rafmacalaba/datause-encoder-data huggingface dataset:
40

# **Table 11: Welfare Loss of 100 kwh and 50 kwh monthly lifeline blocs with 80 percent price increase for consumption above the lifeline (as a percentage of total expenditures)**

|Expenditure<br>group|Amount<br>spent on|Quantity per<br>capita*|Household<br>size|Block<br>per capita|Welfar|e loss (percent o|f budget)|
|---|---|---|---|---|---|---|---|
||electricity<br>(1,000 zl)*|||(lOOkwh/hh size)<br>(50kwh/hh size)|E =O|e_= -.5_|E= _26<br>l|
|I (poorest)|42.13|48.77|4.03|24.81<br>12.41|1.4<br>2.1|0.8<br>1.6|0.4<br>1.0|
|2|59.22|68.54|3.41|29.32<br>14.66|1.6<br>2.2|1.0<br>1.6|0.6<br>1.1|
|3|75.40|87.27|3.04|32.89<br>_16.45_|1.8<br>2.3|1.2<br>1.7|0.7<br>1.2|
|4|88.65|102.61|2.72|36.76<br>18.38|1.7<br>2.1|1.1<br>_1.5_|0.7<br>1.0|
|5 (richest)|117.67|136.20|2.47|40.48<br>20.24|1.6<br>1.9|1.1<br>1.4|0.6<br>1.0|
|all|76.62|88.68|3.13|31.94|1.7|1.2|0.6|
|||||15.97|2.2|1.7|1.2|

From household budget survey. Source: Authors'osimulatnons. Assumes pice ncrease for non-lme oc.

# _Some advantages and disadvantages of lifeline rates_

There are a few other caveats on lifeline pricing. It is important that the lifeline block remains small. Lifeline subsidies may not be the best way to subsidize energy if the value of the subsidy to consumers is less than the cost of providing the subsidy. The value of the 26/ Note that in some cases the consumption will be reduced to exactly the block. For these cases the method corresponding to figure 3 was used to calculate the welfare loss.
force participation, biases the point estimates of the coefficients of right-hand-side variables (Hausman, Abrevaya, and Scott-Morton, 1998; Hausman, 2001).

Assessing the internal validity of survey measures, although common in psychological sciences, remains scarce in economics.<sup>4</sup> In this paper we focus on two key characteristics of surveys that potentially influence the labor statistics they generate: the structure of the labor-related questions in the questionnaire and the respondent selected for the interview. Our results show that there are significant differences observed with respect to measures of labor force participation, labor supply, earnings, and the distribution of activity across survey designs. Labor force participation rates, for example, vary by as much as 10 percentage points, across the four survey assignments. Using a labor module with no screening questions generates lower female labor force participation and lower rates of wage employment for both men and women. Response by proxy rather than self-report yields lower male labor force participation, lower female working hours, and lower employment in agriculture for men. Survey design matters to measuring labor and, moreover, valid comparisons across surveys of different design can be compromised by these differences.

The structure of the paper is as follows. In the next section, we discuss briefly the wealth of studies from the U.S. and other high-income countries and the scant evidence from developing countries. In Section 3, we describe the experimental design. The fourth section provides a description of the data collected, while Section 5

> 4 In psychological science the sensitivity of results to measurement goes under the label of ‘method bias’; see Podsakoff et al (2003) for an overview. With the increasing cross-fertilization of psychology and economics, the latter is increasingly paying attention to the internal validity of measures, especially in the field of experimental economics, where there is a strong interest in the effect of ‘framing’ or how a question / puzzle / game is presented to a subject (see Tversky and Kahneman, 1981 for early work in this area).

4
23 can be leveraged to provide a considerable level of insurance against year-to-year fluctuations, especially if the country is willing to restrict coverage to “catastrophic” market declines and to give up a substantial part of the aid program when prices are high. Implementing such arrangements would require novel arrangements, both to hedge IDA commitments to enable disbursements to vary in response to oil markets and to design loan agreements with contingent levels of financing. But there appear to be no specific legal barriers in the way and indeed, the principle of such operations has been endorsed by IDA. Some of the approaches can draw on precedent operations that provide for insurance against natural disasters.

There are of course limits to the types of insurance that can be provided through an aid program. Even for an oil exporter it would be impossible to hedge the long swings in oil prices that have tend to occur around every twenty years or so. Self-insurance through the accumulation of large balances in a Sovereign Wealth Fund, combined with determined efforts to diversify the economy, is probably the only way to approach this problem. But not all countries have the institutional conditions to succeed and for many the risk is high that the fund will be raided, and so transfer resources from a more towards a less responsible government.

This paper is only a first effort. More detailed assessments would be needed to move these possibilities towards actual operations.

# **References**

African Development Bank. 2009. “Managing Oil Revenue in Uganda: A Policy Note”.

Arezki R., A Dupuy and A. Gelb. 2012. “Resource Windfalls, Optimal Public Investment and Redistribution: The Role of Total Factor Productivity and Administrative Capacity.” Working Paper 12/200, International Monetary Fund.

Arezki R., K. Hamilton and K. Karimov. 2011. “Resource Windfalls, Macroeconomic Stability and Growth: the Role of Political Institutions.” Working Paper 11/42, International Monetary Fund.

Bulir A. and A. J. Hamman. 2006. “Volatility of Development Aid: From the Frying Pan into the Fire?” Working Paper 06/65, International Monetary Fund. March.

Collier, Paul. 2007. The Bottom Billion: Why the Poorest Countries are Failing and What Can be Done <u>About It. Oxford University Press.</u> Claessens S. and P. Varangis. 1994. “Oil Price Instability, Hedging and an Oil Stabilization Fund: The Case of Venezuela”. Policy Research Working Paper 1290, World Bank, April.

Dabla-Norris E., J. Brumby, A. Kyobe, Z. Mills and C. Papageorgiou. 2011. Investing in Public Investment: An Index of Public Investment Efficiency,” Working Paper 11/37, International Monetary Fund, February.

Dagher J, J Gottschalk and R. Portillo. 2010. “Oil Windfalls in Ghana: A DSGE Approach” Working
more mixed. Several studies examining the impact of the release of stratospheric aerosols from volcanic eruptions have found improvements in photosynthesis and carbon sequestration in natural ecosystems.<sup>17,19</sup> Work specifically examining the impact of these eruptions on crop yields, however, suggests that both C3 (e.g. soybean) and C4 (e.g. maize) crops suffered yield declines as the reduction in total sunlight dominated the increase in diffuse light.<sup>7</sup> While the role of wildfire smoke in changing crop yields has received less attention than clouds or aerosols broadly, available evidence suggests that the effect is similar to other sources of aerosols. The 2018 wildfire season, a particularly intense season, led to large increases in the fraction of light that was diffuse in California’s Central Valley, a location that typically has few clouds and receives mostly direct sunlight.<sup>23</sup> This led to increases in daily integrated gross ecosystem productivity of between 1.2 and 4.2%. These effects were concentrated in alfalfa and corn and were substantially larger than reductions in productivity due to associated increases in surface level ozone.<sup>23</sup> These estimates come from a single research plot and one especially intense fire season in an area with relatively high levels of direct radiation. It remains unquantified how wildfire plumes impact yields at a landscape level, in areas with higher average cloud cover, and across many years.

This study examines how North American smoke plumes have affected corn and soybean yields in the Midwestern and eastern United States over the period 2008–2020. This region experiences frequent exposure to smoke plumes over our sample period (Figure 1). We obtain data on smoke plumes from the National Oceanographic and Atmospheric Agency’s (NOAA) Hazard Mapping System (HMS) and data on the location and yield of corn and soybean crops from the United States Department of Agriculture’s (USDA) National Agricultural Statistics Service (NASS). Smoke plumes are divided into “low density” and “high density” categories, because we hypothesize that different smoke densities may have different signed impacts.

To identify the impact of smoke plumes on crop yields, we leverage the quasi-random variation in exposure to smoke plumes within a county from year to year. We estimate a standard two-way fixed effects model that examines how quasi-random exposure to smoke plumes in a given county and year impacts yields in that year. To isolate the impact of smoke plumes operating through the amount and type of sunlight available for photosynthesis, we control for a range of weather conditions that might influence yield including temperature, precipitation, and cloud cover.

Lastly, we estimate the impact of smoke plumes on crop yields in 2050, taking into account the current state of knowledge about future burned area and smoke exposure. Because there is large uncertainty in the future quantity and density of smoke plumes, we analyze how crop yields will be impacted under a wide range of changes in smoke exposure.

3
# 3.5 Descriptive Statistics

We briey describe the average characteristics of farming individuals in our sample (Table A.4). More than half of the individuals are women, and the prevalence of female headship is consistently high (approximately 30 percent) for the region (TIA, 2008). The average farmer is 38 years old with two years of schooling. Most plot owners are married with three children, and live in a single-room house made of mud and sticks with a palm or bamboo roof (not reported). Farmers possess 2.2 hectares of land on average, with a standard deviation of 2.1.

CFs are more knowledgeable (Tables 1 and 2), more educated, and wealthier (Table 3) than other farmers. While CFs are positively selected in attributes, they are also well known in their communities: 84 percent of farmers in the control group declared knowing them personally. However, only 72 percent of the same group of farmers reported knowing that these individuals assumed a role as CF in their community.

We also note that usage of demonstration plots was quite high and not statistically dierent across treatment and control communities (Table A.3). Of the CFs in treatment and control communities, 85 percent maintained a demonstration plot.<sup>18</sup> Thus, changes in patterns of CFto-other-farmers information diusion across the two modalities can be interpreted as resulting from variations at the intensive margin of CFs' activities (e.g., number of techniques demonstrated, quality of the demonstration).

# 3.6 Balance

We use data from the baseline CF survey as well as time-invariant and retrospective information collected in the 2012 household survey to check for balance across treatments. Table 1 indicates minor dierences between CFs in the treatment and control communities. Treatment CFs spent almost four more hours a week working as a CF (pre-intervention) and had slightly more recent training when we condition on being formally trained. Control CFs were exposed to a greater number

> 18There was no instruction, however, as to what type of plot should be used for demonstration activities. CFs could choose to use their own, private plot or communal land. Hence, we present the demonstration results for any plot (own or not).

13
|STATE-OWNED|1.0|2.1|5.2|23.2|69.3|66.2|10.1|
|---|---|---|---|---|---|---|---|
|COLLECTIVE SECTOR|0.1|0.5|1.3|1.8|3.6|3.2|1.0|
|PRIVATE SECTOR|1.1|3.6|4.4|8.5|7.0|12.2|4.6|
|FOREIGN-INVESTED|0.4|1.6|1.7|5.5|2.0|7.3|2.3|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

_Source: VHLSS 2008_ Some gender differentials are apparent in the returns to education in terms of labor force participation and employment; these tend to be worse at lower educational levels. Women with no degree are more likely than their male counterparts to be inactive. Women with primary or secondary education are slightly less likely than their male counterparts to be working, while those with college or university education are more likely than their male counterparts to be working.

The analysis so far has shown a relatively positive evolution of labor market performance with an increase in labor force participation and employment-to-population ratio for both men and women. Some regional and gender differences have appeared, illustrating that women in certain regions may be more at risk of being disadvantaged in the labor market. This analysis therefore raises several questions. In particular, has the increase in employment been in productive jobs or not? To what extent has migration influenced and/or counter-balanced regional disparities? Although it seems that most women enjoy equal access to employment, has this employment been of the same quality as that of men’s?

# _Employment in the informal sector_

A challenge in most low and middle-income countries is the size of the informal sector. This sector is important because it operates outside legal boundaries, not contributing to taxation and providing working conditions that are not supervised. Moreover, employment in the informal sector is often of low productivity and has high turnover, which means workers experience repeated spells of unemployment. Of course, this sector is far from homogenous, including street vendors trying to survive on meager earnings as well as successful small entrepreneurs deliberately choosing not to burden themselves with sometimes overwhelming rules and regulations (for recent evidence on this see Bosch and Maloney, 2010).

13
45 Table 6: Inequaity within and between Racial Groups

||Mean|Minimum|Maximum|
|---|---|---|---|
|Overall Inequality|0.514|0.234|1.228|
|Wfthin-GroupInequality|0.324|0.198|0.839|
|Between-GroupInequality|0.189|0|0.785|
|ShareofBetween-GroupInequalityinOverallInequality|36.8%|0%|63.9%|
|Percentageofcommunitieswhereoneracialgroupaccountsformore|32.2|0|1|
|than95%ofjurisdictionpopulation||||
|IndexofRacialHeterogeneity|0.264|0|0.666|

> In South Africa, the population census allows for four specific population groups (African, White, Colored, Asian/Indian) and a fifth category "other". For reasons related to the availability of data, the inequality and racial

> heterogeneity measures presented here refer to three population groups: African, White, and other. Index of Racial Heterogeneity is equal to one minus the sum of squared shares of each racial group. It can be interpreted as the

> probability of two households selected at random belonging to different racial groups.
<!-- Start of picture text --> Error term Random effect<br>. .<br>ij<br>0.4- *°<br>25-<br>24 £2<br>3 o<br>g 5 02-<br>c E<br>S2Sa o0- s8<br>5 -—<br>3 =)®<br>$s §<br>Go fe)<br>-2.5- 402nol<br>*<br>¢° ir<br>. O4- =4<br>Theoretical quantiles Theoretical quantiles<br><!-- End of picture text --> <!-- Start of picture text --> Error term Random effect<br>. e<br>?<br>0.2-<br>25-<br>2 o<br>2s 23S<br>7 £<br>= £<br>832oo S& 00-<br>.0- —<br>53 =3<br>$s 6<br>3S 0.2-<br>-2.5- °e<br>.<br>.<br>‘e .<br>P -04-°<br>Theoretical quantiles Theoretical quantiles<br><!-- End of picture text -->
**Figure 5.- Informality rates for top importing industries,**<sup>**3**</sup> **2009-2017.**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Textiles Chemicals Metals<br>Machinery Transportation Vegetables<br>Minerals Manufactures Services<br><!-- End of picture text -->

**Figure 6.- Mean annual real wages for top importing industries and the manufacturing and services sector,**<sup>**3**</sup> **2009-2017**<sup>**5**</sup>

<!-- Start of picture text --> 4,500<br> 4,000<br> 3,500<br> 3,000<br> 2,500<br> 2,000<br> 1,500<br>200920102011201220132014201520162017<br>Textiles Chemicals Metals<br>Machinery Transportation Vegetables<br>Minerals Manufactures Services<br><!-- End of picture text -->

# **3. Is the Liberalization Strategy Increasing Imports?**

# • **Overview of the Gravity Approach**

The Gravity model is an often-used approach to estimate the relationship between bilateral trade flows and variables that affect trade including trade policy (e.g. Head and Meyer 2015, Chaney 2018). Its name references the gravitational pull two countries exert on each other based on their proportional economic mass. Thus, to predict the volume of trade between two countries the model uses each country’s gross domestic product (GDP), the distance between them, and their relative trade frictions or trade costs (Tinbergen 1962).

We follow a familiar foundation of the empirical approach by Anderson and van Wincoop (2003) and model exports from region i to region j (xij ) as a function of gross domestic production in regions i and j (yi and yj ), world nominal income (y<sup>w</sup> ), the cost in j of importing a good from i (tij), a substitution elasticity σ between all goods from different countries, and aggregate price levels (consumer price indices) for each country Pi and Pj: For relative trade frictions, we include the aggregate price terms to capture “multilateral resistance”, distance (modeled as the geographic distance between countries), a wide range of information costs –such as language and colonial lineage (Egger and Lassman 2012, Fidrmuc and

> 5 The 2012 dips in earnings for some industries can be explained by the political context surrounding the 2011 Egyptian revolution and its impact on some economic variables.

6
generated). Given the granularity of our control data, we aggregated this data into square cells with sides measuring 5 arc minutes in length (approximately 10km).

For the EDS-RDC, conducted in DRC in 2007, 8,891 households were interviewed. The data are representative at the national level, for urban and rural residence, and for all eleven provinces. As EDSRDC does not provide a direct measure of income, consumption or expenditures, we develop several alternative measures of welfare described below. From EDS-RDC we obtain two outcome variables: (i) a wealth index, and (ii) a multi-dimensional poverty indicator. The first indicator captures an economic measure of well-being as indicated by the level of wealth that households accrue over time, and arguably captures the longer term economic effects of infrastructure. It is calculated including all items in the EDSRDC household questionnaire that are related to household goods, dwelling construction, and access to services and resources like electricity, water, and sanitation. The second indicator, our multi-dimensional poverty measure, summarizes both economic well-being as well as human capital, and captures a more holistic measure of living standards. We follow Alkire and Santos (2010) to calculate the MultiDimensional Poverty Index (MPI) for each household. The MPI is a weighted sum of ten indicators of deprivation across three dimensions: education, health, and standard of living. We adhere to convention and use equal weights for each of the three dimensions and for indicators within each dimension. A household is considered to be multi-dimensionally poor if it is deprived in three of the ten weighted indicators. Appendix I gives more specific details on how this index was constructed.

# **5. Empirical Framework**

In estimating the effects of conflict and transportation costs on the wellbeing of households, we consider three alternative specifications. First, we examine whether the impact of conflict differs depending on whether it is located near markets, or near households. Then, we investigate the combined effect of both transportation costs and conflict on household wellbeing by including an interactive term. This section discusses each of these specifications in greater detail and explains our identification strategy.

## 5.1 Conflict near market versus conflict near household

Does the impact of conflict differ depending on whether it is located near the market or near the household? Presumably, the effects of conflict near markets could be avoided by retreating to subsistence modes of livelihood, whereas conflict near the home is likely more devastating because its negative effects cannot be avoided even by adopting subsistence farming. To investigate this, consider the following specification: 17
<!-- Start of picture text --> Congo<br>=<br>ml<br>i) 10 20 30 40 Quantile50 60 70 80 90 100<br>—+— Education ——+— Enperience<br>—+*— Numberof Children ——*—\ Married<br>—*— Industry —+— Monthly Hours<br><!-- End of picture text --> <!-- Start of picture text --> Ghana<br>=<br>mM<br>° intee tta a a ey<br>~<br>i) 10 20 30 40 Quantile50 60 70 80 90 100<br>—*— Education ——*— Enxperience<br>—+*— Numberof Children ——*#—\ Married<br>—*— Industry —+*— Monthly Hours<br>Savings<br><!-- End of picture text -->
<u>Appendix 1. Attrition analysis, Check 3</u>

||Non-attritor|Attritor||Treatment<br>N|<br>Control<br>on-attritor||DD|Model:|
|---|---|---|---|---|---|---|---|---|
|School Characteristic at Baseline|(NA)|(A)|NA-A|(T)|(C)|T-C|A|A x T|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Number of teachers|16.63|14.93|-1.696|16.87|16.37|0.501|-0.964|-2.149|
||(4.90)|(3.26)|(1.89)|(5.45)|(4.28)|(1.02)|(2.29)|(4.19)|
|Percentage of female teachers|0.2|0.18|-0.02|0.21|0.18|0.031|-0.009|-0.015|
||(0.10)|(0.06)|(0.04)|(0.10)|(0.10)|(0.02)|(0.05)|(0.08)|
|Percentage of permanent teachers|0.61|0.76|0.149*|0.62|0.61|0.007|0.133|0.062|
||(0.23)|(0.23)|(0.09)|(0.20)|(0.25)|(0.05)|(0.11)|(0.20)|
|Teacher is in class right now|0.14|0.11|-0.036|0.17|0.12|0.052|0.005|-0.103|
||(0.19)|(0.06)|(0.07)|(0.19)|(0.19)|(0.04)|(0.09)|(0.16)|
|Teacher is absent|0.36|0.43|0.071|0.3|0.43|-0.125**|-0.021|0.22|
||(0.30)|(0.16)|(0.11)|(0.29)|(0.30)|(0.06)|(0.14)|(0.25)|
|Number of students|221.16|182.49|-38.67|235.07|206.02|29.05|-31.626|-0.702|
||(134.38)|(86.71)|(51.71)|(148.65)|(116.66)|(27.73)|(62.47)|(114.20)|
|Number of toilets|7.13|5.73|-1.404|7.6|6.63|0.97|-1.213|0.134|
||(3.92)|(1.51)|(1.50)|(4.21)|(3.57)|(0.81)|(1.81)|(3.30)|
|Electricity|0.39|0.31|-0.08|0.38|0.39|-0.012|-0.04|-0.15|
||(0.45)|(0.36)|(0.18)|(0.45)|(0.45)|(0.09)|(0.21)|(0.39)|
|Library|0.47|0.34|-0.132|0.49|0.45|0.045|-0.064|-0.198|
||(0.46)|(0.37)|(0.18)|(0.47)|(0.46)|(0.10)|(0.22)|(0.40)|
|Presence of BOG|0.96|0.98|0.023|0.95|0.97|-0.018|0.015|0.014|
||(0.18)|(0.02)|(0.07)|(0.20)|(0.15)|(0.04)|(0.08)|(0.15)|
|Number of observations|94|7|101|49|45|94||101|

Note: Columns (1), (2), (4) and (5), mean and SD; Columns (3) and (6), difference and SE; Columns (7) and (8), coefficient of a regression of each characteristic versus attrition, treatment and interaction.

29