’s path from the classroom to the work place in three Latin American countries. We use panel data to follow young people’s movement between school and work and between employment sectors to better understand the dynamics of youth employment, including where youth go upon leaving school, how long they spend in each state, and where they go upon leaving various employment states. The analysis differentiates between adolescents (15-18 years old) who are at the point of deciding to leave school and enter work and may still be dependent on parents, young adults (19-24 years old) who are settling into the labor force, and prime aged adults (age 25-44), who are used as a counterfactual. We consider the behavior in Brazil, Mexico and Argentina, which offer distinct labor market characteristics that may give us further insight into the school-towork transition processes<sup>2</sup> .

> 2 Brazil has moderate unemployment rate, Mexico is characterized by low unemployment rates, and Argentina is a high unemployment economy.

2
22 For the MICs, the teacher survey from McKinsey (2020) reports reach _×_ effectiveness rate = 54% for China. Given an effective closure rate of 43.5%, the loss is about 20%. Likewise, children were 1.4 months behind by November 2020, also a loss of 20%. In India, another large MIC, the ASER (2020, 2021) surveys suggest students were about 50% behind in 2020. Given an effective closure rate of 77%, reach _×_ effectiveness = 35%. Since India is likely more representative of the MIC group’s ability to provide effective remote learning than China, and since no improvement has been observed based on descriptive evidence, we assume reach*effectiveness = 35% for MICs in both years.

Finally, for the LICs we have reach*effectiveness = 25% based on the ministries’ data. Since we use 40-45% for HICs, 35% for MICs, we arbitrarily use 20% for LICs. **Computing the loss** _ν_ **.** The disruption to school was greatest in the MICs (33% of two years of education), due to their higher effective school closure rate (see Table 2). Schools were then slightly more open in the LICs, but reach*effectiveness was much higher in HICs, making the shock less severe in HICs (21% vs. 28% for LICs). **Employment Shock** The ILO (2020) reports that the peak loss in total hours worked (from unemployment, inactivity and reduced hours) across the HICs, MICs and LICs are 15 _._ 8, 20 _._ 9 and 12 _._ 4 percent respectively. However, the peak is not indicative of the impact of the shock over the entire year. Instead, we assume a loss equal to the average over 2020, i.e. 8 _._ 3, 9 _._ 5 and 6 _._ 7 percent respectively (see Web Appx. Section D for details). **5.2.2. Results** For the HICs, the employment disruption is of 8 _._ 3 percent, and the schooling disruption is of 21 percent (south east of point C in Fig. 5(a)). For the MICs, the employment disruption is of 9 _._ 5 percent, and the schooling disruption is of 33 percent (middle near the right edge in Fig. 5(b)). For the LICs, the employment disruption is of 6 _._ 7 percent, and the schooling disruption is of 28 percent (point to the west of point C in Fig. 5(c)).

The welfare impact equals 2 _._ 5 percent of steady state income in perpetuity in the HICs, compared to 2 _._ 2 percent in the MICs and 1 _._ 5 percent in the LICs (hence 2 _._ 1 percent on average in developing countries).<sup>41</sup> After ten years, when the direct impact of the shock has worn off, we get 1 _._ 2 percent in the HICs, compared to 1 _._ 2 percent in the MICs and 0 _._ 7 percent in the LICs (1 _._ 1 percent on average in developing countries).<sup>42</sup> LICs are,

> 41This is equivalent to a one-off hit of 63, 55 and 36 percent of income respectively. Alternatively, this is equivalent to $15 _,_ 478, $3 _,_ 478 and $419. Overall, this amounts to 59 percent of global GDP ($50 _._ 3 trillion). 42This is equivalent to a one-off hit of 29, 29 and 17 percent of income respectively. Alternatively, this is
**Table  2.2 Urban population, water supply and sanitation coverage, and selected disease burden by level of development**

|**Characteristics by**<br>**level of economic**<br>**development**|**Low-income**<br>**countries**<br>**(43 countries)**<br>**<$975**<br>**GNI/cap**|**Lower-middle-**<br>**income countries**<br>**(55 countries)**<br>**$976-3,855**<br>**GNI/cap**|**Upper-middle-**<br>**income countries**<br>**(46 countries)**<br>**$3,586-11,905**<br>**GNI/cap**|**High-income**<br>**countries**<br>**(66 countries, 27 OECD)**<br>**>$11,906**<br>**GNI/cap**|
|---|---|---|---|---|
|Total urban<br>population<br>(millions)|280.4|1,528.3|709.7|812.1<br>OECD 750.9|
|Percent of total<br>population that is<br>urban (%)|28.7|41.3|74.8|76.0<br>OECD 77.3|
|Urban population<br>with improved<br>drinking-water (%)|86.2|94.6|95.2|99.3<br>OECD 100.0|
|Urban population<br>with improved<br>sanitation (%)|49.6|60.3|86.6|99.4<br>OECD 100.0|
|Diarrheal disease<br>burden|59,207|11,798|1,309|438|
|(thousand DALYs)|||||
|Ascariasis disease|||||
|burden<br>(thousand DALYs)|661|304|34|6|

_Sources:_ Economic and urban population data from World Bank, 2010; improved sanitation data from WHOUNICEF, 2010; disease burden data from WHO, 2008.

DALY = Disability-Adjusted Life Years, a measure of the burden of disease due to a specific risk factor. _Note:_ Improved drinking-water is defined by WHO and UNICEF (2010) as using one of the following water sources: piped water into the dwelling, yard or plot; public tap or standpipe; tubewell or borehole; protected dug well; protected spring; and rainwater collection; Improved sanitation is defined by WHO and UNICEF (2010) as using facilities that ensure hygienic separation of human excreta from human contact. They include: flush or pour-flush toilets/latrines that are connected to a piped sewer system, septic tank or pit latrine; ventilated improved pit (VIP) latrines; pit latrines with slab; and composting toilets.

8
eliminating the effect of quality differences. The opportunity cost approach was used to derive the value of family labor.

**Table 5:** Surveys **included** in **the farm size-efficiency analyses of commercial farming**

|Region|Type of Farming (Predominant)|Year Covered by<br>Survey|Number of Farmers<br>Surveyed|
|---|---|---|---|
|Eastern Free State|Summer-rainfall (mixed):<br>maize, wheat, cattle, sheep|1979/80<br>1982/83<br>1985/86<br>1988/89|92<br>83<br>76<br>72|
|Transvaal Highveld|Summer-rainfall (mixed):<br>maize, sorghum, cattle, sheep|1974/75<br>1983/84|71<br>77|
|Western Transvaal|Summer-rainfall (grain):<br>maize, sunflower, cattle|1981/82|78|
|North-western Free State|Summer-rainfall (grain):<br>maize, wheat, sorghum|1979/80|87|
|Ruens|Winter-rainfall (mixed):<br>Wheat, sheep, dairying|1978179<br>1987/88|69<br>77|
|Swartland|Winter-rainfall (mixed):<br>Wheat, sheep, dairying, beef|1983/84|82|
|Vaalharts|Irrigation (annual crops):<br>wheat, cotton|1990/91|34|

All analyses were conducted separately for each region/survey. Because the analysis implicitly neglects possible production uncertainty (for example due to weather effects), the underlying assumption is that all farmers within each survey face similar production uncertainty. This seems to be appropriate given that the analysis is conducted for a given production year and one relatively homogeneous region at a time.

Table 6 provides a summary of the size characteristics of the farms in each of the surveys. From this information it is clear that the surveys cover a relatively large range of farm sizes. While relatively small farms are also part of the data set, the average farm size indicates that the **farms** are in general large, specifically relative to world standards. The median farm size is smaller than the average in all the data sets, indicating a positively skewed size distribution.

The final data for each farm in the different samples involve inputs and outputs. These were aggregated to give two output series -- crops and livestock -- and seven input series --land, buildings, livestock and machinery represented the stock inputs, while labor, management (including family labor) and variable inputs'7 represented flows. All quantity measurements used in the analysis were annual flow variables. The stock variables were transformed into flow variables by calculating the equivalent annuities 1" Variable inputs represented all the other inputs, including seed, fertiliser, purchased animal feed, chemicals, etc.

25
funds are reaching non‐poor regions instead. Countries in this group include Sweden and Latvia. In this case, countries could improve poverty reduction by better targeting the CAP funds to include poor regions where agriculture takes place.

# **6. Conclusion**

In this paper we first document the relationship between agriculture and poverty, and use this information to place the EU member states in their path towards achieving a complete structural transformation. We then proceed to investigate the regions where the CAP funding has been going within each country, by analyzing the relationship between the program’s payments and different indicators of poverty. Additionally, we find that the CAP has contributed to poverty alleviation. In this way, we conclude that the CAP is a powerful instrument towards achieving shared prosperity, and thus also complements the EU goal of social cohesion by reducing disparities in the regions where agriculture prevails.

# **7. References**

Crescenzi, R., & Giua, M. (2016). The EU Cohesion Policy in context: Does a bottom‐up approach work in all regions? _Environment and Planning A: Economy and Space_ 48(11), pp. 2340‐2357.

European Comission: Agriculture and Rural Development. (2017). The History of the Common Agricultural Policy. Retrieved from: https://ec.europa.eu/agriculture/cap‐overview/history_en European Parliament. (n.d.). Economic, Social, and Territorial Cohesion. Retrieved from: http://www.europarl.europa.eu/atyourservice/en/displayFtu.html?ftuId=FTU_3.1.1.html <mark>Mathur, S. K. (2005). Absolute convergence, its speed and economic growth for selected countries for 1961-2001.</mark> _<mark>Journal of the Korean Economy</mark>_ <mark>,</mark> _<mark>6</mark>_ <mark>(2), 245-273.</mark> World Bank (2018). Thinking CAP ‐ Supporting Agricultural Jobs and Incomes in the EU. _EU Regular Economic Report_ ; no. 4. Washington, D.C. : World Bank Group. <u>http://documents.worldbank.org/curated/en/892301518703739733/EU‐Regular‐Economic‐ Report‐Thinking‐CAP‐Supporting‐Agricultural‐Jobs‐and‐Incomes‐in‐the‐EU</u> <mark>Sachs, J. D., & Warner, A. M. (1997). Fundamental sources of long-run growth.</mark> _<mark>The American economic review</mark>_ <mark>,</mark> _<mark>87</mark>_ <mark>(2), 184-188.</mark> United Nations Human Settlement Program. (2016). Structural Transformation in Developing Countries: Cross Regional Analysis (Series 1). UN Habitat.

21
**Table 3. Correlations with aggregate steady-state employment ratios**

||I|II|III|IV|V|VI|
|---|---|---|---|---|---|---|
|Dependent variable: Aggregate<br>steady-stateEWAP|Sub-<br>Saharan<br>Africa|Other<br>EMDEs|Sub-<br>Saharan<br>Africa|Other<br>EMDEs|All|All|
|Non-agricultural steady-state<br>EWAP|0.177<br>[0.227]|0.718***<br>[0.072]|||||
|Agricultural steady-state EWAP|||0.666***<br>[0.085]|0.440**<br>[0.175]|||
|Female steady-state EWAP<br>|||||0.689***<br>[0.041]||
|Male steady-state EWAP||||||0.768***|
|||||||[0.048]|
|Constant|3.916|-5.460***|-6.221***|1.966|0.0159|-0.002|
||[3.547]|[1.250]|[1.881]|[2.162]|[0.779]|[0.801]|
|Observations|37|66|37|66|103|103|
|R-squared|0.017|0.611|0.637|0.091|0.735|0.720|

Source: Authors’ estimation.

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. Results from a cross-country OLS regression of deviations of steady-state employment ratios for aggregate employment on those for sub-components of employment. Deviations of steady-state employment ratios are derived from the estimates of Table 1. Standard errors in brackets.

25
**Figure 9. Assets of Micro-finance Institutions** **<u>(as per cent of assets of all financial institutions)</u>**

<!-- Start of picture text --> 10%<br>9%<br>8%<br>7%<br>6%<br>5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>Barbados Brazil India Cameroon Ghana Nigeria Mozambique Peru Slovenia Bulgaria Cote d Ivoire Lithuania<br><!-- End of picture text --> World Bank database on FSAPs.

During the past years, micro-finance institutions have suffered multiple problems. MFIs have failed to become self-sustainable and many do not meet basic reporting and financial disclosure requirements. Moreover, there have been instances in which the public has been defrauded by unscrupulous persons posing as MFI officers (see Chiumya 2006). To address these problems, authorities have designed a new regulatory framework for this type of financial institutions that would create two categories of MFIs, based on their size and capital. According to the new Banking and Financial Services (Microfinance) Regulations of 2006, the minimum capital for deposit-taking microfinance institutions is Kwacha 250 million (US $78,125 dollars) and for nondeposit microfinance institutions is Kwacha 25 million (US$7,812 dollars). The scope of permissible activities for MFIs would vary for each group of MFIs. Larger and more capitalized institutions would be allowed to take deposits from the public and lend, whereas smaller institutions would be allowed to lend, but not to take deposits from the public. Eventually, authorities expect the new regulatory regime will trigger a consolidation process among MFIs that will result in fewer but stronger micro-finance institutions in Zambia.

At this time, the major challenge for authorities is to put in place a regulatory framework that promotes the protection of people’s money, transparency and sound governance of MFIs, and the growth and long term self-sustainability of the micro-finance sector. Critically important would be to establish rules on secured and un-secured lending, including a broad definition of “assets” that can be used by borrowers to pledge as collateral for their loans. These regulations should be aligned with the economic condition of MFI’s clients in Zambia. Zambian authorities may wish to consider the experiences of successful cases of micro-finance, such as Jamaica, where MFIs serve 50% of the population on a sustainable basis.

18
by Equation (23) , by reparameterizing ann in Equation (24) as follows: where a ̃0n and τ nn are parameters to be estimated nonlinearly. Thus, the fi nal version of the share equation is where regression error term, u ̃<sup>t</sup> nc<sup>, has a normal distribution with expected value of zero and variance</sup> ̃ ̃ ̃ σ<sup>2</sup> . Given that a0n and τ is nonlinear with respect to u<sup>t</sup> nc<sup>,nonlinearestimationtechniquesare</sup> necessary.

Note that this may not be enough to ensure that all import demand elasticities are negative. Indeed, if the estimated a ̃0n turns out to be smaller than s ̄n, then some of the elasticities may still turn out to be non-negative. In other words, this is not a deterministic setup and s ̄n is only the E( ̃a0n). Thus, when estimating the import share equation prior to differencing with respect to the observation where the second order condition is more likely to be violated, if the error term of a particular observation is positive, then the estimated elasticity for this observation will also be positive. In those cases we impose a ̃0n ≡ s ̄n in the estimation procedure, which ensures that all elasticities are negative. This occurs in less than 3 percent of the sample.

Finally, given that the import demand elasticity is non-linear in the estimated parameters we estimate the standard errors of the import demand elasticities through bootstrapping (50 random draws for each six digit HS good).

# 4 Data

The data consist of import values and quantities reported by different countries to the UN Comtrade system at the six digit of the HS (around 4,600 products).<sup>11</sup> The HS was introduced in 1988, but

> 11 It is available at the World Bank through the World Integrated Trade System (WITS).

14