rafmacalaba/datause-encoder-data huggingface dataset:
evidence for developing countries, there are many anecdotes of conflicts escalating between operators and regulators/governments in various Latin American and African countries as a result of aggressive bargaining strategies<sup>31</sup> (some of Argentina’s provinces, Bolivia, and Mali come to mind). To get a sense of the main private actors and their global market size, Box 3 provides a snapshot of those who have delivered large scale projects between 1991 and 2015.

# **Box 3: How concentrated is the water operators market?**

The following is a list of the top 10 players in the sector between 1991 and 2015. They control 25% of the private projects and 60% of the investments made. France, Spain, Brazil and Malaysia, each have two firms in the top 10. The Brazilian firms are mostly present in Latin America while the Malaysian firms focus on Asia. The French and Spanish firms are the only global players among the top 10. The variance in average project size is quite remarkable. The “Other” category delivers 75% of the projects but their projects are usually smaller. It includes, notably, Chinese constructions companies which are present in Asia and SSA.

On the face of it, there is no concentration since the top 10 firms share 60% of the business. In practice, the situation is more complex. There is a regional specialization of actors as already noted in Benitez et al. (2005). The Asian companies are mostly active in Asia, the Brazilians focus on Latin America (and to a lesser extent the Portuguese-speaking African market). The French companies are the only ones to be present in all markets over time. They have taken over some of the main global competitors (mostly Spanish firms). Although only two firms are needed to ensure competition, with the benefit of experience, we know that the market concentration at both the local and global level has limited the number of bidders in many of the deals. Big markets attract interest. Small and poor markets do not.

||Investment<br>(Billion US$)|Number of<br>projects|Average project<br>size|Share of total<br>projects|Share of<br>investment|
|---|---|---|---|---|---|
|Suez (France)|14.5|73|198.6|7.7%|17%|
|Ordebrecht (Brazil)|7.2|27|266.7|2.9%|9%|
|Aguas de Barcelona (Spain)|7.1|25|284.0|2.6%|9%|
|Construtora Queiroz Galvao (Brazil)|3.9|16|243.8|1.7%|5%|
|Puncak Niaga Holdings (Malaysia)|3.3|5|660.0|0.5%|4%|
|Veolia (France)|3.3|58|56.9|6.1%|4%|
|Abengoa (Spain)|3.1|10|310.0|1.1%|4%|
|Benpres Holdings (Phlippines)|2.7|4|675.0|0.4%|3%|
|Ranhill BhD (Malaysia)|2.4|6|400.0|0.6%|3%|
|Mitsui (Japan)|2|10|200.0|1.1%|2%|
|Other|33.54|712|47.1|75.3%|40%|
|Total|83.04|946|87.8|100.0%|100%|
|||Source: PPI databas|e|||

> 31 See for instance the many papers on Argentina, Bolivia or Mali’s experience.

25
# 4. **The** Peruvian **VTS Data** Set

To assess the relative returns to VTE and general education in Peru, we **make use** of the Peruvian Living Standards Survey (rLSS) national probability sample of hiuseholds and individuals surveyed in 198511986.5 From the full PLSS sample we drew **a** subsample (n - 2,210) of all urban males between the ages of 15 and 69 who were either employ,ed in the private wage sector or were "self-employed," that is, working for pay in a family enterprise. Rural workers were excluded from the present study because of the small number of such individuals with VTE.<sup>6</sup> The breakdown of the sample between those who are self-employed and those who are wage-employed, and between those living in Metropolitan Lima and those living in all other urban areas (OUAs) is presented in table 2. The distribution across the four cells is nearly equal, the largest group being the wage-employed in Lima (756), and the smallest the self-employed in Lima (427).

S The PLSS was implemented between June 1985 and July 1986 by Peru's Instituto Nacional de Estadistica (INE). The survey questionnaire was developed by the World Bank in collaboration with INE and the Central Bank of Peru and adapted from a prototype questionnaire used earlier in Cote d'Ivoire. A parallel study to this one comparing the returns to VTE and general education in Cote d'Ivoire is available (Grootaert 1988). For details about the PLSS survey and sample, see Grootaert and Arriagada (1986).

6 Analyses of the earnings of similar categories of Peruvian workers, without the focus on VTE, are described elsewhere -- see Stelener, Arriagada, and Moock (1987), which look at the returns to education in general, and Arriagada (1988), which focuses on the returns to training. These two studies are confined also to males, but they they use an upper age cut-off of 65 rather than 69 and they include workers in rural as well as urban areas and in public as well as private employment. Female workers, for whom the specification of an appropriate earnings function will tend to differ, are treated in King (forthcoming) and Arriagada (forthcoming).

7
Table 5: Predicted Poverty Rates ($1.90 per Day) from Different Models

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|
|---|---|---|---|---|
|**National**|14.6|19.1|18.0|17.4|
|**Urban**|10.0|11.3|12.1|12.5|
|**Rural**|16.8|22.8|20.9|19.8|

Sources: India National Sample Survey Office (NSSO) Surveys.

Table 6: Comparison of Actual Poverty in 2011-12 with Forward Prediction of Models

||**Actual**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|
|---|---|---|---|---|---|
|**National**|21.1|23.2|25.9|23.1|19.1|
|**Urban**|13.4|14.6|16.0|16.3|16.7|
|**Rural**|24.8|27.4|30.7|26.5|20.2|

Table 7: Comparison of Actual Poverty in 2004-05 with Reverse Projection of Models

||**Actual**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|
|---|---|---|---|---|---|
|**National**|37.5|41.0|48.7|61.0|26.6|
|**Urban**|25.4|34.6|34.2|38.7|14.9|
|**Rural**|43.4|44.1|55.8|71.9|32.4|

Sources: India National Sample Survey Office (NSSO) Surveys. Model 1: Final Model Model 2: District dummies*Time Trend Model 3: Expenditures at the Extensive Margin Model 4: Constant Coefficient Model Table 8: Preferred Model vs. Typical Line-Up Method Predictions for 2014-15

||**Model**|**Line-up**|
|---|---|---|
|National|14.6|11.0|
|Urban|10.0|7.1|
|Rural|16.8|12.9|

Source: India National Sample Surveys (NSSO) Surveys.

25
countries to estimate that the proportion of adult migrants with tertiary education increased fourfold over 1975-2000. The increasing amount of South-North migration coupled with the increasing skill level of this migrant flow means that brain drain is increasing in absolute terms.

However, educational levels in developing countries have also been rising dramatically over the past decades. As a result, de Foort (2008) finds that the _rate_ of high-skilled emigration (relative to the base of all tertiary-educated individuals) has been very stable at the global level over the period 1975-2000, with the educational level of the home workforce increasing at a similar rate to the increase in tertiary educated migrants. However, sub-Saharan Africa is an exception to this pattern – a region in which tertiary education growth remained low and did not offset the rise in skilled migration.

Analysis of migration flows faces severe data constraints, and a full picture of brain drain trends during the 2000s will not emerge until after data are released from the 2010-11 round of global population Censuses. However, brain drain appears likely to have fallen in relative terms during this time. Tertiary enrolment rates have continued to grow dramatically, with gross tertiary enrolment rates for sub-Saharan Africa increasing from 3.9 percent in 1999 to 6.0 percent in 2009, those in South Asia increasing from 8.0 percent in 2000 to 11.4 percent in 2008, and those in Latin America and the Caribbean increasing from 20.9 percent in 1999 to 35.2 percent in 2007 (World Bank WDI and GDF global database [accessed November 22, 2010]). At the same time as stocks of tertiary-educated individuals have been rising in many developing countries, the intake of skilled workers has been quite flat over the first part of the decade in many OECD destination countries, and fallen in 2008-2010. For example, the United States H1-B visa program (the main temporary residence category for admitting skilled workers<sup>3</sup> ) issued visas to an average of 130,000 workers a year over the 2000s, reaching a peak in 2007 at 154,000, and dropping in 2009 back to 110,000 (United States Department of State, 2011). It is worth noting how small this magnitude is – less than one skilled worker admitted per 1000 population.

> 3 There are a variety of other visa categories that also are directed towards skilled workers, but the number admitted through these are smaller. In particular, the H1-C program for registered nurses issued 128 visas in 2009; the L1 category of intra-company transfer visas issued 64,000 visas, and the O1 category for individuals of extraordinary ability admitted 9,368 (United States State Department, 2011). By way of comparison, 690,000 individuals received permanent residency in the U.S. in 2010 as either the immediate relative of a U.S. citizen or as a family-sponsored migrant.

8
pay-offs. A policy-induced structural change of this sort, just like an exogenous structural change (induced for example by globalization), requires attention to active labor market policies with respect to training, education and transitional income support.

# **References**

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Babiker, MH, and RS Eckaus (2007): _Unemployment effects of climate policy_ , Environmental Science & Policy, Vol. 10, pages 600-609.

Barbier, EB (2009): _A Global Green New Deal_ , United Nations Environment Programme, Geneva.

Behrman, J (1999): _Labor markets in developing countries_ , Chapter 43 in Ashenfelter, J, and D Card (ed.s) (1999), _Handbook of Labor Economics_ , Volume 3, pages 2859-2939.

Bosetti, V, Carraro, C, Galeotti, M, Massetti, E, and M Tavoni (2006): _WITCH: A World Induced Technical Change Hybrid Model_ , The Energy Journal, Special Issue on Hybrid Modeling of Energy-Environment Policies; Reconciling Bottom-up and Top-down, pages 13-37.

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CEDEFOP (2009): _Future skill needs for the green economy_ , European Centre for the Development of Vocational Training, Publications Office of the European Union, Luxembourg.

Collier, P (2007): _The bottom billion_ , Oxford University Press, Oxford. EBRD (2011): _The Low Carbon Transition Report_ , EBRD, London, April.

EC (2007): _Facts and Figures: Links Between EU’s Economy and Environment_ , Office for Official Publications of the European Communities, Luxembourg.

Dutt, AK (2007): _Heterodox Development Economics and the Role of Aggregate Demand_ , Prepared for presentation at Glasgow University’s International Conference on Development Economics, _Policies and institutions for growth and development: Is there a ‘heterodox’ view?_ 26-27 June 2007, Glasgow, UK.

35
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28
significant. In most cases the size of the coefficient exceeds 0.70 or is close to it. Somewhat smaller coefficients are obtained for Chile(0.44), Ghana(0.47), Tanzania(0.59) and Zambia (0.31). In the last three countries one should expect that the estimated coefficient will underestimate the true coefficient due to the imposition of extensive price controls. The official CPI was used to estimate inflation in all cases. For these countries, this procedure would understate thelevel of actual inflation. From the results, it is possible to say that the dual exchange rate system arrangements provides very little insulation to domestic prices. The largest coefficient we obtain is for Peru at 0.98, which would indicate that the DER provides no insulation whatsoever.

Given that most coefficients we obtained are less than 1, this could be taken as an indication that the DER system provides some insulation in the short run. This interpretation, however, in some cases will be misleading for two reasons. First, flexible exchange rates typically adjust faster than prices, thus suggesting that even if the effect were large, this might take several quarters to be fully reflected in domestic prices. Second, because over time there are changes in the prices of tradeables to nontradeables, one would expect some changes in the exchange rate not to be accompanied by proportional changes in domestic prices. In summary, the empirical evidence indicates that the DER system provides partial insulation on domestic prices.

# V. Summary and Conclusions

This paper examined and tested some macroeconomic implications of multiple exchange rates arrangements for a large sample of developing countries. We tested the influence of certain macroeconomic fundamentals ( the money supply, the stock of international reserves and the real exchange rate) on the behavior of the parallel premium. We also tested the hypothesis that a DER arrangement provides insulation to domestic prices by enabling the authorities to postpone a maxi-devaluation which could be inflationary. The countries in the sample were classified either as high premium (if mean annual premium exceeded 35 percent) or moderate premium (if mean annual premium was between 10 and 35 percent). We expected the relationship between the parallel premium and the macroeconomic
# <u>Table 2 – Stimulus and social protection response budget</u>

|**Variable**|**Low-**<br>**income**<br>**countries**|**Lower-middle**<br>**income**<br>**countries**|**Upper-middle**<br>**income**<br>**countries**|**High-**<br>**income**<br>**countries**|**All**<br>**countries**|
|---|---|---|---|---|---|
||_All_<br>|_values are country_<br>|_averages expresse_<br>|_d in % of 201_<br>|_9 GDP_<br>|
|Stimulus budget<br>_of which_|3.14|3.00|4.26|10.03|5.58|
|Health response budget|1.06|0.69|0.72|1.34|0.95|
|Social protection response budget|0.75|0.97|1.91|3.48|1.99|
|Income protection measures|0.75|0.90|1.31|1.57|1.20|
|_of which_||||||
|Unconditional cash transfers|0.38|0.60|0.74|0.68|0.63|
|In-kind and food transfers|0.28|0.13|0.03|0.04|0.10|
|Utility and financial waivers|0.07|0.07|0.19|0.27|0.16|
|Unemployment/out-of-work<br>income support|0.00|0.01|0.11|0.38|0.15|
|Job protection measures<br>_of which_|0.00|0.07|0.59|1.92|0.79|
|Social insurance contributions|0.00|0.01|0.21|0.21|0.13|
|waivers||||||
|Wage subsidies|0.00|0.05|0.35|1.55|0.60|
|Numberofcountries|19|40|38|45|142|

_Note_ : see table 1 for the classification of social protection into policy focus areas (income protection and job protection). UCT = Unconditional Cash Transfers. _Source_ : Social protection budget data is from the authors’ dataset. Stimulus budget and health response data from IMF (2021).

32
three groups of developing countries in the MENA, SA and LAC regions. With minor exceptions, these groups are clearly identifiable on the estimated curve, and countries within these groups present common features and a similar FLFP to the inequality gradient. While there is an extensive literature on the dis-equalizing impact of FLFP in developed countries, little has been written about developing countries, and to our knowledge this is the first empirical estimation of an inverted U- shaped relationship between FLFP and inequality.

To strengthen our novel findings, we replicate these macro trends using household- and individuallevel data. While micro-data are scarcer than macro-data, we were able to confirm the results from the macro analysis. Overall, the impact of FLFP is dis-equalizing in the developing countries under consideration, and the lower the FLFP the higher the impact. This is because the FLFP rate of return of the last two deciles is higher than that of the bottom two – although participation is higher among the top deciles. This shows that the increase in FLFP would have a strong pro-inequality impact, if not compensated with measures that favor women in the bottom deciles. Among developed countries, the Scandinavian and the Euro-Mediterranean show a pro-equality impact as FLFP returns in the lower deciles are higher than those <mark>in the higher ones,</mark> while in the remaining group of European countries the dis-equalizing effect is prevalent.

In conclusion, while there is almost unanimous consensus that increasing FLFP levels is an important developmental outcome, particularly in countries where FLFP levels are low and with high levels of gender discrimination – the translation of this general principle into effective policies becomes much more complicated. In certain contexts, fostering FLFP without considering its distributional impact can have potentially negative consequences in terms of inequality. There are two good reasons why policy makers should consider this an important piece of information when formulating policies to increase FLFP levels.

First, these policies often require the usage of public resources, e.g. tax breaks or subsidies for companies employing women, financing of nurseries and schooling for children, targeted training programs for women’s insertion in the labor market, as well as offering publicly financed parental leave schemes (World Bank, 2012; Martínez and Perticará, 2017; Lopez-Acevedo et al., 2020). If not targeted, these policies can benefit women working in the formal sector, in urban areas, or with higher levels of education, thus contributing to increasing inequality. The paradox, therefore, is 20
Moulton, B. R. 1990. An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units. _The Review of Economics and Statistics,_ 72(2): 334-338.

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17
**Table 1.** Summary statistics number of grids used in the groundwater risk mapping at the national-scale in Bangladesh; area covered under four risk zones under each risk model.

# **Groundwater Risk Map (model 1: arsenic, salinity and groundwater storage)**

|**Risk Zone**|**No of grids**|**Area (km**<sup>**2**</sup>**)**|**Cumulative**<br>**area (km**<sup>**2**</sup>**)**|**Proportion of**<br>**cumulative**<br>**land area (%)**|
|---|---|---|---|---|
|Extremely high risk|27|208|208|0.15|
|High risk|955|7,146|7,354|5.26|
|Medium risk|2,299|10,350|17,704|12.66|
|Low risk|5,951|28,123|45,826|32.78|

# **Groundwater Risk Map (model 2: arsenic and salinity only)**

|**Risk Zone**|**No of grids**|**Area (km**<sup>**2**</sup>**)**|**Cumulative**<br>**area (km**<sup>**2**</sup>**)**|**Proportion of**<br>**cumulative**<br>**land area (%)**|
|---|---|---|---|---|
|Extremelyhigh risk|2,376|18,297|18,297|13.09|
|High risk|4,343|15,147|33,444|23.92|
|Medium risk|6,052|13,160|46,604|33.33|
|Low risk|8,493|18,797|65,401|46.78|

23
|Horng,<br>Chang and<br>Wu|2012|Taiwan,<br>China|1961-<br>2006|Real GDP/<br>capita|Total<br>insurance<br>density,<br>growth in real<br>insurance<br>density,<br>M2/GDP|3 variable<br>VAR Granger<br>/ VECM||Financial<br>development<br>short run<br>supply<br>leading,<br>demand<br>following for<br>insurance<br>growth.|
|---|---|---|---|---|---|---|---|---|
|Chen, Lee<br>and Lee|2012|60<br>countries|1976-<br>2005|Real GDP/<br>capita<br>growth|Life insurance<br>density and<br>life insurance<br>penetration|Two step<br>GMM, basic<br>and extended<br>models|Base:<br>Inflation,<br>Gov’t cons.,<br>openness,<br>trade, human<br>capital|Both life<br>density and<br>life<br>penetration are<br>supply leading<br>to GDP<br>growth but<br>density has<br>more effect.<br>The positive<br>impact of life<br>insurance is<br>driven by<br>middle and<br>low income<br>countries, but<br>life insurance<br>and capital<br>markets tend<br>to be<br>substitutive.|
|Akinlo|2013|Nigeria|1980-<br>2010|Real GDP<br>growth|Total<br>premium|Granger<br>causality<br>/VECM.|Inflation,<br>interest rate|Long run<br>supply<br>leading. No<br>short term<br>causality.|
|Chang, Lee<br>and Chang|2013|10 OECD<br>countries|1979-<br>2006|GDP|Life insurance<br>premium,<br>Non life<br>insurance<br>premium,<br>total<br>insurance<br>premium|Bootstrap<br>Granger panel<br>causality,<br>cross<br>sectional<br>dependency<br>and slope<br>homogeneity.||Insurance -<br>growth nexus<br>is dependent<br>on country<br>conditions.<br>Research<br>needs to<br>consider<br>banking<br>insurance<br>interactions.|
|Hu, Su and<br>Lee|2013|31<br>Chinese<br>provinces|1997-<br>2011|Real GDP/<br>capita|Real total<br>insurance<br>density|Bootstrap<br>Granger panel<br>causality||Direction of<br>causality<br>varies by<br>province with|

11