rafmacalaba/datause-encoder-data huggingface dataset:
works only for one subdelegacia and reports to the subdelegado.<sup>48</sup> There is an effort to apply homogeneous criteria in the enforcement of the law throughout the country. This is achieved giving continuous training of the labor inspectors, by having common implementation manual and a similar software.<sup>49</sup> The inspectors visit the plants with the objective of evaluating the compliance with several dimensions of the labor laws and regulations. For example, they inspect the compliance with regulations related with registration, wages, FGTS, working hours or leave bene ts.

We have administrative data collected by the Ministry of Labor in 2002 at the city level for different variables: number of inspected fi rms in the city, number of fi nes issued in the city. In particular we have information on the number of fi nes for different labor violations: informal worker fi nes, are fi nes related with the fi rm not registering the worker for a work permit, work load fi nes are fi nes related with the fi rm not complying with the official work load, wage fi nes are fi nes related with the fi rm not paying the minimum established by the law, hours of work fi nes are fi nes related with the fi rm not complying with the number of hours of work and the mandatory pauses, FGTS fi nes are fi nes related with the fi rm not making the mandatory discounts to the FGTS, transport subsidy fi nes are fi nes related with fi rm not paying the mandatory transport subsidy, and other fi nes are fi nes related with other mandatory obligations of the fi rm to their workers. The total labor fi nes is simply the sum of all these fi nes. This information was aggregated from the city level to the subregion level using information provided by the Ministry of Labor on all the cities that belong to each subdelegacia. Finally, we also have information on the number of regional offices in each subregion.

Data for population in 2002 and for the total number of plants in 2002 is collected by the National Statistics Institute (IBGE) at the city level. City level data for GDP (1985), total population (1991) average years of schooling in the population with more than 25

> 48This is the case of the inspections related with formal labor contracts. However, for some speci fi c tasks, like anti slavery inspections, inspectors from different subdelegacias can gather to work in a team. 49At the end of 2002, there was a total of 2341 labor inspectors in Brazil which were distributed as follows for the federal regions that we cover in the Investment Climate Survey: 35 Amazonas, 103 Bahia, 129 Ceara, 71 Goias, 45 Maranhao, 205 Minas Gerais, 34 Mato Grosso, 99 Parana, 42 Paraiba 294 Rio de Janeiro, 137 Rio Grande do Sul , 73 Santa Catarina and 489 Sao Paulo.

28
In 2005, the primary net enrollment rate in Haiti was estimated to be about 50%, driven by large numbers of children out of school or over-age for their grade (e.g., in preschool rather than primary) (Cayemittes et al 2007). The contextual landscape in Haiti was unique: of all primary schools in the country, the 2002-2003 school census showed that more than 90 percent of them were non-public, accounting for more than 80 percent of enrolled students. If the country was to make progress toward the goal of universal primary education, it necessarily needed to work with these schools.

Non-public schools are a highly heterogeneous group. They include religious schools, community schools, schools started by non-government organizations (NGOs), and for-profit schools. The impetus for their creation varies. For some, the motivation was necessity: given the state’s limited ability to provide services, especially in rural areas, community leaders often organized to respond to the latent demand within the community for schooling for their children. Others were driven by a sense of duty and a desire to serve the less fortunate. Others still were driven by motives of profit, and took a more entrepreneurial approach to school creation. Regardless of their motivation, nearly all types of non-public schools were unified in their need to cover costs such as paying teachers, which they did through collecting tuition fees.

In 2007, with financial and technical support from the World Bank and the Caribbean Development Bank (CDB),<sup>1</sup> the Government of Haiti launched a new program aimed to make education free for poor Haitian children. The program, called the _Programme de Subvention_ (known as the “tuition waiver program”, or TWP, in English), provides an annual per-student payment to participating non-public schools that agree to not charge any form of tuition fees to students. The TWP was first rolled out in the Nippes and Artibonite departments in 2007.<sup>2,3</sup> The primary objective of the TWP is to reduce families’ financial barriers to sending children to school, and thereby increase the number of children coming to and staying in school. The theory of change behind the program is that if the costs of education are substantially reduced in non-public schools, this releases a binding demand-side constraint, enabling more parents to consistently send their children to school. The approach of focusing on non-public schools

> 1 The program would later be financed by other donors as well, including the Canadian International Development Agency (CIDA), the Global Partnership for Education (GPE), and the Inter-American Development Bank (IDB).

> 2 Departments are the first-level administrative division in Haiti.  The country is divided into 10 departments.

> 3 The TWP is part of the _Stratégie National D’Action/Education Pour Tous_ (SNA-EPT)

4
Figure 3

# SEIGNIORAGE AND INFLATION

<!-- Start of picture text --> ARGENTINA<br>0.12 ._200<br>0.09 _140<br>0.06 .120<br>0.04 4-_ A - _8 0P-e<br>- I '~~~~~~~~~~~~~~~4<br>0.02. A..' S .6r 0<br>092<br>.qy~~~~~~~~~~-0<br>19os 1S86 1987 1988 1989 1990<br><!-- End of picture text --> <!-- Start of picture text --> BRAZIL<br>.90<br>270<br>.0<br>iSr._ I .50 _<br>-Xo 0, 7[ 7 s / .- 40<br>.0<br>20<br>O.,<br>198 1986 1987 1988 1989 1990<br><!-- End of picture text --> <!-- Start of picture text --> PERU nfiaton was<br>097- 120<br>go, . . ____; _<br>70 TO ~~~~~~~_.__ i ___ -100 -o<br>60 ._e<br>so ._ . _ _ r<br>40 ___ 3V~~~~~~~~3<br>.0<br>lo 0<br>19D8 1S80 1987 19W 1909 1990<br>-inflalin - ueagnivrn<br><!-- End of picture text -->
- 12 -

account for other district-specific unobservables, a set of dummy variables representing project location are included. The data contain 155 rural road contracts; the summary statistics are shown in Table 1. The size of road works is fairly small. The average cost estimate is about NRs8.7 million or $120,000 per contract. The works aim at upgrading an 8.7 km, single lane road on average. As discussed, precipitation is significant in Nepal. Each project has more than 1,000 mm precipitation during the project implementation. Of particular note, the actual precipitation is 24 percent more than the predicted one by looking back at the same period last year. The predicted values by the AR models fit better; it rains only 3 percent more than expected.

||**Table 1. Summary**|**statisti**|**cs**||||
|---|---|---|---|---|---|---|
|Variable|Abbreviation|Obs|Mean|Std. Dev.|Min|Max|
|Cost overrun rate (percent)|_overrun_|155|3.7|<br>6.3|-14.8|21.7|
|Project delay (days)|_delay_|155|105.5|<br>206.5|-76.0|1217|
|Actual precipitation during the original<br>project period (mm)|_rain_<sup>_a_</sup>|152|1092|<br>974|0|4858|
|Actual precipitation during the actual|_rain_<sup>_t_</sup>|155|1652|<br>1724|0|8774|
|project period (mm)|||||||
|Precipitation during the original project|_rain_<sup>_e_</sup>|153|1163|<br>937|0|3771|
|period predicted by looking back (mm)|||||||
|<br>Precipitation during the original project<br>period predicted by AR model (mm)|_rain_<sup>_eAR_</sup>|145|1213|<br>971|16|4819|
|Memorandum items:|||||||
|_rain_<sup>_t_</sup>_/ rain_<sup>_a_</sup>||146|2.56|<br>4.13|0.00|23.40|
|_rain_<sup>_a_</sup>_/ rain_<sup>_e_</sup>||146|1.24|<br>1.81|0.17|19.06|
|_rain_<sup>_a_</sup>_/ rain_<sup>_eAR_</sup>||145|1.03|<br>0.71|0.14|5.62|
|Engineering cost estimate (NRs million)|_cost_|155|8.7|<br>8.6|0.2|39.4|
|Length of roads (km)|_length_|155|7.4|<br>5.0|0.2|34.0|
|Number of lanes|_lane_|155|1.0|<br>0.2|1.0|2.0|
|Thickness of road surface (mm)|_thickness_|155|6.7|<br>14.5|0|150.0|
|Gravel (m3)|_gravel_|155|2036|<br>2497|0|18600|
|Bitumen (kg)|_bitumen_|155|3386|<br>11942|0|79030|
|Earthworks (m3)|_earth_|155|7615|<br>11622|0|93403|
|Brickworks (m3)|_brick_|155|62|<br>161|0|1204|
|Gabion (m3)|_gabion_|155|241|<br>742|0|8400|
|Excavation (m3)|_excavation_|155|2448|<br>4840|0|29267|
|<br>Cement concrete (m3)|_cement_|155|37|<br>72|0|508|
|Dummy variable for postqualification of<br>bids|D(_postqualify_)|155|0.9|<br>0.3|0|1|
# selected for the empirical application'.

Note that the expected cost E(C,) associated with the pressure to spend effect has a functional forr. which is consistent with the functional form selected for the corresponding constraint in the sense that its expected value is zero whenever the constraint is assumed to be non binding (with S - 0) and that it is an increasing function of the quantity on which the constraint is imposed$. This suggests that the expected cost E(C,) should be expressed as the product of an indicator function (indicating whether or not a crisis occurs) and of a measure of the seriousness of the crisis, for example, the square of the difference between the government's choseni and the constituents' desired policy.

# _Limited disinvestment or policy_ reversal effect

The marked increase in government expenditures which typically follows a commodity boom can, for example, translate into investment spending on projects or into an increase in the size (and/or the wage) of the public labor force. Once this spending pattern is in place, it may become difficult to reverse. The government finds itself trapped by recurrent expenditures whivh are inflexible or even irreversible. Furthermore, the longer the 7It leads to a more tractable version of the model aLnce the functional form for the cost (convex) does not have to be a probability.

$Thus, C, Is related to thc strength of the pressure to spend effect and as bich measures the extent to which the corresponding constraint is binding.

9
# EXECUTIVE SUMMARY

Developing countries, particularly those in Asia, are fast adopting industrial pollution control standards similar to those in developed countries. However, formal regulation has been greatly hampered by the absence of clear and legally binding rules; limited institutional capacity; lack of appropriate equipment and trained personnel; and inadequate information on emissions. At present, the government-imposed 'price of pollution' is nearly zero for many manufacturing facilities in these economies.

A conventional analysis would predict highly pollutionintensive production under such conditions. However, our research has uncovered strongly contradictory evidence. Despite weak or nonexistent formal regulation and enforcement, there are many clean plants in the developing countries of South and Southeast Asia. Of course, there are also many plants which are among the world's most serious polluters. What explains such extreme interplant variation? This paper reviews the evidence from a survey of pollution abatement by 26 pulp and paper plants in four countries: Bangladesh, India, Indonesia and Thailand.

Our analysis incorporates three sets of factors which may affect the pollution intensity of an industrial process: Relevant **plant characteristics** may include choice of technology, vintage, ownership, management quality, available human resources and technical expertise. Firms' responsiveness to pressure for abatement may also vary significantly with **economic considerations:** input prices, profitability, market characteristics, availability of information on abatement technology, and financing possibilities. Finally, plants may adapt to **external pressure** from government regulators, buyers or investors, and neighboring communities which suffer damage from pollution.

Our results show that all three factors play significant roles. Abatement is positively associated with scale and competitiveness; negatively associated with pubic ownership; and unaffected by foreign links (in ownership or financing). Community pressure, or informal regulation, emerges as a clear source of interplant differences. Under some circumstances, communities successfully pressure plants to abate even if little or no support is available from formal regulation. Our results suggest that local income is a powerful predictor of effective informal regulation. We also find that existing formal regulation has measurably beneficial effects, even when it is weakly-developed.

i
Figure 4 also underscores the importance of examining these relationships by income and age — among poor households, younger women and men are equally likely to want to work additional paid hours. This diverges substantially, however, among groups aged 35 and older, likely due to women’s greater responsibilities within the home, including care of children. Also, some women may want to work more but are time constrained, so may consider themselves as neither available nor willing to work given their circumstances. As discussed further in Section V, asking additional contextual questions on reasons for wanting/not wanting to work more, including factors related to the quality of available economic opportunities as well as time constraints, can help in better understanding the trends in Figure 4.

Figure 4. Uganda National Panel Survey, 2013-14: relationship between age and desire to work more (own-reporting sample) <!-- Start of picture text --> Households below the poverty line Households above the poverty line<br><!-- End of picture text --> Finally, to complement statistics on unemployment, the 19<sup>th</sup> ICLS also introduced the concept of the potential labor force to provide additional data on those out of the labor force, but who could be responsive to employment policy. This includes (a) unavailable jobseekers, who are seeking employment but not available; (b) available potential jobseekers, who are available but not seeking; and (c) willing potential jobseekers, who are neither seeking nor available for employment, but who want employment.

A question on desire to work, in particular, is critical for capturing (c) – and with important implications for women, who may be out of the labor force and not actively seeking work<sup>5</sup> because of unpaid work burdens, lack of suitable economic opportunities, not being allowed to work/other barriers to seeking work, or other social/cultural norms affecting their time allocation (see Section V as well). The ILO LFS study found, for example, that a much higher percentage of women cited family responsibilities as a reason for not searching for employment (Walsh, 2018). Wording can also matter — the ILO LFS study found that more women tended to report job search when the question on search was split into two (seeking a job, and seeking to start their own business) as opposed to a single question on seeking work for pay or profit (Benes and Walsh, 2018c).

> 5 19th ICLS defines the concept of unemployment as “active job search by persons not in employment who are available to work.”

12
# **1. Introduction**

The volume of corporate debt has rapidly expanded worldwide since the early 1990s, growing faster than equity financing and gross domestic product (GDP). This growth has occurred not only in one, but in several debt markets that have become key sources of external finance for corporations. Evidence from the United States shows that most publicly listed firms borrow simultaneously from banks and bond markets, with bonds accounting for about 63 percent of the total domestic debt outstanding by the non-financial sector (Rauh and Sufi, 2010; Crouzet, 2018). Among bank loans, syndicated lending has become the main alternative to bond markets, especially for large corporations (Ivashina and Scharfstein, 2010; Cerutti et al., 2015). The borrowing activity through bonds and syndicated loans has taken place in both domestic and international markets (Henderson et al., 2006; Hale, 2007; Gozzi et al., 2015; Claessens, 2016).

Researchers have exploited the corporate debt issuance data in different markets to document how publicly listed companies in the United States substituted domestically issued syndicated loans for bonds to withstand negative shocks to the supply of bank credit (Adrian et al., 2013; Becker and Ivashina, 2014). These papers build on the idea that domestic bond financing can replace domestic bank lending during banking crises, when capital markets might act as a “spare tire” (Greenspan, 1999; Levine et al., 2016). Other research highlights that different markets are not perfectly integrated and provide different types of financing, even for the same borrower (La Porta et al., 1997; Karolyi and Stulz, 2003; Pirinsky and Wang, 2006; Japelli and Pagano, 2008; Bekaert et al., 2011; Gozzi et al., 2015; Crouzet, 2018). Hence, changes in financing across markets during supply contractions can have important implications both for the amount of credit obtained and for other debt attributes.

In this paper, we expand the existing literature by studying whether firms from around the world reacted to well-known financial crises by switching across debt markets. We examine substitutions not only between domestic syndicated loans and domestic bonds, as the existing 1
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE A number of greenfield investments have been made to develop the communications backbone in several countries. A one-time investment in Senegal is resulting in a 34 percent rate of return annually. In several other countries such as Burkina Faso, Niger, and Nigeria, the greenfiled invesments that are in place have had an impressive rate of return. Yet when the gaps in the backbone and connectivity are bridged, the rate of return will escalate. For example, in Burkina

**Table 4.8  Intercontinental and intraregional spending needs for ECOWAS for 10 years**

||Intercontinent<br>Projects|al connectivity<br>Required annual<br>investment (US$ millions)|
|---|---|---|
|East Africa|EASSy, TEAMS|260|
|Southern Africa|Infraco, SRII|510|
|Central Africa|Infinity, GLO-1,|1010|
|West Africa|WAFS|,|
|Total, Sub-Saharan Africa||1,780|
|Source: Mayer and others 2009.|||

Faso, a $6 million investment to address ICT gaps will lead to a 56 percent rate of return on this investment annually (table 4.9).

**Table 4.9  Summary of cost-benefit analysis of regional integration**

||Fixed bro<br>subscriptio|adband<br>ns ̳000s|Monthly b<br>(US$ milli|enefit<br>ons)|Investme<br>(US$ mi|nt need<br>llions)|Rate of r|eturn (%)|
|---|---|---|---|---|---|---|---|---|
|||||||Remaining||Remaining|
||Before|After|Before|After|Greenfield|<br>gap|Greenfield|<br>gap|
|Benin|2.7|7.4|0.12|0.10|2.0||134||
|Burkina Faso|4.5|11.5|0.15|0.12|19.6|5.9|17|56|
|Cape Verde|7.4|8.3|0.10|0.01|0.0||||
|Côte d'Ivoire|10.0|74.8|0.17|0.54|27.1|2.5|31|337|
|Gambia|0.3|1.4|0.01|0.01|5.2||5||
|Ghana|23.0|55.9|0.38|0.27|8.1|5.7|97|139|
|Guinea||...|0.00|0.00|7.7|7.8|||
|Guinea-<br>Bissau|-|0.3|0.00|0.00|4.9|3.1|||
|Liberia|...|0.5|0.00|0.00|10.6|10.3|||
|Mali|5.3|20.4|0.11|0.16|26.3||12||
|Niger|0.6|2.3|0.06|0.09|11.6|2.0|15|86|
|Nigeria|67.8|258.0|2.67|3.74|59.3|5.4|130|1,425|
|Senegal|47.4|49.2|0.71|0.01|25.6||34||
|Sierra Leone||1.4|0.00|0.00|8.8|8.8|||
|Togo|1.9|1.8|0.13|0.00|4.4||36||

64
decisionmaking – a study by Corsetti et. al. (2011) using data from the U.S. detects significant spending reversals (falling below trend levels) that follow periods of stimulus in the country‘s economic history. These spending reversals, likely motivated by an effort to stabilize the country‘s debt, also affect public expectations profoundly through subsequent cycles. Understanding how exactly government stimulus plays out into different economic sectors, however, hinges on reliable estimates of the multiplier effects of government spending, which can vary considerably across models (Auerbach et. al. 2010).

There have been few studies that examine the effects of the 2008 crisis on households in low- and middle-income countries.<sup>1</sup> In the case of East Asia, the most-recent relevant studies have typically looked at the 1997 financial crisis in which the external environment was much more favorable and it was possible to stimulate the crisis-hit economies through sharp increases in net exports. The distributional impacts of that crisis are particularly interesting. Bresciani et al. (2002), for example, studied the impact of the Asian financial crisis of 1997-98 on farm households in Thailand and Indonesia; they found that cross-country effects on similar household demographic groups can vary considerably - poor farmers were hard hit in Thailand, but not in Indonesia, and that in both countries farmers specializing in export crops benefited from the currency devaluation associated with that crisis. Friedman and Levinsohn (2002) also find, as in the case of Thailand, that following the crisis in Indonesia, the urban poor were hit the hardest compared to the rural poor, who being able to produce food were able to absorb some of the effects of rapid inflation.

Although the broad issue we are addressing – household responses to shocks and the role that policy might play in softening the effects of these shocks – is of universal importance, our study focuses on just one country, Thailand. This choice is driven not only by its exposure to the 2008 crisis as a heavily export-oriented economy, but also largely by data availability. In this paper, we examine recent quantitative evidence on the household response to the crisis from Thailand using

> 1 Much of the literature on the 2008 crisis is descriptive, including, for example, a recent study from September 2009 by the Economic and Social Commission for Asia and the Pacific on the ―Impact of the Economic Crisis on Poverty and Inclusive Development: Policy Responses and Options‖ (UNESC 2009).

4