rafmacalaba/datause-encoder-data huggingface dataset:
18 TABLE 4 ARGENTINA, PERU AND BRAZIL HYPERINFLATIONS /1 4 s% GDP)l

|_<br>ARGENTINA<br>1985|42.20|38.82|22.04|0.91660|672.2|
|---|---|---|---|---|---|
|1988|39.18|36.90|21.98|0.94334|90.1|
|167|39.28|33.75|20.81|0.85922|131.3|
|1966|34.08|28.97|16.21|0.85006|343.0|
|1|35.66|26.83|17.39|0.60847|179.9|
|1|34.20|30.50|16.64|0.89181|480.0|
|III|30.50|27.93|15.97|0.91394|954.8|
|IV|36.47|29.19|15.90|0.80038|220.2|
|1980|30.65|27.58|18.16|0.89400|3079.8|
|1|37.15|28.86|15.74|0.77685|189.2|
|1|36.66|26.76|11.52|0.69183|12459.0|
|III|22.49|24.06|14.71|1.06981|198171.2|
|IV|39.01|30.27|17.80|0.84060|350.9|
|1990|20.06|28.07|16.95|1.07713|2314.0|
|1|22.62|21.23|12.35|0.93655|35399.7|
|1|31.28|29.27|17.40|0.93574|1807.3|
|III|30.59|26.88|17.28|0.94410|352.0|
|IV|29.52|26.51|17.13|0.96579|192.1|
|PERU<br>1985|23.50|14.60|14.30|0.62979|163.4|
|1966|21.30|12.60|12.20|0.59155|77.9|
|1987|16.20|9.20|9.30|0.50549|85.8|
|1986|15.60|9.20|9.10|0.5s974|687.0|
|I|18.90|10.90|11.20|0.57672|310.4|
|it|14.00|9.10|9.50|0.65000|424.2|
|III|17.50|7.70|7.90|0.44000|2615.3|
|IV|14.70|9.70|9.30|0.6es8s|8501.3|
|1989|13.20|6.00|0.50|0.454|33398.6|
|1|14.40|9.10|9.40|0.63194|6630.6|
|1I|12.70|7.70|7.90|0.60o|4579.9|
|III|12.40|5.50|6.00|S 0.44355|1362.4|
|IV|13.60|5.70|0.20|0.41912|1527.8|
|1990|14.50|7.80|7.90|0.53793|7481.7|
|1|12.70|4.80|5.00|0.37795|2403.8|
|If|21.30|5.40|5.70|0.25352|3728.5|
|III|13.70|.60|6.70|0.46175|524510.2|
|IV|14.40|8.60|8.70|0.59722|942.4|
|BRAZIL<br>196|29.20|27.10|20.30|0.92608|145.2|
|1967|31.90|27.00|18.O|0.4639|229.7|
|1996|30.70|2830|17.80|0.92182|682.3|
|1989|34.60|26.20|18.40|0.75072|1287.0|
|1990|32.70|31.50|23.90|0.96o30|2937.8|
|1991/4|28.30|27.60|20.30|0.97527|440.8|

# _SOURCES:_

_ARGENINA l Minkt"y ot Economy. PERU, Central Bnk of Peru. BRAZIL, BrazilRacnt economic DOlopmeit /t : Consolidated Public Seor (Brazil and Argentina) Csntral Govemmt (Pe ru) /2 : Total renes exceptfotr Pel tony the current reenue. 13 : The quaterly detae m annumllaed. /4_ : _lotW d_
As mentioned earlier, the vast majority of emerging countries have far below 1000 patents and the mean is a mere fraction of that found in the advanced countries which never shows any zeros.

Table 7 breaks the sample into OECD and developing countries.<sup>18</sup> We again report the results for our two preferred estimators although we now have clearer reasons to choose one over the other. Of the 6.5 % of zero patent values in the sample, all are found among the developing countries. Hence, in theory, the LFM estimator should be preferable for this sample and the GMM for the advanced countries. In practice, there is little difference in the estimates for the OECD-.95 for the LFM and 1.04 for the GMMoffering somewhat stronger evidence for constant returns in the advanced countries.

The suspicion that, for some reason, the higher returns to scale arise from using aggregate as opposed to private sector R&D spending turns out not to be justified. Using a reduced sample for the OECD of countries with complete data on R&D done by the productive sector versus the non productive sector (including government) still generates an elasticity very close to unity for both aggregate- and productive-sector investment.

Since our aggregate sample spans a wide variety of sectors and countries which cannot be seen as the aggregate counterpart to the US manufacturing firm level data, asserting that the gap between this finding and the bulk of the micro findings captures spillovers is probably too heroic. Nor does a finding of constant returns to scale in the production of patents necessarily imply the same is true of knowledge generally. Still, the robustness to technique, sample and proxy for variable of the CRS finding for the OECD sample is suggestive that there we cannot rule out scale economies emerging at the aggregate level that may offset the increasing difficulty and/or patent race effects that might be found at the level of the firm.

The results from the developing countries are strikingly different, however, with the LFM for the developing countries, yielding a coefficient of .7 and the probably

> 18 OECD includes Korea but excludes Mexico which looks far more like the poor country sample.

15
|Any part of wages transferred off the record?|-|-0.00914<br>(0.308)|0.458*<br>(0.274)||-0.219**<br>(0.105)|-0.319***<br>(0.123)|
|---|---|---|---|---|---|---|
|Constant|3.537***|1.383|1.744|0.691***|-1.298**|-1.817***|
||(0.582)|(1.067)|(1.093)|(0.251)|(0.604)|(0.674)|
|Observations|5242|4662|4134|5247|4666|4137|
|Work conditions|N|Y|Y|N|Y|Y|
|Wages|N|Y|Y|N|Y|Y|
|Region FE|Y|Y|Y|Y|Y|Y|

Source: Authors’ estimation.

Note: Columns (3) and (6) exclude self-employed informals.

This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their self-assessed health, measured as good or poor health, controlling for other factors. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed).. Poor health is defined as bad or very bad health (out of a 5-point scale). Good health is defined as good or very good health. All specifications include region dummies. Omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10) and hours worked (<40 hours). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively.

60 Informal Employment and Worker’s Wellbeing in Russia / June 2019
Other country-specific factors are somewhat more difficult to disentangle. For example, the efficiency of the banking sector or the size of the government bond market may help determine the supply of (especially long-term) local currency debt. The next column in Table 5 shows the relation between net interest margin for the commercial banking sector.<sup>18</sup> The strong negative correlation is consistent with firms in low legality countries seeking foreign capital because of an inefficient banking system. The last column of Table 5 shows the size of the government bond market as a percent of GDP. As expected, countries with poor legal quality have low levels of public-market government borrowing, thus this could also explain the positive relation between levels of local currency debt and legality. However, in both of these cases it is again likely that legal effectiveness is the driving factor. Banks will need to charge more for loans if recovering assets in bankruptcy is relatively more costly in a poor legal system. Likewise, the level of a government’s public borrowing may be a symptom of poor legal quality as well as a determinant of corporate long-term debt if lenders worry that poor legal quality extends to the government’s ability (or desire) to pay debtors. We believe the latter are more likely scenarios, but a detailed analysis is beyond the scope of this paper.

# **VI.   Conclusions**

The main findings of this paper are that: i. In East Asia, there exists a strong negative relation between the use of debt and the effectiveness of the legal system consistent with the monitoring theories of Diamond (1991, 1993).

- ii. When examining only firms known to have foreign currency denominated debt,

there exists no relation between legal effectiveness and total debt.

> 18  Net interest margin is considered a measure of banking efficiency and is defined as accounting value of a bank’s net interest revenue as a share of its total assets.  Values are from Beck, Demirguc-Kunt, and Levine (2000).

21
|medicinal and<br>pharmaceutical<br>products||medicinal and<br>pharmaceutical<br>products||Industry established; but<br>fragmented. Mergers could<br>helpreduce cost.|
|---|---|---|---|---|
|Machinery -<br>electrical,<br>metalworking or<br>power-generating|Machinery -<br>electrical,<br>industrial|Machinery -<br>electrical, general<br>industrial, power-<br>generating|electrical machinery|Metal industry in place;<br>but too small and scattered<br>to be cost effective.<br>Scaling up could be<br>facilitated through creation<br>of clusters.|
|Organic chemicals;<br>chemical materials<br>& products; artificial<br>resins, plastic<br>materials; inorganic<br>chemicals||organic chemicals||Organic chemicals industry<br>could benefit from<br>abundant supply of raw<br>materials; however,<br>petroleum subsidy is major<br>distortion blocking larger<br>foreign direct investment.|

# **Imports of labor-intensive manufactured goods have limited economies of scale, and require only small investments**

38. A review of imports of labor-intensive manufactured goods with limited economies of scale shows the following commodities (at the 4-digit SITC level).

**Table 9: Top Nigeria Imports 2010**

|**Product(4-digit)**|**2010 in 1000 USD**|**pct**|
|---|---|---|
|Cereals and cerealpreparations|863,917|10.7%|
|Telecommunications & sound recording|330,136|4.1%|
|Fish,crustaceans,mollucs,preparation|276,152|3.4%|
|Other transport equipment<sup>9</sup>|255,846|3.2%|
|Medicinal andpharmaceuticalproducts|241,312|3.0%|
|Manufactures of metal|214,157|2.7%|
|Artif.resins,plastic mat.,cellulose|151,868|1.9%|
|Essential oils &perfume material|104,932|1.3%|
|Professional,scientific & controllingequipment|101,065|1.3%|
|Dairy products and birds'eggs|99,125|1.2%|
|Miscellaneous manufactured articles|98,169|1.2%|
|Rubber manufactures,n.e.s.|96,489|1.2%|
|Paper,paperboard,articleofpaper|91,269|1.1%|
|Textileyarn,fabrics,made-upart.|82,120|1.0%|
|Beverages|58,480|0.7%|

_Source: COMTRADE database, 4-digit STIC Revision 2._

> 9 Since transport equipment may be protected by patents, Nigeria could start by producing generic products.

20
32 Figure 2: Percentage change in cotton farm income from reform of cotton tariffs and subsidies, as a multiple of the percentage change for developing countries as a whole <!-- Start of picture text --> Other Sub-Sahara Africa<br>Uganda<br>Nigeria<br>Other Southern Africa<br>Malawi<br>Mozambique<br>Full Liberalization<br>Sri Lanka Doha partial reform<br>US-only partial reform<br>VietNam<br>Argentina<br>Brazil<br>C. Asia and S.E. Europe<br>Other Latin America<br>Malasya<br>Indonesia<br>1 2 3 4 5 6 7 8 9<br><!-- End of picture text --> Source: Authors’ GTAP model simulation results
The fi rm level data used is the Brazilian investment climate survey collected by the World Bank in 2002.<sup>46</sup> The criteria used for the sample selection was the representativeness of the population in the speci fi ed industrial and regional categories, and diversity in the fi rm size. To account for these considerations a random sample was strati fi ed using employment has weights. The sample frame covered all the registered fi rms in the following industrial sectors: food, textile, garments, chemicals, machinery, electronic equipment, auto components and wood products. The selected industries together account for more than 75% of the manufacturing value added and employment. The fi rst part of the survey, collects information on different topics: general information about the fi rm and its manager, business environment and business relations, services and government regulations, labor and human resources, production capacity, planning and innovations, supplier and client relations, infrastructure and services inspections, fi nance. The second part of the survey collects balance sheet information for the 2000-2002 period.

We de fi ne next the construction of the main variables in the paper: Employment is the total number of workers in the fi rm in 2002, Sales per employee is the total sales of the fi rm in 2002 divided by total number of employees, Value added per employee is the fi rm’s value added divided by the total number of employees, Wages per employee is the total labor cost, excluding mandatory social contributions, Capital per employee is total value of machinery and equipment at the end of 2002, excluding depreciation, divided by total number of employees, Technology developed internally is a dummy variable that assumes the value one when the most important way of acquiring technology is developed within the fi rm, Share of high educated workers is the share of workers with at least the secondary education, Share of females is the share of females in total workforce, Share workers with training is the share of workers that participated in training offered by the fi rm in the past year, Share public ownership (foreign) is the share of the fi rm’s capital owned by public (foreign) owners, Tax evasion is the share of the fi rm’s total sales that the manager reports as not being reported for tax purposes, Bribes for government contracts is the share of a total

> 46This investment climate survey project has evolved over time within the World Bank. Previous similar projects included the Regional Program on Enterprise Development that has been collecting fi rm-level data in Sub-Saharan Africa countries for a decade and the World Business Environment Survey.

26
- 74 -

The purpose of the Escuela Nueva Program is to raise the level of rural instruction through teacher training and also to equip teachers to become leaders In community development. At La Planada, the teachers not only receive training in classroom instruction but also in the practicalities of home gardening and the raising of small animals and poultry. In its first year, the program reached 31 teachers in 24 AwaCoaiquer schools (ibid).

The Environmental Education Centre commenced operations in 1986. A set of environmental education materials has been developed for incorporation within regional curricula. A mobile environmental education unit has been put together for extension to remote communities.

A currently active campaign concerns the spectacled bear, the only Latin American bear. This campaign will be directed at rural campesino communities and urban hunting clubs, both of which are exerting considerable pressure on this species and its forest habitat.

Linkages with the international conservation community can have an impact at the national level which in turn may lead to more support for a reserved area. Endorsement as a biosphere reserve or world heritage site has value in this respect, besides improving the value of such sites as tourist attraction3, if that is desired and compatible. In Ecuador, the Galapagos Islands provide concrete evidence of this effect.

# 7.4. IMPACT MANAGEMENT

This refers to the assessment, monitoring and mitigation of the environmental and socio-economic effects of industrial development. Though none of the case studies contained any examples of indigenous peoples' involvement with the management of environmental impacts, this could well be the case with World Bank-supported projects.

Geisler et al (1982) describe several examples where Indians in North America have managed to force an entry into the environmental impact assessment process. They were able to accomplish this partly because mineral or energy reserves lay under Indian land and partly because of the new environmental legal regimes governing resource exploitation that had emerged since the 1960's. They were able to oblige corporations and government agencies to take account of the Indian perspective on industrial development rather than conducting token consultations while excluding the tribes from the crucial process of impact evaluation. Though Indians were not able to bring development to a general halt on reservation lands, they were able to exert a significant effect upon its conduct.

There appear to be parallels with Latin America; it seems that mir.eral and energy development on reserved lands cannot be completely prevented, but can be moderated. To the extent that there are also parallels with certain Bank-supported projects, these North American Indian cases could serve as iuaeful models. The research capabilities developed by the Alaska Eskimo Wnaling Commission have on occasion been focused upon environmental impact management studies, and have used these as vehicles for on-the-job training programs.
# **Figure 9 R&D capital stock**

<!-- Start of picture text --> % of GDP, constant 2010 prices<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> _Source:_ NBS (2016), Statistical Yearbook 2016 and 2015, Science and Technology Yearbook The definition of infrastructure used in the above estimates attempts to exclude investment in the vehicles purchased to use transport infrastructure by enterprises in the transport sector. It assumes that the higher ratio of investment in equipment in the transport sector relative to other infrastructure sectors is due to investment in vehicles. This investment is reassigned from the infrastructure sector to the services sector.

# **_Incremental capital-output ratios_**

The rise in the overall capital-output ratio is associated with a rise in the incremental capital-output ratio (ICOR) – a measure of the productivity of capital. It is measured as the investment share divided by the growth rate. Conceptually investment should be measured net of depreciation but here it is measured on a gross basis, following a methodology used in many cross-country studies. In order to calculate sectoral ICORs, estimates have been made of sectoral GDP.

This rise in the overall ICOR has been driven by very different trends in the various sectors of the economy (Figure 10). The contrast between the ICOR in the housing, infrastructure and government sectors and the business sector is very striking both in terms of the level and the trend. The housing, infrastructure and government sectors have an ICOR that was nearly 14 times higher than in the business sector in 2016. During the recent slowdown in the economy, the incremental capital-output ratio in the business sector has risen, despite the fall in the growth rate of investment.

**Figure 10 Incremental Capital-Output ratio by sector**

<!-- Start of picture text --> Four year moving average<br>A. Infrastructure, government and housing B. Business sector ( scale changes )<br>35.0 3.5<br>30.0 3.0<br>25.0 2.5<br>20.0 2.0<br>15.0 1.5<br>10.0 1.0<br>5.0 0.5<br>0.0 0.0<br>1991 1996 2001 2006 2011 2016 1991 1996 2001 2006 2011 2016<br>Source: Author estimates<br><!-- End of picture text --> 18
Source: Authors’ calculation using HIES 2016. Note: C, P, B = coffee, pepper, betel.

The crop mix of female farmers contains a greater share of export-oriented crops than that of male farmers, which increases their overall productivity. Figure 2 shows the composition of the crop mix by the farmer’s gender, as measured by the average share of area devoted to each crop. Among male farmers, about 45% of land was accounted for by paddy, which is more than twice as much as the share among female farmers (20%). In addition, the crop mix of female farmers tends to skew toward export-oriented crops: for example, an average of 31% of land area was dedicated to tea cultivation among female farmers, whereas the share was only 14% for male farmers.

Figure 2. Crop mix by gender of farmers <!-- Start of picture text --> banana/fruits horticulture Male horticulture Others Female<br>3% 0% Others banana/fruits2% 5%<br>4%<br>2%<br>coffee,<br>pepper,<br>betel coffee, paddy<br>6% pepper, 20% chilies<br>betel<br>coconut 1%<br>12% paddy 7%<br>rubber2% 45% coconut16% vegetables9% onions0%<br>other<br>tea<br>cereals<br>14%<br>tea 4%<br>rubber<br>31% yams<br>2% 1%<br>yams<br>chilies<br>1%<br>other vegetables onions 1%<br>cereals 8% 1%<br>3%<br><!-- End of picture text --> Source: Authors’ estimations using HIES 2016.

In contrast to the previous literature, initial analysis suggests that female farmers are more productive than male farmers, unconditionally. Gender differences in crop productivity, income, and access to land are evident. Overall, male farmers have higher earnings than female farmers, as shown in Figure 3a which presents the kernel density estimates of the log of total gross crop output value by gender. Female farmers tend to cultivate less land, as shown by the kernel density estimates of the log of land area by gender (Figure 3b.) Figure 3c demonstrates the kernel density estimates of the log of crop output value divided by the cultivated area (Rs/acre), which measures land productivity. Female farmers have higher land productivity per unit of land than male farmers. The next section investigates the sources behind this unconditional female productivity advantage.

6
POVERTY IN LATIN AMERICA: SOURCES OF WELFARE DISPARITIES IN ECUADOR _Gladys López-Acevedo_  Senior Economist, LCSPP The World Bank gacevedo@worldbank.org _Monica Tinajero_ Consultant The World Bank Tinajerobm@gmail.com _JEL classification: Keywords:_ Ecuador, Welfare, Poverty _Corresponding author_ : Gladys Lopez-Acevedo, The World Bank, 1818 H Street NW, Washington DC 20433-USA. E-mail: gacevedo@worldbank.org.

This paper is part of a Latin America and Caribbean regional World Bank study on Sources of Welfare Disparities Within and Across Regions in LAC financed by the LAC Chief Economist office and the PREM Poverty anchor unit. The findings, interpretations, and conclusions in this paper are entirely those of the authors and do not necessarily reflect the view of the World Bank.

>  Background paper for the regional study on poverty in Latin America at the World Bank. These are views of the authors and do not necessarily reflect those of the World Bank, its executive directors, or the countries they represent.