rafmacalaba/datause-encoder-data huggingface dataset:
under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act.

In the service industries, there is no simple legal distinction as in manufacturing. Service establishments, regardless of size or other characteristics, are not required to register and thus are all officially unorganized. There are various existing methodologies to comparably differentiate small-scale, autonomous establishments from larger employers which constitute the organized sector, as generally defined. We assign establishments with less than five workers and/or listed as an “own-account enterprise” (OAE) to the unorganized sector. OAE enterprises are firms that do not employ any hired worker on a regular basis. The choice of five employees as the size cutoff recognizes that average establishment size in services is significantly smaller than in manufacturing. Our results are robust to also using a ten-employee demarcation for services.

The organized manufacturing sector is surveyed by the Central Statistical Organisation every year through the Annual Survey of Industries (ASI), while unorganized manufacturing and services establishments are separately surveyed by the National Sample Survey Organisation (NSSO) at approximately five-year intervals. Establishments are surveyed with state and fourdigit National Industry Classification (NIC) stratification. We use the provided sample weights to construct population-level estimates of total establishments and employment at the state and three-digit NIC level. We focus mostly on state and industry variation in our empirical analyses, but we also consider district-level variation as a robustness check and to allow more localized traits where warranted. Districts are administrative subdivisions of Indian states or union territories.

Tables 1a and 1b document the establishment size distribution for India. In manufacturing, over 98% of establishments are unorganized, most with fewer than five workers. The last column compares this skewness to the distribution evident in the 1997 Census of Manufacturers for the United States. Only 51% of U.S. manufacturing establishments have fewer than ten employees. More broadly, about 74% of establishments have fewer than ten employees in the U.S. County Business Pattern data (regardless of sector). From an establishment size distribution perspective, India has an extreme concentration of very small establishments, as many others have noted.

These unorganized establishments account for 79% to 83% of all manufacturing employment in India. This concentration of employment among small establishments contrasts even more sharply with the United States, where the vast majority of output comes from larger establishments that would be part of the organized sector using India’s definitions. Despite 6
# **References Cited**

Acharya, Sanjaya. 2008. “Poverty Alleviation and the Industrial Employment of Women (The Case of Nepal),” _Journal of International Development_ 20 (5): 670-85.

Allendorf, Keera. 2007. “Couples' Reports of Women's Autonomy and Health-Care Use in Nepal,” _Studies in Family Planning_ 38 (1): 35-46.

Angrist, Joshua, and Adriana Kugler. 2008. “Rural Windfall or a New Resource Curse? Coca, Income, and Civil Conflict in Colombia,” _Review of Economics and Statistics_ 90 (2): 191–215.

Bhalotra, Sonia, and Marcela Umana-Aponte. 2010. “The Dynamics of Women‟s Labour Supply in Developing Countries,” IZA Discussion Paper No. 4879.

Blanc, Ann. 2004. “The Role of Conflict in the Rapid Fertility Decline in Eritrea and Prospects for the Future,” _Studies in Family Planning_ 35 (4): 236-45.

Bohara, Alok, Mitchell, Neil, and Mani Nepal. 2006. “Opportunity, Democracy, and the Exchange of Political Violence: A Subnational Analysis of Conflict in Nepal,” _Journal of Conflict Resolution_ 50 (1): 108-128.

Collier, Peter. 1999. “On the Economic Consequences of Civil War.” _Oxford Economic Papers_ 51 (1): 168-83.

Deraniyagala, Sonali. 2005. “The Political Economy of Civil Conflict in Nepal,” _Oxford Development Studies_ 33 (1): 47-62.

de Walque, Damien. 2006. “The Socio-Demographic Legacy of the Khmer Rouge Period in Cambodia,” _Population Studies_ 60 (2): 223-31.

28
# **Systemic Oversight Frameworks in LAC: Current Practices and Reform Agenda**

# **Eva Gutierrez, Patricia Caraballo**<sup>**1**</sup>

# **The World Bank**

**Financial and Private Sector Development Vice Presidency Latin America and the Caribbean Regional Vice Presidency**

## **JEL: G21, G28**

**Keywords:** systemic oversight frameworks, prudential oversight, financial regulation, financial supervision, macroprudential policies, Latin America

> 1 Eva Gutierrez is a Sr. Financial Sector Specialist in the Latin America and Caribbean Region of the World Bank (egutierrez2@worldbank.org). Patricia Caraballo is consultant at the World Bank (pcaraballo@worldbank.org). The authors want to thank first and foremost all survey respondents, as well as Alain Ize, Augusto de la Torre, Rudy Araujo, Rafael Diaz, Ilias Skamellos and Mariano Cortes for their comments and inputs on the survey design. Any remaining mistakes are our own.
# **<u>1. Introduction</u>**

Youth employment is at the center of the policy agenda around the world. The 2013 World Development Report emphasizes that employment not only matters for individuals’ welfare, but also for social cohesion (World Bank, 2012a). The Arab Spring highlighted the dramatic consequences of high youth unemployment in countries such as Egypt or Tunisia. Throughout most of the Middle East and North Africa, youth unemployment rates are particularly high among university graduates, and policymakers often look for innovative and effective policies to facilitate the transition of youth from education to work (World Bank, 2012b).

In economies characterized by constrained labor demand from the private sector and high rates of youth unemployment, entrepreneurship-support interventions targeting students are promising policy options. Entrepreneurship training has the potential to enable graduates to gain skills and create their own jobs, as well as possibly also better align their skills with private employers’ needs. Some OECD countries are providing entrepreneurship education to university students (Oosterbeek et al., 2008). Yet despite the potential of entrepreneurship training, evidence on how such programs shape students’ skills and facilitate entry into self-employment remains thin.

Tunisia has long experienced high unemployment, particularly among new university graduates. For instance, 46 percent of graduates of the 2004 class were still unemployed eighteen months after graduation (MFPE and World Bank, 2009). Recent data show that graduates’ unemployment has been rising. Based on the Tunisian Labor Force Survey, unemployment among youth holding a university degree increased from 34 percent in 2005 to 56 percent in 2011. In this context, Tunisia has attempted various reforms aiming to promote employability or self-employment among university graduates. This paper presents results from a randomized control trial evaluating a unique reform: the introduction of an innovative entrepreneurship track in the university curriculum.

In 2009, Tunisia mainstreamed a new entrepreneurship track into the applied undergraduate ( _licence appliquée_ ) curriculum. Students enrolled in the last year of their _licence appliquée_ were invited to apply to the entrepreneurship track, which entailed business training as well as 2
2 The essay is organized In the following way. The first two sections set the stage by first providing In Section B brief characterization ofdisplaced workers in Industrialized nations. Section C then discusses some of the major differences In labor market policies and emphasis on adult retraining in six OECD nations. The six nations examined are Australia, Britain, Canada, Japan, Sweden, and the U.S. Sections D and E draw in the experience of these OECD nations to address a number of Issues related to the role of retraining services in assisting displaced workers. Discussed In Section D are the questions of who needs retraining and how retraining should be done. Considered are issues involving the structuring of retraining courses, the importance of employer involvement In curriculum design, and performance-based contracting. In Section E, the cost effectiveness ofretraining services Is considered. The bulk of the evidence presented is for the U.S., since the U.S. devotes far more resources to evaluation research than any other industrialized country. However, some limited evidence is also discussed for programs in Australia, Britain, Canada, and Sweden. Finally, Section F summarizes the experience of OECD nations in meeting the retraining needs of displaced workers In a short list of lessons which might be applied In developing and middle-income countries. Finally, an agenda for future research is laid out in Section G.

# B. Characterizinq DQplaced Workers

While definitions of displaced workers differ acrbss countries, the following characterizations appear In most definitons (i) displaced workers are adults (20 years of age and older); (1 they possess a stable work history; and (iii they have permanently lost their jobs, typically because their plant or company closed down or moved. National surveys of displaced workers carried out during the 1980s in the U.S. and Canada allow for the two countries a good fix on the characteristics of the displaced. Using a definition that encompasses the three points just listed, displaced workers in the U.S. are found to be mainly men between the ages of 25 and 54 who lost blue-collar jobs in manufacturing, especially In the durable goods manufacturing sector. However, the third of the three U.S. surveys Indicates that by 1988 the proportion of workers displaced from nonmanufactunng, other than blue-collar
# Figure 4.2 - Plot of Sectoral Ranks for Linearly Weighted Acute Human Toxicity against Sectoral Ranks for Exponentially Weighted Acute Human Toxicity

<!-- Start of picture text --> 80<br>bd ®e |<br>70 ° ° |<br>° 6 |<br>°<br>e<br>*<br>e<br>eo<br>eo<br>e<br>-<br> ¢<br>Fa 60 °<br>2o<br>3° °<br>-_ e e@<br>2 50 e e<br>2 e °<br>Fx °<br>< e e<br>Zu e<br>2e<br>°<br>&4} °<br>@ ;<br>== | td °<br>=¥ 30 ° e °<br>Fr] o<br>2 | ; °<br>fd e °<br>20 | ; o<br>td<br>| -" @<br>o<br>¢<br>10 °<br>| Sd ° |<br>1 @<br>| @<br>e<br>e<br>°<br>Ce: @<br>0 10 20 30 40 50 60 70 80<br>Rank for Linearly Weighted Acute Human Toxicity<br><!-- End of picture text -->
# **Figure 5:** Effects on Public Goods Provision (Administrative Data)

**(a)** Administrative data, 2021

<!-- Start of picture text -->
-.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger<br>1.3<br>0.5<br>-0.4<br>-1.2<br>Local public service provision, 2021 (z-score)<br>-2.0<br><!-- End of picture text -->

# **(b)** Administrative data, 2014 (balance)

<!-- Start of picture text -->
-.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger<br>1.2<br>0.6<br>0.0<br>-0.6<br>Local public service provision, 2014 (z-score)<br>-1.2<br><!-- End of picture text -->

_Notes_ : In panel (a), the dependent variable is a standardized index of local service provision constructed using the 2021 _Podes_ survey. The index has the following 10 components: drinking water, sewage, garbage collection, street lighting, kindergartens, primary schools, village maternities ( _polindes_ ), community health centers ( _puskesmas_ ), paved roads, and public transit. We first standardize each individual component before taking the village-level average of all components. The sample includes all villages in our sample that conducted their last election before 2021. In panel (b), the dependent variable is a standardized index of local service provision constructed using the 2014 _Podes_ survey. The 2014 index has the same components except garbage collection, which was not collected in 2014, and a different coding for street lighting, which was categorized into two groups in 2014 ( _no lights_ , _any lights_ ), compared to three groups in 2021 ( _no lights_ , _some lights_ , and _many lights_ ). The dots are conditional means of each outcome across binned intervals of the margin of victory of the best-ranked challenger on each side of the RD threshold, with 95% confidence intervals in solid gray lines.

31
30 for 61 percent of the income gap. Estimates in the literature range from one third or less (Hall and Jones, 1999; Bils and Klenow, 2000; Caselli, 2005) up to four fifths (Jones, 2014), but recent work by Hendricks and Schoellman (2018) finds that human capital accounts for 62 percent of the income gap. Lagakos et al. (2018a) find a value of 74% when estimating returns to education and experience among immigrants in the US. Thus, our results are broadly consistent with the recent literature.

What is the impact of education and experience _separately_ ? Assume that demographic and labor market factors are the same in the developing economy as in the developed economy, but that the returns to experience are the same, so that the only difference is in the quantity and returns to schooling. In this case, GDP is 75 _._ 6%, leaving 33 _._ 9% of the output gap unaccounted for. Instead suppose that demographic and labor market factors are again the same, but that the quantity and returns to education are the same, so that the only difference is in the returns to experience. Now GDP is 75 _._ 2%, leaving 34 _._ 4% of the output gap unaccounted for. Thus, separately, schooling and experience each account for about a third of the income gap.

Figure 4 displays _pcGDPt_ in the developing economy after various reforms that might impact the schooling distribution and/or the returns to schooling and experience. First, in the NW panel, we increase the quantity of schooling, the returns to education and the returns to experience in the developing economy to the levels of the developed economy. Relative _pcGDPt_ rises steadily for about 50 years before slowing and settling around 87 percent. Output per worker converges to 92 percent of that in the developed economy (not shown) – closer, because it takes account of the fact that participation is lower in the developing economy, but still some distance away.

The NE panel assumes the schooling distribution converges to that in the developed economy. This raises income per head to 69 _._ 4 percent. Part of this is attributable to the _experience college premium_ . If we set _r_ ̄ _e_ equal to _<u>re</u>_<sup>inthedevelopingeconomy</sup> after transition (eliminating the premium), the impact of increasing education is lower, raising income to 65 _._ 2 percent of the developed economy. About a quarter of the jump from increasing schooling is due to the experience college premium.

This experiment assumed that the returns to schooling do not respond to increases in the quantity. We repeat the experiment of increasing the distribution of schooling to that in the developed economy, while also changing the schooling return parameters to
The range, however, is quite large, from low figures of about 50 days in Rostov Oblast and 10,000 Rubles in Novgorod Oblast to high figures of over 400 days in Novosibirsk and 360,000 Rubles in Moscow Oblast.

- While many regions and municipalities have instituted mechanisms to privatize real estate, most are not yet transparent or fair. Unfortunately, left to their own devices, they have little incentive to improve their procedures and advance the reforms.

The authors then developed and tested a number of hypotheses, using the survey data, with findings as outlined below.

The principal factor influencing the level of land privatization in a region (for land under privatized buildings) is the pricing policy pursued by local authorities. The analysis demonstrates that in those surveyed regions where the local government pricing policy is at the low end of the range allowed by Federal law, the rate of land privatization transactions is higher (even though rental rates for long-term leases may often appear more beneficial). Within the examined model, all other factors being equal, a modification of the pricing policy from the higher end of the range allowed by Federal law to the lower end is associated with a significant increase in the rates of land privatization (more than doubling for some regions).

Excluding the pricing policy from consideration, the length of time required to complete the relevant procedures becomes the main factor influencing the level of land privatization (for all types of land privatization). Although the survey data don’t show it explicitly, it is understood that most market participants are generally aware of the time required for such transactions, _ex ante_ , within their jurisdiction. The longer the duration of the procedure, the lower the rate of the privatized land in a region. A decrease in the average procedure duration by one month (30 days) or by approximately 14%, other things being equal, increases the number of land privatization transactions per 100,000 residents by about 11%.

Delays associated with land privatization procedures in turn lead to an increase in the proportion of transactions for long term land leases as opposed to land ownership (leases in the Russian Federation are less than fully marketable, relative to, say, Hong Kong). If the delays are reduced by 25% from their mean length, other things being equal, the rate of land lease transactions would decrease by about 15 percentage points in favor of land privatizations.

A second factor influencing land privatization is frequency of refusals by government agencies in the course of a procedure. The analysis demonstrates that while processing land privatization applications, government agencies tend to refuse the completion of such transactions twice as much, on average, as land lease procedures, even though the procedures and criteria are supposed to be the same.

Procedures in which applicants have significant sunk costs are much longer in comparison to the “reversible” ones. On average, other things being equal, “sunk cost procedures” take about 34% more time than procedures where the applicant usually does not have sunk costs.

3
Here, _V LS_ refers to the volume of local shallow groundwater in m<sup>3</sup> that is available in GAEZ grid cell _g_ . _q_ ranges from 1 to _N_ , representing the set of aquifer grids intersecting with GAEZ grid _g_ . _V GW_ is the total volume of groundwater across all aquifer types available in aquifer grid _q_ . We multiply _V GW_ by the share of the aquifer area that intersects with GAEZ grid _g_ and the share of groundwater that is attributable to local shallow aquifers, _PoLS_ . We then multiply the total volume of local shallow water by (1 - _SDU_ ), where _SDU_ is the share of groundwater needed for sanitation and drinking usage.

To estimate a crop’s potential share of local shallow groundwater, we multiply _V LS_ by the share of the crop’s harvest area relative to the grid’s total area.

## **5.2.2 Estimating Water Demand**

To estimate the water demand for each crop in each grid, we sum the evapotranspiration of the crop and the crop’s water deficit during the growing season. For grids where either of these variables is missing, we impute the crop water demand or crop’s water requirement based on the mean crop water demand of immediate neighbors, neighbors within 25 kms of the grid’s center, or the national average crop water requirement for that crop.

# **5.3 Measuring Income Gains**

Understanding the income potential of the simulated gains in production is important for agricultural policy engagements with governments in Africa. To estimate income gains, we simply apply producer prices to GAEZ grids with estimated production gains and then compare the total gains to the 1.90 USD poverty line. We employ a back-of-the-envelope approach as described below.

We first source geo-located food product price data (Development Data Group, 2021) via the World Bank Micro-data Library API. Our API query returns local currency (nominal) prices for all African countries with production gains for each crop and each market between 2007 and 2015 ( _πcm_<sup>_nom_).Thisallowsustoincreasethenumberofmarket-level</sup> prices available within each country and smooth over sudden price shocks. We apply the corresponding national monthly consumer price index (CPI) and PPP conversion factors from the World Bank’s Poverty and Inequality Platform (PIP) (Aron et al., 2023) to convert to real monthly average prices in 2010 USD for each crop in each market.

We then perform a spatial merge of the grids with production gains to the nearest market from the food producer price database. Consequently, the prices that apply to specific crop production gains in each grid are those from the nearest markets in the food 13
that the land area under coffee even increased slightly in the second period (Table 6), which can be attributed to the further expansion of coffee farms that were already large in 1995.

Unfortunately, there is very little information on other agricultural inputs in the 1995/96 survey. Actually, only hired farm labor turns out to be an agricultural input that is comparable across the three surveys, but the data appears to be severely contaminated by changes in survey design between 1992/93 and 1995/96. Yet, there is some evidence that in the second half of the 1990s more permanently hired labor was used in agriculture, and more so by coffee farmers.

Table 5: Distribution of coffee farmers by land size groups, 1992-1999

|_Landholdings in_|_Distributio_|_n of farms by_<br>_(%)_|_land size_|_Share of coff_<br>_si_|_eefarmers in_<br>_zegroup (%_|_each land_<br>_)_|
|---|---|---|---|---|---|---|
|_acres_|_1992_|_1995_|_1999_|_1992_|_1995_|_1999_|
|less than 1|25.5|29.5|22.7|18.5|24.6|27.6|
|1-2|28.7|32.0|31.4|24.9|39.1|42.7|
|2-3|19.1|17.1|19.6|23.8|49.2|49.6|
|3-5|16.9|12.4|15.6|27.6|43.5|50.7|
|5-10|7.8|5.9|8.1|39.0|56.9|64.7|
|more than 10|2.0|3.2|2.8|40.8|70.2|72.9|
|total|100.0|100.0|100.0|24.9|39.1|44.5|

Source: Authors’ calculations.

response because coffee yields tend to vary widely with weather conditions. This qualification also holds for the following assessment of changes in coffee production.

21