rafmacalaba/datause-encoder-data huggingface dataset:
seed husks and stalks (making use of plant residues not needed for food production) and fastgrowing grasses and trees (Doornbosch and Steenblik, 2007). Lignocellulosic biomass is comprised of polysaccharides (cellulose and hemicellulose), which are converted into sugars through hydrolysis or chemical (or combined) processes; the sugars are then fermented into ethanol using existing fermentation technology<sup>5</sup> . While no commercial scale lignocellulosic ethanol plants are operational as of early 2008, around 15–20 companies, mostly in the US, are involved in pilot plant studies with different biotechnological and thermo-chemical biomass conversion routes (OECD, 2008).

The use of microalgae for biodiesel production appears to be a very promising future technology (Christi, 2008) since 80% or more of the dry weight of algae biomass, compared to 5% for some food crops, may be retrieved as oil for some species (Christi, 2007). They also create little pressure on arable land because they can be cultivated in a wide variety of conditions, even in salt water and water from polluted aquifers (GBEP, 2008).

# **3. Production, Consumption and Trade**

# _3.1 Production and Consumption_

Global production of fuel ethanol grew from 30.8 billion liters in 2004 to over 67 billion liters in 2008 at an average annual growth rate of 22%. The two leading producers, the US and Brazil, accounted for more than 90% of the total in 2008 (see Figure 1).

> 5 There exists another approach for converting lignocellulosic biomass into biofuels. This approach involves the gasification of the feedstock to produce synthetic gas or syngas (a mixture of mainly CO, H2, CO2 and water vapor, as well as some light hydrocarbons and other volatile and condensable compounds). This syngas is then converted to a variety of fuels, such as synthetic diesel, through Fischer-Tropsch synthesis, the same technology used in gas-toliquids and coal-to-liquids plants (Worldwatch, 2007).

5
devote their implementation time towards more specific export-oriented activities, such as studying how to enter the Ecuadorian market, or offering advice on talking with potential clients in the United States.

The **Operational Productivity consulting** appears to have worked primarily on efforts to standardize processes, reduce bottlenecks, and cut stoppages and dead time. One of the key tools used was value-stream mapping (VSM), a lean manufacturing method that maps the flow of materials through production and the time taken for each step. For example, in one factory we visited, the consultant had gone through with the firm to outline step-by-step in the production process the amount of time, manpower, and moving distance involved in each step. They then suggested how improvements could be made by reducing the number of times an item gets moved back and forward in production.

Some firms noted that these approaches had helped them with production efficiency. However, several challenges arose. A first one is that the methodology may not be well suited for all firms, especially multi-product firms making a wide variety of products and where the production steps may differ across products. One textile firm we visited noted that after going through the VSM process, they concluded that it was not working that well for their purposes, since the production flow changes a lot from clothing item to item. A second potential drawback is that these activities often took place before the firm worked on its commercial strategy, so that the focus was on lowering cost and improving efficiency for a particular existing product, but not, for example, “we should focus more on developing a higher quality brand if we want to export to the US/Europe, using organic products and better packaging, and then design the production process around this”. This is a general challenge for the program, since different consulting firms are contracted to work on the different types of management improvement.

The **Labor Productivity consulting** appears to have focused largely on helping firms retain talent and improving worker morale. It was recommended that firms do an employee climate survey, to get a sense of employee views, attitudes towards the organization, and issues faced. A common issue identified was that production-level workers did not feel that involved in decision-making, and so in several firms a recommendation was made to start each day with a short line-level meeting where workers could talk about production indicators and any problems in operations. Other firms introduced worker suggestion boxes or other ways for workers to give feedback. Firms were also advised to introduce non-monetary recognition programs, and consider other morale-boosting activities like social events.

Some of the firms we spoke to noted that the consultants lacked sector-specific knowledge about specific labor force issues in their industries, and that a challenge was that some of the ways to motivate labor productivity depended on first having sorted out operational productivity indicators first. The sequencing and coordination with the operational 48
and expenditure aggregates at the household level. This is not a trivial point because the varying assumptions and methodologies used in doing this can make a tremendous difference to the final outcomes. As an illustration, we show in Tables 1 and 2 total household income and expenditure and their regional distributions as calculated by various autho1s, but based on exactly the same raw data. The differences are striking, not only in the country-wide means, but also in the regional distributions. For example, Kanbur (1990) estimates the average expenditure level in Abidjan as 86% above the country average, while Grootaert (1992) estimates this difference as 58%, even though the Abidjan mean is about the same in both studies. The estimates of Kanbur (1990) and Glewwe (1987) of welfare in Savannah are almost 50% lower than Grootaert's estimates (Table 1). On the income side, Kozel's (1990) estimate of country-wide mean household income is one third higher than that of Grootaert, and her estimate for Abidjan is two third& higher (Table 2).

Under such conditions, transparency of methodology becomes very important, and it is essential that analysts provide explicit information on how their incomne and expenditure aggregates were constructed. In the poverty research project underlying t;,:s paper, the methodology was documented in detail in Johnson, McKay and Round (1990) and Oh and Venkataraman (1992).

# 3.2 Data Quality

There is a fairly general consensus among the various users of the data that the quality of the CILSS results is good. Some attempts have been made to validate the CILSS results with other data sources (population census and other household surveys). While in general those comparisons were favorable (see Grootaert, 1992), they need to be treated with caution since it is not always obvious that the reference data are of better quality than the CILSS.

Nevertheless, the quality of the CILSS data is not uniform across the different modules of the survey. We found two weaknesses. First, the information on non-farm 4
Table 2 illustrates the challenges inherent in the long run growth dynamics when the public (government) savings rate is constrained. It presents a decomposition of the various factors contributing to growth in the Optimistic Scenario with government savings rising to its target of 1.5 percent of GDP. One of the striking features is the negative contribution to growth from the labor force for most of the simulation period because the working age population is expected to experience a decline until at least 2020.

**Table 2. Sri Lanka: Growth Simulation results with TFP g=2.5%/year and Government Savings reaching 1.5% of GDP by 2012**

|||<br>Y|<br>ear||
|---|---|---|---|---|
||2012|2015|2020|2030|
|Real GDP growth|8.1|7.1|5.7|4.7|
|GDP per capita growth|7.3|6.4|5.3|4.5|
|**_Contribution to per capita GDP growth_**<br>**a**|||||
|TFP|2.5|2.5|2.5|2.5|
|Physical Capital|4.6|3.8|2.7|1.8|
|Human Capital|0.2|0.3|0.2|0.2|
|Labor Force|-0.1|-0.1|-0.1|0.0|

Source: Authors' calculations based on Hevia and Loayza 2011. a Percentage points In sum, the realization of an 8 percent growth rate sustained over the long term will require significant increases in the national savings rate. Achieving high rates of TFP growth and increasing government savings beyond levels currently contemplated will make it easier to achieve the required savings rates—the first by lowering the need for capital accumulation and hence the savings rate itself; and the second by putting less pressure on private savings rates. Such effort will help offset the diminishing returns to capital and adverse effects of Sri Lanka‘s demographics. The challenge then is to create conditions conducive to large and sustained productivity improvements in the context of lower public indebtedness and an attractive investment climate. The next section discusses the micro foundations of growth.

24
# **1. Introduction**

Despite decades of retrenchment, the entrepreneurial state’s footprint is notable across many countries’ economies. Such prevalence is not just concentrated in sectors where government intervention is rationalized on the grounds of market failures, coordination problems, and increasing returns to scale. By contrast, it is widespread across all sectors of the economy, with 70% of government-owned enterprises operating in competitive industries, such as food, construction, and hospitality, typically dominated by private sector firms. Even when operating in sectors subject to market failures, state-owned enterprises (SOEs) may crowd out resources from more efficient private-owned firms (POEs). Thereby reducing aggregate productivity and output.

In this paper, we study one particular source of interference from governmentowned enterprises: the competition with private firms for external financing in capital markets. There are several well-understood mechanisms through which less efficient firms with a large government stake could access external financing more favorably than private counterparts, thereby distorting finance, innovation, investment, and resource allocation. These range from lower debt issuance costs due to the federal government’s backing of corporate debt to direct budget support from the government in the form of internal equity. While the mechanisms are well acknowledged, there is less clear understanding of their quantitative significance. Do capital markets favor SOEs with lower debt and equity financing costs? If so, how large is their financial premium? And what are the aggregate implications on productivity?

To study these questions, we appeal to Whited and Zhao’s (2021) methodology for inferring financial distortions from firms’ balance sheet data and apply it to 24 European countries from 2010-2016. Mirroring the real misallocation literature (Hsieh and Klenow, 2009), the methodology delivers a distribution of firm-specific financial distortions resulting from deviations in the rates of marginal revenue products of debt and equity across firms, the properties of which we characterize across state- and private-owned firms. We find that firms with public authorities as direct shareholders (SOEs) get, on average, subsidized access to debt and equity compared to private2
Finally, this analysis produces the following eight sets of predicted occupational placement indicators that will be used in section 3.2. to assess US skilled immigrants’ performance over time and by country: (1)ÔP1980,τ , ÔP1990,τ , ÔP2000,τ , ΔÔP1980-1990,τ , ΔÔP1990-2000,τ , for the hypothetical individual who arrived in the 1970s; (2) ÔP1990,τ , ÔP2000,τ , ΔÔP1990-2000,τ , for the hypothetical individual who arrived in the 1980s. _τ_ stands for country of origin and takes values from 1 to 122.

# **_3.2. DESCRIPTION OF THE US CENSUS DATA_**

This paper relies on data from the 5% IPUMS samples of the US Censuses 1980, 1990 and 2000<sup>24</sup> and focuses on employed male immigrants, not living in “group quarters”, not enrolled in school as of 1980, 1990 or 2000, and whose reported years of immigration lie within the 1970-1989 interval. The sample in Census 1980 is random, each observation representing 20 individuals in the US population, while Censuses 1990 and 2000 are weighted so that each observation represents a different number of individuals in the US. To increase the over time structural stability of cohorts, I focus only on people who earned their degrees in the origin country. Since a variable indicating the place where education was obtained is not directly available in either census, I construct it using the number of years of education and the age at migration. A person is considered “US educated” if he arrived in the US before he would have normally acquired their declared education level. For example, if a university graduate arrived at the age of 23 or older, then he is considered “foreign educated”.

Censuses report occupation only for people aged 16 or more. When following the 1980s cohort through 1990 and 2000 I set the minimum age to 16 for Census 1990 and 26 for Census 2000, whereas when analyzing individuals arriving in the 1970s, whose performances can be captured by all three censuses, the minimum ages are defined as 16, 26 and 36 for Censuses 1980, 1990 and 2000, respectively. To reduce the effect of cohorts thinning out across censuses because of older individuals, I also place upper limits on the age intervals used in the samples: 40 for Census 1980, 50 for Census 1990 and 60 for Census 2000.

Summary statistics including size of cohorts, weighted and not-weighted, and information on sample structure by age, years since in the US and education are available in Table 2. The first three columns refer to the individuals who arrived in the US during the 1970s and whose performance levels are captured by Censuses 1970, 1980, 1990. Arrivals in the 1980s are summarized in columns 4 and 5. The first row indicates the size of the five samples, while the

> 24 Available at ipums.org

15
self-employed workers are typically characterized by “voluntary entry to informality,” while informal salaried workers are more likely to be excluded from the formal sector.

More recently, several authors have developed and estimated dynamic discrete choice models to study the impact of trade liberalization on labor reallocations, wages and welfare<sup>1</sup> . Artuc _et al_ . (2010) study the impact of trade liberalization on the US labor market. The authors find low labor mobility in response to trade liberalization. They simulated a 30% reduction of tariffs in the manufacturing sector, and found that 95% of the labor reallocation in the entire economy would take place in eight years after the reform. This growing literature, however, has focused on worker flows across industries ignoring one of the main characteristics of labor markets in developing countries, the existence of a large informal sector. This paper seeks to contribute to the literature by explicitly modeling workers’ decision to move both across sectors and employment status.

The model proposed herein is empirically tractable, requiring only worker employment transitions across industries and employment status from labor-force surveys with panel data. With these estimates of labor mobility costs, we use simulations of changes in labor demand across sectors to study labor-market adjustments caused by a reduction in the relative price of manufactures that could be brought about by trade liberalization or other trade-related persistent shocks, such as the growth of Chinese exports in global markets. The results suggest that inter-industry labor mobility costs are large but entry costs into informal employment tends to be significantly lower than the costs of entry into formal employment. Consequently, declines in prices raise real wages and thus increase labor flows into the labor market but with disproportionate entry into informal employment. The share of informal employment rises after trade liberalization (or other price-reducing shocks) but this is due predominantly to entry into the labor market rather than displacement out of the affected industry.

> 1See Artuc _et al_ (2010); Dix-Carneiro (2010); and Menezes-Filho and Muendler (2011).

3
# _Toronto_

Toronto spent over US$7.7 billion in 2005. Although the City is required to balance its operating budget (as are all cities in Canada), it nevertheless faces a mismatch between its expenditure responsibilities and revenue-raising tools. The mismatch occurs because the property tax funds a significant amount of redistributive services such as health, social services, and social housing. These services are not appropriately funded by the property tax but rather should be funded by more progressive taxes such as the income tax.<sup>21</sup> Table 4 shows the distribution of operating expenditures and revenues in Toronto in 2005. The main expenditures that the city makes are for transportation (roads and transit), social services and social housing, and water and sewers. The main source of revenue is the property tax which accounts for 41 percent of total revenues. Other major revenue sources are user fees and intergovernmental transfers.

**<u>Table 4: Distribution of Municipal Operating Expenditures and Revenues, Toronto, 2005</u>**

|**Expenditures**|**%**|**Revenues**|**%**|
|---|---|---|---|
|General government|5.3|Property and related taxes|41.0|
|Fire|4.3|User fees|20.6|
|Police|9.9|Federal and provincial transfers|20.1|
|Roads and transit|19.2|Licenses and permits|5.9|
|Water, sewers and solid waste|16.3|Other revenues|12.4|
|Health|4.0|Total|100.0|
|Social services and social housing|29.8|||
|Recreation and culture|8.7|||
|Planning and development|0.8|||
|Other|0.7|||
|Total|100.0|||

Source: Ontario Ministry of Municipal Affairs and Housing, Financial Information Returns

> 21  In other parts of the country, social services and social housing are funded by the provincial level of government.

54
# **_3.3 The role of institutions_**

One factor of crucial importance in determining the availability of international trade finance is the legal system in which trading countries operate. Inefficiency of the legal system, in the form of inadequate contract law or bankruptcy law, or inefficient judicial system, increases enforcement costs and thus commercial risk. This affects the cost and the availability of financing, thus hampering international trade.

Which is the effect of the legal system on inter-firm credit? A number of papers (Demirgüç-Kunt and Maksimovic, 2001; Beck _et al.,_ 2008) find evidence that trade credit is relatively more prevalent in countries with worse legal institutions and lower investor protection. This may seem puzzling as one may expect that better legal institutions facilitate all types of borrowing, including trade credit. It can nevertheless be explained with the fact that, unlike financial intermediaries, trade creditors may be able to more effectively enforce contracts without resort to the legal system by stopping future supplies (this may be particularly relevant when the good supplied is specific or when it is hard to find alternative suppliers).<sup>13</sup> This intuition seems to be confirmed by Johnson _et al._ (2002), who, based on survey data collected in transition countries in 1997 for small and medium-sized manufacturing firms, find that ongoing relationships are more likely to be preserved when goods are complex and assets are specific and when it is difficult for customers to resort to alternative suppliers.

According to some studies (La Porta _et al_ ., 1998), the differences in the efficiency of the legal system observed across countries might be related to their different legal origin. More precisely, countries belonging to the common law tradition are found to have a more efficient judicial system, and thus lower enforcement costs, as compared with those belonging to the civil law tradition. Djankov _et al._ (2003) provide an example of the importance of the legal system in shaping the efficiency of judicial systems by constructing for a variety of countries an index of _procedural formalism of dispute resolution_ .<sup>14</sup> The authors find that judicial formalism is systematically greater in civil law countries than in common law countries and that higher formalism is a strong predictor of poorer enforceability of contracts and longer duration of dispute resolution.

Another expression of a country’s legal system is the bankruptcy law, and debtorcreditor law more generally. Its formal structure, actual effectiveness, length and practical operation differ greatly across countries. A measure of efficiency of the bankruptcy regimes is provided by Djankov _et al._ (2008) for 88 high and middle-

> 13 In line with Cuñat (2007).

> 14 This index is constructed describing the exact procedures used in 109 countries by litigants and courts to evict a tenant for nonpayment of rent and to collect a bounced check. It measures the extent to which regulation causes disputes resolution to deviate from a benchmark of a third party informally resolving the dispute on fairness grounds.

13
- 57 -

Table 24: Major Poverty Alleviation Programs, 1987-88

|A. Financial Expenditures|Rs million|
|---|---|
|1. Credit-based self-employment programs:||
|a. Integrated Rural Development Program (IRDP)*<br>(Investment per beneficiary=Rs4.471)|19,000|
|2. Wage employment programs:||
|a. National Rural Employment Program (NREP)|7,850|
|b. Rural Landless Employment Guarantee Program (RLEGP)|6,480|
|c. Maharashtra's Employment Guarantee Scheme (MEGS)|2,650|
|3. Area development programs:||
|a. Drought Prone Area Program|900|
|b. Desert Development|500|
|c. Watershed Development|2,400|
||39,780|
|As percent of GDP|1 percent|
|As percent of Plan Outlay|9 percent|
|B. Physical Achievements, 1987-88||
|1. Credit-based self-employment programs:|1987-88|
|a. IRDP: Number of beneficiaries covered (milLion)|4.2|
|2. Wage employment programs:||
|a. NREP: Mandays of employment generated (million)|370|
|b. RLEGP: Mandays of employment generated (million)|100|
|c. MEGS: Mandays of employment generated (million)|150|

Source: Progress Reports of the 20th Point Program, Ministry of Programme Evaluation, 1988 and 1989.

"crossing the poverty line" is inappropriate for judging the full benefits<sup>of</sup> this program, since households way below the poverty line (the ultra<sup>poor) may</sup> register incremental incomes and thus benefit from the program, even<sup>if they</sup> are unable to cross the poverty-income threshold.-71 Pulley (1989)<sup>has shown</sup>

- 17/ To illustrate, consider a state which assisted households close to the poverty line and helped them all cross the line, with another state that selected households way below the poverty line (i.e. the ultra<sup>poor), and</sup> helped their incomes grow, but not sufficiently enough to cross the poverty line. Previous IRDP evaluations hailed the former<sup>state and</sup> condemned the latter, following the criterion of "crossing<sup>the poverty</sup> line". Gaiha's (1989) critique misses this important consideration<sup>in</sup> the assessment of program effectiveness.