rafmacalaba/datause-encoder-data huggingface dataset:
expenditures are constructed as measure of welfare.<sup>4</sup> Regional price differences have been accounted for by weighting the welfare aggregate by regional price indices. Similarly taken into account are economies of scale within the household. Therefore, the data is suitable to analyze household energy expenditures and simulate the likely impact of the gas price increase on household welfare.<sup>5</sup> The impact of the higher energy costs on households depends on several factors. First, the impact will vary by location (i.e., large cities such as Yerevan, small towns and rural areas), as the degree of household dependence on gas varies by location (see Table 5). Second, it depends on whether a given household is a consumer of gas and other alternative energy products that could also see their prices go up. Third, the degree of impact also depends on the level of consumption across different income groups. Finally, the impact depends on the relative share of gas and other alternative energy products in total household budget. For example, households that spend larger share of their consumption budget on energy could see greater impact from the gas price increase. Although not directly addressed in this paper, for a given household, the impact of the gas price increase will also depend on substitution and income effects. Since there is no variation in gas price, it is not possible to estimate a household demand function for gas. We therefore estimate the welfare impact of the price increase by assuming the elasticity of gas demand to price changes is zero<sup>6</sup> and that households do not change the quantity of gas consumption due to higher prices and the consumption patterns before price change hold. Before estimation of the welfare impacts, we look at the incidence, the level and the relative budget shares of gas, electricity and fuelwood consumption in Armenia.<sup>7</sup>

> 4 Household consumption expenditures, rather than income, are used, as income suffers from widespread underreporting in household survey instruments. Consumption is also commonly used in poverty analysis.

> 5Ideally, the ILCS data would be matched with household-level data from the utility companies on gas and other alternative energy consumption and payment to allow a more accurate analysis of the residential demand and the distributional consequences of price changes. Unfortunately such data is not available for this exercise.

> 6 This assumes household don’t resort to other and cheaper sources of energy.

> 7 All data on energy access, use and expenditures come from the 2008 ILCS.

9
Table A.5: **Quadratic CEFs of Exposed and Comparison Cohorts for Senior High School Completion**

# **Dependent Variable: Senior High Completion of Children**

||**Son**|**s**|**Daughte**|**rs**|
|---|---|---|---|---|
||**Comparison**|**Exposed**|**Comparison**|**Exposed**|
||(1)|(2)|(3)|(4)|
|Father’s Edu|0.0254***|0.0372***|0.0165***|0.0467***|
||(0.00265)|(0.00278)|(0.00292)|(0.00326)|
|Father’s Edu Sq|0.0022***|0.0013***|0.0028***|0.0008***|
||(0.00019)|(0.00020)|(0.00021)|(0.00024)|
|Constant|0.1155***|0.1935***|0.0467***|0.1412***|
||(0.00660)|(0.00790)|(0.00331)|(0.00709)|
|R2|0.213|0.226|0.261|0.284|
|Observations|119825|1079989|132141|716209|

Notes: Robust standard errors in parentheses clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Senior high completion takes the value of 1 if a respondent has completed senior high or more schooling and 0 otherwise. Comparison cohorts are children born between 1957 and 1962, and exposed cohorts are children born between 1968 to 1972. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. Data source: Indonesia’s full count census 2000.

57
18 Table 1: Health coverage of the Thai population in 2000

|Type of Health Coverage|Number<br>(millionpersons)|Percentage of Population|
|---|---|---|
|Medical Welfare Scheme (WHS)|20|33%|
|Health Card Scheme (HCS)|7-8|12%|
|Civil Servant Medical Benefit Scheme (CSMBS)|~7|11%|
|Social Security Scheme (SSS)|6|10%|
|Private Health Insurance|~5.9|~10%|
|No coverage|>15.5|>25%|
|Eligiblefor more than 1type ofcoverage*|?|?|
|Total|61.5|100%|

Source: Na Ranong et al. (2004).

* For example, those who covered by CSMBS from being a parent or a child of a civil servant would be covered by CSMBS but also automatically covered by WHS.
- 8 -

conversations with private citizens in Warsaw confirmed that such prices are typical for free market sales and are also consistent with black market prices of rental housing.

Prices at these levels appear to be well above construction costs. Nationwide, cooperative construction costs averaged about 24,000 zlotys per square meter in 1983-1984. Since that time, construction cost indices have risen by about 59 percent, so current costs are estimated to be about 40,000 zlotys per square meter. In Warsaw and other large cities, costs are reported to be higher, perhaps in a range of 50,000 to 75,000 zlotys per square meter. Thus it appears that the market gyice of housing can exceed construction costs to a considerable degree.-

# Is Investment Less than Investment in Similar Countries?

Poland's level of investment in housing has been lower than that of other countries with either similar levels of GNP per capita or similar levels of housing deficits. Table 3 illustrates housing Table 3: HOUSING INVESTMENT RELATIVE TO CDP IN SELECTED COUNTRIES (Percent)

|Country|1970|Year<br>1975|1980|
|---|---|---|---|
|Czechoslovakia|4.95|5.27|4.01|
|Hungary|4.11|5.99|5.59|
|<br>Poland|3.89|4.34|5.52|
|Romania|n.a.|4.67|4.59|
|Yugoslavia|5.76|6.77|7.76|
|<br>Creece|6.60|5.36|5.76|

Source: Residential construction statistics were taken from the United Nation's National Accounts Yearbook. Construction variables for socialist nations are the sum of gross investment in owner-occupied and non-communal housing. Gross domestic product estimates for socialist nations were taken from World Bank Staff Working Papers. The sources are: (a) Zbigniew Fallenbuchl, 1985; (b) Peter Havlik and Friedrich Levcik, 1985; (c) Edward Hewett, 1985; and (d) Marvin Jackson, 1985.

- 5/ Such differentials between cost and price are indicative of high real rates of return to housing investment (perhaps 15 to more than 30 percent) and suggest the welfare losses associated with not allowing sufficient resources to be invested in the housing sectcr.
**Table 1: CO2 Emissions, National/Regional Shares and Average Annual Growth Rates**

|Country<br>or|CO<br>(Million|2Emissio<br>MetricTo|ns<br>ns CO2)|Cou<br>Sha|ntry/regi<br>re ( perc|onal<br>ent)|Average An<br>Rate ( p|nual Growth<br>ercent)|
|---|---|---|---|---|---|---|---|---|
|Region|1980|2004|2030|1980|2004|2030|(1980-04)|(2004-30)|
|United States|4,755|5,912|7,950|25.9|21.9|18.5|0.9|1.2|
|Canada|453|588|750|2.5|2.2|1.7|1.1|1.0|
|Mexico|231|385|699|1.3|1.4|1.6|2.1|2.4|
|OECD EU|3,263|4,653|4,684|17.8|17.2|10.9|1.4|0.0|
|Japan|938|1,262|1,306|5.1|4.7|3.0|1.2|0.1|
|South Korea|126|497|691|0.7|1.8|1.6|5.6|1.3|
|ANZ|218|424|573|1.2|1.6|1.3|2.7|1.2|
|Russia|3,028|1,685|2,185|16.5|6.2|5.1|-2.3|1.0|
|OECA|1,395|866|1,693|7.6|3.2|3.9|-1.9|2.7|
|China|1,475|4,786|11,239|8.0|17.7|26.2|4.8|3.5|
|India|300|1,113|2,156|1.6|4.1|5.0|5.4|2.7|
|Other Asia|499|1,523|3,141|2.7|5.6|7.3|4.6|2.9|
|Middle East|495|1,320|2,306|2.7|4.9|5.4|4.0|2.3|
|Africa|534|987|1,655|2.9|3.6|3.9|2.5|2.1|
|Brazil|187|337|597|1.0|1.2|1.4|2.4|2.3|
|OLA|437|705|1,254|2.4|2.6|2.9|1.9|2.3|
|Global|18,333|27,042|42,879|100|100|100|1.6|1.9|

_OECD EU refers to OECD Europe; ANZ stands for Australia and New Zealand; OECA for Non-EU countries and Central Asian Republics, and OLA for South America excluding Brazil._ Source: EIA (2007a) for historical emissions and IEA (2007b) for projected emissions.

As illustrated in the figure and table, the global CO2 emissions reached about 27 billion tons of CO2 (tCO2) in 2004 from about 18 billion tCO2 in 1980, registering a 50 percent increase. Asia and Middle East countries have experienced the fastest growth in their CO2 emissions during the 1980-2004 period. For example, the CO2 emissions in China in 2004 are more than double its 1980 level. India and South Korea’s emissions in 2004 are about three times as high as their 1980 emissions. Other developing Asian countries as a whole have also experienced the doubling of their CO2 emissions in 2004 as compared to their 1980 emissions. Intuitively, two factors are responsible for developing Asia and Middle East’s relatively higher growth of CO2 emissions: first, high economic growths and secondly, heavy reliance on fossil fuels. Some OECD countries

-10-
quality multiplicatively tend to show larger gaps; the _LAYS_ approach is the most conservative of the three discussed here, yielding a top-to-bottom ratio of 2.9 versus 3.3 and 6.3 for the other approaches). The last approach, which is the additive approach from section 5.4, yields a smaller gap: its top-to-bottom ratio of 1.7 is similar to that of the simple TIMSS approach.

**Table 5.2: How different approaches characterize the human capital gap between the bottom and top** **<u>performers</u>**

|Approach|Based on|Average value for<br>four bottom‐<br>performing<br>countries|Average value for<br>four top‐<br>performing<br>countries|Ratio (top<br>to bottom<br>performers)|
|---|---|---|---|---|
|(1) Average years of<br>schooling of 25‐ to<br>29‐year‐olds|Barro‐Lee|7.1|14.2|2.0|
|(2) Math test scores<br>on international<br>assessment|TIMSS|377.5|601.8|1.6|
|(3) Learning‐Adjusted<br>Years of Schooling<br>(LAYS)|See text|4.8|13.7|2.9|
|(4) “Human capital”<br>based on returns to<br>schooling of US<br>immigrants|Schoellman (2012)|0.9|3|3.3|
|(5) Quality‐adjusted<br>years of schooling<br>based on test score<br>gains acrossyears|Kaarsen (2017)|2.1|13.3|6.3|
|(6) “Human capital”<br>based on linear<br>combination of years<br>of schooling and test<br>scores|Hanusheck,<br>Ruhose, and<br>Woessman (2015)|1.3|2.2|1.7|

Source: Authors’ analysis based on Barro and Lee (2013), TIMSS 2015 data, Schoellman (2012), Kaarsen (2017), and Hanushek, Ruhose, and Woessman (2017). See text for details on how each measure is implemented.

Note: The sample of countries is the same 35 countries as that for Figure 2.1, except in the case of Row (4). Under the Schoellman approach, data are not available for 8 of those countries, and the analysis underlying Row (4) therefore only includes 27 countries.

These various approaches clearly have different implications for how one assesses 41
contribution of demographic change towards output-per-worker growth and translate the results into per capita terms. The results suggest that over the period 1960-1995 demographic change, particularly due to changes in the youth dependency ratio, accounted for approximately 20 percent of per capita output growth impacts, with larger shares in Asia and Europe.

**Figure 3 A rising working-age population share is positively correlated with GDP per capita growth**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-10 -5 0 5 10 15 20 25 30<br>-4<br>GDP per capita, 1960-2014<br>Average annual percentage point change in real<br><!-- End of picture text --> Percentage point change in the share of the working-age population, 1960-2014 _Source_ : Authors’ estimates _Note_ : Data from World Development Indicators 2015 and UN (2015).

Overall, the positive effect of a larger share of working age population on growth is widely supported in the literature (Bloom and Williamson, 1998; Higgins and Williamson, 1997; Eastwood and Lipton, 2011; Kelley and Schmidt, 1995, 2005, 2007), including its important role in Asia’s growth between 1965 and 1990 (Bloom et al., 2000) and improvements in the accuracy of growth projections by taking age structure into account (Bloom et al., 2007). The evidence of the positive impact of a larger share of working age population on growth is not limited to crosscountry analysis. Mody and Aiyar (2011) suggest that a one standard deviation increase in the working age ratio is associated with an increase of about 0.6 percentage point in per capita income growth across states in India.

As part of the second demographic dividend, national private savings rates have been found to depend on the age composition of the population: individuals are typically net savers when they are working-age and continue to save in old age, on average, but tend to be predominantly consumers when they are children. Regarding the effect of demographic changes on savings, there have been many studies finding that lower child dependency leads to higher saving rates.<sup>12</sup> Loayza

> 12 Please see Mason (1987), Kelley and Schmidt (2005), Higgins and Williamson (1997), and Kinugasa and Mason (2005).

8
Appenidix 1

# List **of** variables.

## For all dummy variables, 1=Yes, O=No.

_Renegotiation (resp. initiated by the firm/by the Government):_ Dummy variable indicating whether there was or not a renegotiation of the concession contract.

_Existence of regulatory body:_ Dummy variable indicating whether there was or not a regulatory body at the time of the concession first coming into operation.

_Price cap:_ Dummy variable indicating whether the tariff regulation imposed by the regulator is a price cap.

_Investment requzrements:_ Dummy variable indicating whether there are or not investment requirements as part of the concession contract.

_Duration since award:_ Indicates the number of year a concession has been in operation since its award.

_Prvate financzng:_ Dummy variable indicating whether the project is funded entirely through private funds (without any financial investment of the state, whether local or national) or not.

_Biddzng process:_ Dummy variable indicating whether there was or not a bidding process to award the concession.

_Minzmum income guarantee:_ Dummy variable indicating whether there is or not a government guarantee in term of minimum income promissories.

_Arbitration process:_ Dummy variable indicating whether there is or not a formal set of arbitration processes stated in the contract providing for the settlement of a dispute between the concession holder and the government, should such a situation arise.

_Duratzon of contract:_ Duration, in years, for which the concession is signed for.

_Corruption/Rule of law/Bureaucratbc quality:_ Indices from Political risk service, International Country Risk Guide; annual values from 1989 to 1995, and 1998 value after. Higher value means less corrupt/better rule of law/better bureaucratic quality.

_Growth:_ Yearly growth rate of GDP in real terms. Source: World Bank and InterAmerican Development Bank.

_Exchange rate:_ Annual evolution of the real exchange rate (calculated as rate of year t - rate of year t-1/rate of year t). A positive value indicates devaluation. Source: InterAmerican Development Bank.

35
<!-- Start of picture text --> Tal<br>at Lp f~<br>E rr<br>c<br>i<br>Saf“E —<br>as — me,<br>= ——<br>5 AN og ee, ra —<br>uni<br>rH<br>tH<br>1965 1S COSaC TA<br>—+—_ Price adjuctment, elasticity of suhetitwtion=1.5<br>—$— Price aduetment, elasticty of subetitution=0.5<br>—a— Obrerved<br><!-- End of picture text -->
- <mark>Birthal, P. S., and J. Hazrana. 2019. “Crop Diversification and Resilience of Agriculture to Climatic Shocks: Evidence from India.”</mark> _<mark>Agricultural Systems</mark>_ <mark>173: 345–54.</mark>

- Blinder, A. 1973. “Wage Discrimination: Reduced Form and Structural Estimates.” _Journal of Human Resources_ 8 (4): 436–55.

- <mark>Carletto, C., S. Savastano, and A. Zezza. 2013. “Fact or Artifact: The Impact of Measurement Errors on the Farm Size–Productivity Relationship.”</mark> _<mark>Journal of Development</mark> Economics_ 103: 254–61.

- <mark>Carr, E. R. 2008. “Men’s Crops and Women’s Crops: The Importance of Gender to the Understanding of Agricultural and Development Outcomes in Ghana’s Central Region.”</mark> _<mark>World Development</mark>_ <mark>36 (5): 900–15.</mark>

- Carter, M., and K. Wiebe. 1990. “Access to Capital and Its Impact on Agrarian Structure and Productivity in Kenya.” _American Journal of Agricultural Economics_ 72 (5): 1146–50.

- <mark>Castañeda, A., D. Doan, D. Newhouse, M. C. Nguyen, H. Uematsu, and J. P. Azevedo. 2018. “A New Profile of the Global Poor.”</mark> _<mark>World Development</mark>_ <mark>101: 250–67.</mark>

- <mark>Chayanov, A. V. 1926/1966.</mark> _<mark>The Theory of Peasant Economy</mark>_ <mark>. H</mark> omewood, IL: R. Irwin for the American Economic Association.

- <mark>Croppenstedt, A., M. Goldstein, and N. Rosas. 2013. “Gender and Agriculture: Inefficiencies, Segregation, and Low Productivity Traps.” World Bank, Washington, DC.</mark>

- <mark>de la O Campos, A. P., K. A. Covarrubias, and A. P. Patron. 2016. “How Does the Choice of the Gender Indicator Affect the Analysis of Gender Differences in Agricultural Productivity? Evidence from Uganda.”</mark> _<mark>World Development</mark>_ <mark>77: 17–33.</mark>

- <mark>Deichmann, U., A. Goyal, and D. Mishra. 2016. “Will Digital Technologies Transform Agriculture in Developing Countries?”</mark> _<mark>Agricultural Economics</mark>_ <mark>47 (S1): 21–33.</mark>

- Dharmasiri, L. M. 2008. “Crop Diversification for Sustainable Agriculture: A Case Study from the Mahaweli Development Programme in Sri Lanka.” _Sri Lanka Journal of Agrarian Studies_ 12 (1): 53–68.

- <mark>Di Falco, S., and J. P. Chavas. 2009. “On Crop Biodiversity, Risk Exposure, and Food Security in the Highlands of Ethiopia.”</mark> _<mark>American Journal of Agricultural Economics</mark>_ <mark>91 (3): 599– 611.</mark>

- Eswaran, M., and A. Kotwal. 1986. “Access to Capital as a Determinant of the Organization of Production and Resource Allocation in an Agrarian Economy.” _Economic Journal_ 96: 482–98.

- <mark>Fabregas, R., M. Kremer, and F. Schilbach. 2019. “Realizing the Potential of Digital Development: The Case of Agricultural Advice.”</mark> _<mark>Science</mark>_ <mark>366 (6471).</mark>

<mark>Feder, G. 1985. “The Relation between Farm Size and Farm Productivity: The Role of Family Labor, Supervision and Credit Constraints.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>18 (2-3): 297–313.</mark> 27
of taxation **have an ultimate** effect on national output. In particular, recent papers (Marsden, 1983; Landau, 1986; Skinner, 1988; Eigen and Skinner, 1989; Koester and Kormendi, 1988) have stressed the negative impact of taxation on output growth rates. A sample of 111 countries is used to examine whether there are readily apparent relationships between fiscal policy and output growth rates.

Perhaps one reason why efficiency or "deadwelght" costs receive little weight in policy discussions is because the notion of efficiency cost is so abstract and difficult to quantify. The next section therefore draws on a simple example of commodity taxation to illustrate what the efficiency cost is, and to distinguish It from smuggling costs. Section III provides a selective review of efficiency cost, and in particular draws on historical examples for illustrative purposes. Section IV presents evidence on the correlation between taxation and output growth rates, while Section V concludes.

# II. Why Taxes Cause Efficiency Losses

Because economists have focused on efficiency as the guiding principle for tax design, it is useful first to provide a simple description of the efficiency cost of taxation. I focus on one example, that of a commodity tax on bicycles, but the r-inciples of efficiency cost extend quite generaily to other types of taxes as well.

Consider the case of the tariff on bicycles in a hypothetical
# **Figure 13. Slowdown in investment and global trade**

_The investment growth slowdown across the world has been accompanied by a downturn in the growth of exports as well as capital goods trade._

## **A. World investment and exports**

<!-- Start of picture text --> Log index, 1995=0<br>1.4<br>1.2 Investment Exports<br>1995-2008 Trend<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2 1995-2008 Trend<br>0.0<br>C. Global capital goods trade and investment<br>Index, 2000=100 Capital goods trade<br>350<br>Investment<br>300 Pre-crisis trend<br>250<br>200<br>150<br>100<br>50<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **C. Global capital goods trade and investment**

## **B. Investment and exports growth: EMDEs**

<!-- Start of picture text --> Percent 1990-2008 average<br>12 2003-08 average<br>8<br>4<br>0<br>Investment Exports<br>D. Investment by 5,000 largest multinational<br>companies<br>US$, billions US$, billions<br>Capital expenditures<br>2500 Acquisition outlays (RHS) 800<br>700<br>2000<br>600<br>1500 500<br>400<br>1000 300<br>200<br>500<br>100<br>0 0<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br>2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **D. Investment by 5,000 largest multinational**

Sources: Haver Analytics; Thomson ONE; UNCTAD (2016); World Integrated Trade Solution, World Bank; World Bank.

A. Denotes levels of real gross fixed investment as well as exports.

B. Weighted averages. Long‐term average for investment starts in 1991 due to data availability.

C. Capital goods trade and gross fixed capital formation expressed in current U.S. dollars. Trend line shows the pre‐crisis (2003‐ 08) trend of the average of capital goods trade.

D. Top 5,000 MNEs capital expenditures and acquisition outlays based on data from Thomson ONE.

35
1.6 percent lower than usual levels. Exports only recover to above the baseline level only three years after the drought, reflecting positive effects of the exchange rate depreciation on export demand. (Figure 11a).

**Figure 11: Drought scenario – Demand and sectoral output impacts**

<!-- Start of picture text --> 2.0 % % 2.0 5.0 % % 5.0<br>1.0 1.0<br>0.0 0.0<br>0.0 0.0<br>-5.0 -5.0<br>-1.0 -1.0<br>-2.0 -2.0<br>-10.0 -10.0<br>-3.0 -3.0<br>Real exports -15.0 Crop output -15.0<br>-4.0 -4.0<br>Real domestic demand Industry output<br>-5.0 Real GDP -5.0 -20.0 Livestock output -20.0<br>-6.0 -6.0<br>-25.0 -25.0<br>-7.0 -7.0<br>-8.0 -8.0 -30.0 -30.0<br>2020 2025 2030 2035 2040 2020 2025 2030 2035 2040<br>Source: World Bank analysis.<br>Note: Percent deviation from baseline<br><!-- End of picture text -->

**As agricultural production and exports decline, the exchange rate depreciates, fueling an increase in import prices** (Figure 12). The nominal exchange rate depreciates 5.4 percent on impact, which translates into higher import prices. For livestock, the negative productivity shock leads to a 9.6 percent increase in prices. Agricultural prices are modeled separately for crops and non-traded agricultural products, which make up 80 and 20 percent of total crop production respectively. Non-traded agricultural prices increase by 8.6 percent, while crop prices, which follow world market prices, move in tandem with the exchange rate. Overall, agricultural prices increase by 6.3 percent. Since raw agricultural products and manufactured food products make up around 10 percent of household consumption in Uruguay, higher agricultural prices as a result of the drought combined with higher import prices leads to an increase in consumer prices of 1.7 percent. Prices then fall in the next year in response to the negative output gap, higher unemployment rate, and lower wages.

19