rafmacalaba/datause-encoder-data huggingface dataset:
These conclusions imply higher export prices than domestic prices of natural gas. There have been a number of significant changes in the Russian gas market since Tarr and Thomson did this analysis. I discuss the most important changes below, but significant changes in the Russian domestic market have shown that the recommendation to increase competition within the Russian gas market is even more important today than it was earlier in this decade. Nonetheless, the basic principle that it is not in Russia’s interest to unify natural gas prices has not changed.

# **World Efficiency**

On the other hand, efficient pricing from the perspective of the world would call for unified pricing of gas. One pricing mechanism that could achieve this optimum of unified pricing is “two part tariff” pricing by Gazprom. Our estimates show, however, that two part tariff pricing would approximately double the profits of Gazprom on its European sales.

# **Europe’s Interest**

From Europe’s perspective, rather than pressure Russia in WTO negotiations or in other fora, diversification of energy sources is the best way to achieve more competitive prices of energy. I discuss the most important of the several pipeline proposals under construction or discussion today and argue that construction of the Nabucco pipeline is crucial to diminish the monopoly power of Gazprom, and construction of the TransCaspian pipeline would also be quite helpful for Europe.<sup>1</sup>

# **Russia’s Reserves and Exports** .

Russia is endowed with very significant natural gas resources. Its proved reserves at the end of 2008 were 43.3 trillion cubic meters, which constitute 23.4 percent of the world’s proven reserves.<sup>2</sup> Its 2008 production of 602 billion cubic meters (BCM) constituted 19.6 percent of world production. Its reserves to production ratio in 2008 of

> 1 See Aslund (2008) for a similar view. Aslund also suggests liquefied natural gas projects for the European Union.

> 2 See British Petroleum, _Statistical Review of World Petroleum_ , various years.  Russia’s  proved reserves are down from 47.6 trillion cubic meters and more than 30% of the world’s proved reserves in 2001; production is up from 2001 production of 542 billion cubic meters.

4
in decision experiments to behavior outside the lab has used incentivized measures of individual preferences, but there are notable exceptions (cf. Ashraf, Karlan, and Yin 2006). Existing evidence suggests the use of incentives shifts individual responses toward greater risk aversion (cf. Camerer and Hogarth 1999, Holt and Laury 2002), leading many to question the broad applicability of hypothetical approaches to risk preference elicitation. We contribute to this literature by demonstrating that hypothetical measures of risk preferences, interpreted as an ordinal index of risk tolerance, predict real world behaviors in an internally consistent way. In particular, our measure of risk preferences is associated with real-world behaviors that involve risk: migration and entrepreneurship.

The rest of this paper is organized as follows. In Section 2, we describe the KLPS2 data collection effort and Kenya’s post-election crisis. In Section 3, we describe our measure of risk preferences and assess the extent to which our hypothetical lottery choice questions predict behaviors likely to depend on risk aversion. In Section 4, we explain our analytic approach and present our main results. Section 5 concludes.

# **2 Research Design**

## **2.1 The Kenyan Life Panel Survey**

We measure the risk preferences of a large and heterogeneous sample of young Kenyans by embedding a series of non-incentivized decision problems in the second round of the Kenyan Life Panel Survey (KLPS2), which was administered to more than 5,000 individuals who were enrolled in primary schools in Kenya’s Western Province in the late 1990s.<sup>3</sup> KLPS2 was administered in person, through one-on-one interviews, between August of 2007 and December of 2009. The survey covers a broad range of topics including educational attainment, labor market and entrepreneurial activities, household composition and wealth, migration, and fertility.

Our subject pool includes 5,049 Kenyan youths aged 14 to 31. Summary statistics characterizing

> 3KLPS2 was a long-term follow-up of the Primary School Deworming Project (PSDP) that was implemented in Busia District, in Kenya’s Western Province, between 1998 and 2001. All KLPS2 respondents were enrolled in public primary schools in the project area in 1998. See Miguel and Kremer (2004) for discussion of the PSDP and Baird, Blattman, Casaburi, Evans, Hamory, Kremer, Miguel, Ozier, Siegel, Wafula, and Yeh (2007) for a description of the KLPS data collection effort.

6
Table 4: Quarterly Drivers of Other Investment Debt Inflows by Sector, 2002–2014

||Panel A:|All Countri|es||
|---|---|---|---|---|
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|
|log(VIX_t−_1)|-3.814<sup>_∗∗∗_</sup>|1.017|-3.645<sup>_∗∗∗_</sup>|-1.186<sup>_∗∗∗_</sup>|
||(1.148)|(0.636)|(0.878)|(0.301)|
|GDP Growth_it_|_−_1<br>0.202<sup>_∗∗∗_</sup>|-0.00423|0.166<sup>_∗∗∗_</sup>|0.0397<sup>_∗∗∗_</sup>|
||(0.0459)|(0.0161)|(0.0380)|(0.00780)|
|Observations|2695|2695|2695|2695|
|_R_<sup>2</sup>|0.035|0.002|0.043|0.022|
|CountryFE|Yes|Yes|Yes|Yes|
||Panel B: Adv|anced Econ|omies||
||(1)|(2)|(3)|(4)|
||Total|Public|Banks|Corp.|
|log(VIX_t−_1)|-7.365<sup>_∗∗∗_</sup>|0.287|-6.073<sup>_∗∗∗_</sup>|-1.579<sup>_∗∗_</sup>|
||(2.380)|(1.269)|(1.817)|(0.672)|
|GDP Growth_it_|_−_1<br>0.360<sup>_∗∗∗_</sup>|0.0304|0.294<sup>_∗∗∗_</sup>|0.0353<sup>_∗∗_</sup>|
||(0.120)|(0.0490)|(0.0938)|(0.0159)|
|Observations|1127|1127|1127|1127|
|_R_<sup>2</sup>|0.044|0.001|0.048|0.012|
|CountryFE|Yes|Yes|Yes|Yes|
||Pan|el C: EM|||
||(1)|(2)|(3)|(4)|
||Total|Public|Banks|Corp.|
|log(VIX_t−_1)|-1.511|1.500<sup>_∗∗_</sup>|-2.130<sup>_∗∗∗_</sup>|-0.880<sup>_∗∗∗_</sup>|
||(0.875)|(0.704)|(0.719)|(0.213)|
|GDP Growth_it_|_−_1<br>0.140<sup>_∗∗∗_</sup>|-0.0167|0.113<sup>_∗∗∗_</sup>|0.0440<sup>_∗∗∗_</sup>|
||(0.0360)|(0.00855)|(0.0330)|(0.00917)|
|Observations|1372|1372|1372|1372|
|_R_<sup>2</sup>|0.087|0.018|0.113|0.090|
|CountryFE|Yes|Yes|Yes|Yes|

Sample is from 2002q4-2014q4, countries as listed in Appendix A.4. Other Investment Debt inflow data is constructed by AHKS, as described in Section 2. Public inflows are defined as the sum of General Government and Central Bank inflows. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-on-year percentage growth. Column (5) of Panel A and Column (3) of Panel C use data solely from BOP, with missing data left unfilled. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01 33
# **Table 23. Second Stage Regressions Formal Labor Markets: Male**

||**Worked for a sal**<br>**months (1 if he w**|**ary in the past 12**<br>**orked for a salary)**|**Tried to find a job in**<br>**(1 if he tried**|**the past 12 months**<br>**to find a job)**|
|---|---|---|---|---|
||**OLS**|**2SLS**|**OLS**|**2SLS**|
|Violent Shock|0.0541**<br>(0.0239)|0.2893***<br>(0.0996)|0.0006<br>(0.0246)|0.0153<br>(0.0872)|
|Observations|3,190|3,190|3,190|3,190|
|R-squared|0.142|0.142|0.0714|0.0714|
|Hansen Test||0.600||0.894|
|Estimated effect of a standard deviation increase in probability of violent<br>shock(in standard deviations of dependent variable)||0,187||0,011|
|Cluster by community|||||
|Regional Fixed Effects|||||
|*** p<0.01, ** p<0.05, * p<0.1|||||

Source: CLS-WILL: first wave – household questionnaire.

52
**Table 2: Number of Financial Institutions (1995-2005)**

||**1995**|**2000**||**2005**||
|---|---|---|---|---|---|
|**Number of Institutions**|**279**|**196**||**154**||
|Commercial and Housing Banks|41|30||21||
|_Private domestic_|_21_||_15_||_12_|
|_Public_|_5_||_4_||_3_|
|_Foreign_|_12_||_11_||_6_|
|_Solidarios_|_3_||_0_||_0_|
|Other Credit Institutions|98|54||32||
|_Corporaciones Financieras (CF)_|_24_||_8_||_2_|
|_Compañias de Financiamiento Comercial (CFC)_|_73_||_32_||_24_|
|_Financial Cooperatives_|_N/A_||_13_||_5_|
|_Organismos Cooperativos de Grado Superior (OCGS)_|_1_||_1_||_1_|
|Instituciones Oficiales Especiales (IOEs)|6|9||10||
|Almacenes Generales de Deposito|11|9||6||
|Insurance Companies|60|50||44||
|_Life_|_35_||_27_||_24_|
|_Non-Life_|_25_||_23_||_20_|
|Sociedades de Capitalización|8|5||5||
|Pension Funds|9|6||6||
|Fiduciarias|46|33||30||

_Source: Superintendencia Financiera._ _Note: IOEs refers to second-tier public development banks. Comisionistas de bolsa and other financial entities (e.g. sociedades administradoras de inversión) are excluded from the analysis because their respective numbers were not available for 1995._ Figure 1: Composition of Financial System Assets (1995-2005) <!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1995 2000 2005<br>Commercial and Housing Banks Other Credit Institutions Insurance Companies Pension Funds Fiduciarias<br>% of GDP<br><!-- End of picture text --> _Source: Superintendencia Financiera and own analysis._ _Note: The above figure shows total assets for credit institutions (including sociedades de capitalización), total assets under management for insurance companies and pension funds, and only investment assets for fiduciarias. Other financial institutions are excluded from the analysis either because of small asset size and data unavailability (e.g. comisionistas de bolsa, administradoras de inversión) or because they are already indirectly captured in the above figures (e.g. IOEs)._ 11
Table 1: Average individual characteristics, pre-treatment

||_Mean_|_Mean_|_Dif=0_|_Normalized_<br>|_No. Obs._|
|---|---|---|---|---|---|
||_Control_<br>(1)|_Treated_<br>(2)|_(p-value)_<br>(3)|_Dif._<sup>(</sup><sup>_a_)</sup><br>(4)|(5)|
|**Panel A: Outcomes**||||||
|_Indexes_||||||
|HIV knowledge|-0.049|0.205|0.177|0.029|5166|
|HIV attitudes|0.036|-0.016|0.625|-0.010|5166|
|HIV testing|-0.048|-0.035|0.916|0.002|5166|
|Attitudes towards risky sexual behavior|0.006|-0.041|0.638|-0.010|5166|
|Risky sexual behavior (conditional on sexually active)|-0.018|0.079|0.458|0.020|3246|
|_Individual variables_||||||
|HIV transmitted during pregnancy|0.612|0.611|0.962|-0.001|5166|
|Mentions ARV drugs spontaneously|0.020|0.024|0.330|0.021|5166|
|Mentions drugs to live longer with HIV|0.619|0.634|0.310|0.021|5166|
|Recognizes ARV when mentioned by enumerator|0.187|0.213|0.030|0.046|5166|
|Knows that second test is necessary|0.277|0.287|0.450|0.016|5166|
|Knows about 3-months window period|0.074|0.089|0.078|0.038|5166|
|Can get HIV through intercourse|0.947|0.948|0.897|0.003|5166|
|Would buy from an HIV+ shopkeeper|0.415|0.427|0.398|0.018|5166|
|An HIV+ boy should play footbal|0.579|0.571|0.571|-0.012|5166|
|People HIV+ should not be blamed|0.652|0.632|0.165|-0.029|5166|
|HIV is not punishment for sleeping around|0.433|0.465|0.031|0.046|5166|
|Would reveal HIV status|0.707|0.694|0.365|-0.019|5166|
|Tested last 6 months (self-reported)|0.053|0.055|0.675|0.009|5166|
|Men should have one partner only|0.842|0.860|0.106|0.034|5163|
|Women should have one partner only|0.880|0.898|0.056|0.040|5166|
|Not ok date sugardaddy to fnance educ|0.760|0.745|0.265|-0.024|5166|
|Not ok date sugardaddy for money|0.670|0.675|0.713|0.008|5166|
|Not ok date sugardaddy to go out|0.886|0.866|0.050|-0.042|5166|
|If a woman brings a condom does not mean she’s not serious|0.579|0.603|0.111|0.034|5166|
|Has not had multiple concurrent sexual partners|0.776|0.786|0.521|0.018|2933|
|Number of current sexual partners if sexually active|1.329|1.276|0.119|-0.048|2349|
|Used condom the last time he/she had sex|0.520|0.495|0.219|-0.036|2690|
|**Panel B: Controls**||||||
|Female|0.473|0.474|0.943|0.002|5166|
|Age|20.618|20.614|0.962|-0.001|5166|
|Currently attending school|0.342|0.350|0.565|0.012|5166|
|Years of education|11.598|11.596|0.950|-0.001|5166|
|Speaks English|0.958|0.962|0.484|0.015|5166|
|Single|0.232|0.224|0.509|-0.014|5166|
|Does not live with the family|0.229|0.246|0.186|0.028|5166|
|Household size|4.482|4.257|0.001|-0.067|5166|
|Wealth index|1.781|1.736|0.010|-0.054|5150|
|Home owner|0.448|0.355|0.000|-0.135|5165|
|Father’s education _>_ secondary|0.376|0.314|0.000|-0.092|3928|
|Mother’s education _>_ secondary|0.252|0.214|0.004|-0.065|4393|
|Muslim|0.370|0.374|0.799|0.005|5166|
|Yoruba|0.920|0.921|0.894|0.003|5166|

Notes: (a) Normalized difference is the difference in the sample means of treatment and control groups divided by the square root of the sum of the sample variances.

36
|Dwellings in<br>densely<br>population areas|Share of dwellings in<br>densely populated areas|BSO|Yes|
|---|---|---|---|
|Dwellings in an<br>intermediate<br>population area|Share of dwellings in<br>intermediately populated<br>areas|BSO|Yes|
|Dwellings with<br>energylabel A|Share of dwellings with<br>energylabel A|BSO|Yes|
|Building<br>Stock<br>Excess winter<br>mortality/deaths|Share of excess winter<br>mortality/deaths|BSO|Yes|
|Features<br>Equipped with<br>heating|Share of population living<br>in dwellingwith heating<br>|EU-SILC|Not available<br>for BGR|
|Equipped with<br>air conditioning|Share of the population<br>living in dwellings with air<br>conditioning|EU-SILC|Not available<br>for BGR|
|Number of<br>rooms per|Average number of<br>rooms per person in<br>rented/owned/all|EU-SILC|Yes|
|person<br>Additiona|<br>dwellings<br>l Indicators based on literatur<br>Share of ener|e review||
|10 percent<br>measure<br>(Boardman,<br>1991)|gy<br>expenditure (compared<br>to overall expenditure or<br>income) higher than 10<br>percent|HBS|Yes|
|Energy<br>deprivation|The<br>gap<br>between<br>theoretical<br>standard<br>energy consumption and<br>actual consumption for<br>space heatingand cooling|Several<sup>20</sup>|Requires<br>consumption<br>data|
|Expenditure-<br>Based<br>Low-Income High<br>Costs<br>(Hills, 2011)|Requires two conditions:<br>Required<br>energy<br>expenditure above the<br>median plus remaining<br>income below the official<br>povertythreshold|Originated in<br>England,<br>possible with<br>HBS under<br>conditions|Possible with<br>HBS when<br>relying on HBS<br>income|
|After-fuel-costs<br>poverty (AFCP)<br>indicator<br>(Hills, 2011)|Household<br>equalized<br>income<br>(possibly<br>subtracting housing and<br>domestic energy costs) is<br>below<br>the<br>standard<br>threshold<br>of<br>60%<br>of<br>equivalized<br>national<br>income.|Originated in<br>England,<br>possible with<br>HBS under<br>conditions|Possible with<br>HBS when<br>relying on HBS<br>income|
|Minimum<br>Income Standard<br>(Moore, 2009)|Fuel costs (EHS) > Net<br>household income (EHS) –<br>housing costs (EHS) –<br>minimum<br>living<br>costs<br>(MIS).|England|Requires a<br>combination<br>of several<br>sources|

> 20 Energy needs of a dwelling, or data on indoor temperature (see Palma and Gouveira (2022)).

9
# Appendix Table2. Summary Statistics for Child Sample

|Variable Name|Mean|SD|
|---|---|---|
|Theta|0.286|0.21|
|Child enrollment status|0.625|0.48|
|Per equivalent adult expenditure<br>|1472.118|1306.32|
|Child's age|9.436|3.11|
|Child's age^2/100|0.987|0.62|
|Girl<br>|0.484|0.5|
|# of people in HH (members only)|9.368|4.74|
|Household highest no. of school years|7.904|4.83|
|Punjab|0.366|0.48|
|Sindh|0.243|0.43|
|NWFP|0.23|0.42|
|Rural|0.627|0.48|
|Household head age|44.869|11.83|
|Household head worked last month|0.852|0.36|
|Household head female|0.067|0.25|
|Household head married|0.945|0.23|
|Household head years of schooling|4.493|5.32|
|Agricultural land ownership|0.089|0.28|
|Ownership of buffalo/camel/horse/asses/ mules|0.216|0.41|
|Ownership of cattle/sheep/goat|0.247|0.43|
|Ownership of fridge/freezer|0.331|0.47|
|Ownership of air conditioner|0.14|0.35|
|Ownership of cooking stove|0.288|0.45|
|Ownership of TV|0.463|0.5|
|Ownership of car/truck|0.044|0.2|
|Ownership of motorcycle|0.128|0.33|
|Computer ownership|0.042|0.2|
|Ratio of female people between 0 and 5 years old in HH|0.08|0.1|
|Ratio of male people between 0 and 5 years old in HH|0.084|0.1|
|Ratio of female people between 5 and 15 years old in HH|0.193|0.13|
|Ratio of male people between 5 and 15 years old in HH|0.206|0.14|
|Ratio of female people between 15 and 55 years old in|||
|HH|0.203|0.09|
|Ratio of male people between 15 and 55 years old in HH|0.189|0.1|
|Ratio of femalepeople older than 55years old in HH|0.022|0.04|
|Sample size|17,739||

31
15 with those obtained by Saunders and Klau(1985) for OECD countries (Table 5), although differences exist between the two studies. 15 The developing nation median elasticity for Ge is 1.25 and is remarkably close to the OECD median elasticity of 1.20. Median values for Gc are even closer while for Gt they are further apart.16 (Pluta offers no discussion of Gi.) Relatively similar experience in the growth of government expenditures across countries is suggested by these statistics. However, the range in the individual elasticity estimates for the group of developing economies is large, 0.83 - 2.54. In contrast, the OECD values range from only 1.07 - 1.37. There, therefore, may be far less in common about the recent growth in government spending across these two groups of countries than might have at first appeared.

Another study, Ram(1987), shares this view noting: The main conclusion is that there is much diversity in the character of the covariance between income and government expenditure in different countries. Therefore, it is difficult to make a universally valid statement and one can get a result of almost any kind ...17 Ram's conclusion is drawn from what must be the most extensive study of government consumption growth yet undertaken. He employs the data, described above on Gc in constant international prices generated by Summers and Heston(1984). Ram has 20-30 years of annual observations available per nation and estimates for each of 115 nations two related elasticities: (I) Gc with respect to GDP; and (II) (Gc/GDP) with respect to GDP per capita. (The latter
<!-- Start of picture text --> Figure 11: Dispersion in Returns and Learning Outcomes<br>R 2 = 0.2246<br>Less equitable<br>More equitable Less equitable<br>-8 -6 -4 -2 0 2 4 6 8<br>Difference in returns (Q90-Q10)<br>Dispersion in test scores (90-10th pctl)<br><!-- End of picture text -->

# **4.4 Summing up the evidence**

We hypothesize that there might be a relationship between a country’s development stage (as this is reflected in their labor market characteristics) and the impact of schooling on earnings by quantile. In the OECD it is clear that education is a complement to unobserved skills (or abilities), and that education investment will increase inequality, other things being equal. We observe cases where the OECD pattern does not apply. The idea behind our approach is that if wage effects are higher at the top end of the earnings distribution than at the bottom end (as is the case for OECD countries and, based on our findings, other high and middle income countries), then education tends to increase earnings inequality, since education is a better investment for the better off. On the other hand, if skills and education are substitutes, then the least skilled will benefit more from education and education tends to reduce earnings inequality.

31
Policy Research Working Paper 7790

# **Abstract**

In low- and middle-income countries, scaling essential health interventions to achieve health development targets is constrained by the lack of skilled health professionals to deliver services. This paper takes a labor market approach to project future health workforce demand based on an economic model that projects economic growth, demographics, and health coverage, and using health workforce data (1990–2013) for 165 countries from the World Health Organization’s Global Health Observatory. The demand projections are compared with the projected growth in health worker supply and health worker “needs” as estimated by the World Health Organization to achieve essential health coverage. The model predicts that by 2030 global demand for health workers will rise to 80 million workers, double the current (2013) stock of health workers. The supply of health workers is expected to reach 65 million over the same period, resulting in a worldwide shortage of 15 million health workers. Growth in the demand for health workers will be highest among uppermiddle-income countries, driven by economic growth and population growth and aging, resulting in the largest predicted shortages, which may fuel global competition for skilled health workers. Middle-income countries will face workforce shortages because their demand will exceed supply. By contrast, low-income countries will face low growth in demand and supply, but they will face workforce shortages because their needs will exceed supply and demand. In many low-income countries, demand may stay below projected supply, leading to the paradoxical phenomenon of unemployed (“surplus”) health workers in those countries facing acute “needs-based” shortages.

This paper is a product of the Health Nutrition and Population Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at amaeda@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Self-employed workersinthenon-agriculturalsector(as percentage ofworking adults)|9.2|8.8|-0.4|*|
|---|---|---|---|---|
|**Socio-demographic characteristics for men (%)**||||0|
|**Adults (15 years of age or older)**|65.5|66.4|1.0|**|
|**Working adults (as percentage of adults)**|77.1|78.9|1.8|***|
|Wage/salaried workers in the agricultural sector (as percentage of working adults)|20.8|16.7|-4.1|***|
|Wage/salaried workers in the non-agricultural sector (as percentage of working adults)|43.0|51.5|8.5|***|
|Self-employed workers in the agricultural sector (as percentage of working adults)|48.4|37.4|-11.0|***|
|Self-employed workersinthenon-agriculturalsector(as percentage ofworking adults)|16.9|15.9|-1.1|***|
|**Socio-demographic characteristics for women (%)**||||0|
|**Adults (15 years of age or older)**|66.2|68.9|2.8|***|
|**Working adults (as percentage of adults)**|43.5|47.1|3.6|***|
|Wage/salaried workers in the agricultural sector (as percentage of working adults)|23.9|20.8|-3.1|***|
|Wage/salaried workers in the non-agricultural sector (as percentage of working adults)|15.0|20.1|5.1|***|
|Self-employed workers in the agricultural sector (as percentage of working adults)|74.5|56.7|-17.8|***|
|Self-employed workersinthenon-agriculturalsector(as percentage ofworking adults)|5.5|6.9|1.4|***|

* Significant at ten percent; ** significant at five percent; *** significant at one percent.

Note: The split agricultural/non-agricultural sector does not add up to 100% given that there might be workers who work in both the agricultural and the non-agricultural sector, the same happens on the work/farm/business split. Source: IHDS, 2005 and 2012.

14