rafmacalaba/datause-encoder-data huggingface dataset:
# _More ambitious climate policy_

In the next set of simulations, the implemented carbon price is increased to incite an even larger decarbonization of the economy. Initially the carbon price is the same as in the previous simulations but starting in 2021 the carbon price is increased a further 5% per year in real terms. This results in real carbon prices of 30 USD (27 EUR) and 60 USD (54 EUR) in 2027 and 2041, values at the low-end and midpoint the OECD (2018) estimates of the carbon prices among OECD member countries in 2020. Sweden has a carbon tax of 119 USD already (World Bank 2020a), a level not reached in this scenario until 2057. Because of compounding the carbon price in the end period reaches a very high of about 1400 (2011 USD) in 2100. As Figure 23 shows, this level of climate policy slows the rise in energy-related emissions, but they nevertheless almost double from 2017 levels.

In the steady state, after full adjustment to the very high carbon prices at the end period, carbon levels are about 70 percent higher than the 2017 level, whereas it would have been roughly 150 percent higher with no carbon price. In all three revenue-use variants carbon emissions from energy per unit of GDP decline to close to zero (Figure 23).

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Table 4: Effects on summary outcome measures (indices)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Women’s employment|Women’s decision-making|Women’s mental health|Women’s fnance|Child development|
|**Full sample**||||||
|ITT estimate|0.083**|0.002|0.162**|0.174***|0.203***|
||(0.040)|(0.045)|(0.069)|(0.058)|(0.058)|
|Lee lower bound ITT|0.069*|-0.026|0.123*|0.153**|0.190***|
||(0.040)|(0.046)|(0.068)|(0.057)|(0.057)|
|Lee upper bound ITT|0.116***|0.024|0.202***|0.201***|0.222***|
||(0.036)|(0.043)|(0.068)|(0.056)|(0.055)|
|Rand. inference pvalues|0.180|0.920|0.200|0.120|0.020|
|TOT estimate|0.351**|0.007|0.686**|0.742***|0.882***|
||(0.168)|(0.190)|(0.281)|(0.258)|(0.282)|
|Observations|1967|1967|1957|1967|1618|
|_R_<sup>2</sup>|0.081|0.108|0.117|0.083|0.608|
|Control mean|-0.000|-0.008|-0.000|-0.000|0.000|
|**Have child aged 0-2**||||||
|ITT estimate|0.094***|-0.032|0.120**|0.175**|0.186***|
||(0.033)|(0.052)|(0.057)|(0.069)|(0.060)|
|Lee lower bound ITT|0.074**|-0.063|0.075|0.151**|0.166**|
||(0.033)|(0.053)|(0.056)|(0.068)|(0.061)|
|Lee upper bound ITT|0.130***|-0.004|0.166***|0.207***|0.220***|
||(0.031)|(0.048)|(0.058)|(0.067)|(0.059)|
|Rand. inference pvalues|0.100|0.660|0.180|0.080|0.020|
|TOT estimate|0.367***|-0.129|0.472**|0.692**|0.701***|
||(0.130)|(0.195)|(0.221)|(0.273)|(0.230)|
|Observations|1127|1127|1121|1127|717|
|_R_<sup>2</sup>|0.074|0.118|0.120|0.096|0.601|
|Control mean|-0.000|-0.006|-0.000|-0.001|-0.000|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10. Each cell represents one estimate of the mean effect for a family of outcomes. Intent to Treat (ITT) effects estimated using the ANCOVA specification in equation 1 and TOT effects estimated using the specification in equation 2. Employment index in column 1: An indicator for working at least 1 hour on activity in the last 30 days, number of hours spent on activity in the past 30 days, total monthly income and IHS of total monthly income for salaried, agricultural, and non-agricultural activities, and time in hours doing paid work in the past 24 hours (outcomes in Tables 5, 6, 7, and column 4 of Table 10). Empowerment index in column 2: input into household decisions (on own income, household savings, major household purchases, childbearing, and children’s education) autonomy over household decisions, (-) prevented by husband or household member from visiting relatives or working outside the home, (-) believes that a husband is justified to beats his wife if she burns food or neglects children, partner contributes to food preparation, housekeeping, laundry, and childcare (outcomes in Table 8, 9, and columns 1 and 2 of Table 10). Mental health index in column 3: (outcomes in Table 11). Finance index in column 4: saved money during the past 12 months, saved money in a formal institution, saved money in an informal institution, able to pay 20,000 FCFA in case of an emergency (outcomes in Table 12). Child development index in column 5: gross motor score, fine motor score, and language score (outcomes in Table 13).

36
Policy Research Working Paper 10171

# **Abstract**

This paper assesses the reliability of poverty maps derived from remote-sensing data. Employing data for Malawi, it first obtains small area estimates of poverty by combining the Malawi household expenditure survey from 2010/11 with unit record population census data from 2008. It then ignores the population census data and obtains a second poverty map for Malawi by combining the survey data with predictors of poverty derived from remote sensing data. This allows for a clean comparison between the two poverty maps. The findings are encouraging—although that assessment depends somewhat on the evaluation criteria employed. The two approaches reveal the same patterns in the geography of poverty. However, there are instances where the two approaches obtain markedly different estimates of poverty. Poverty maps obtained using remote sensing data may do well when the decision maker is interested in comparisons of poverty between assemblies of areas, yet may be less reliable when the focus is on estimates for specific small areas.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at rvanderweide@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
# **Figure 1: Number of deaths by year among siblings of women aged 15-49. Rwanda Demographic and Health Survey 2000.**

<!-- Start of picture text --> 4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>Year<br>197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999<br>Number of deaths<br><!-- End of picture text --> 21
km/h. This would reduce the time it takes to travel from these cities to market centers of 100,000 people from an average of 5 hours to 3 hours.

- In the second case, we simulate improvements of roads around high profit cities– Iganga, Mpigi and Mubende, which are in the country’s main industrial agglomeration. Again these road improvements are assumed to increase travel speeds from 60 to 100 km/h.

We focus on road improvements as this is the most important modes of transportation in the country. The World Bank’s Country Economic Memorandum (World Bank 2006) for Uganda identifies that improvements in the national road network are extremely relevant for Uganda’s economic growth given their strategic functions, relative high usage (they carry most of Ugandan traffic) and thus –in practice—their yielding of highest social returns to capital. In terms of usage, national roads account for 15 percent of the network but carry about 80 percent of the total traffic. Further, the paved segments of the national network experiences greatest traffic and usage. In 2003, the 25% paved length of the national network carried 70% of the vehicle-kilometers. On average, paved roads carried 2670 vehicles per day, while unpaved roads carried only 346; 91% of the paved national roads and 26% of the unpaved national roads carried more than 500 vehicles per day.

In our model, improving roads would have two effects. First, it would increase access to markets, and second it would increase the spatial extent of agglomeration economies as firms can effectively be ‘closer’ to a larger number of firms in their industry. We use firms in the food and beverages industry to calibrate the simulation. The location choice model shows that agglomeration economies and infrastructure are both important factors. We find that these improvements only provide modest gains for industry location in the Northern districts of Gulu and Lira. In Gulu, the share of national food and beverage establishments increases from 1.7 percent to 2 percent. In Lira, it moves from 1.9 to 2.3%.

In comparison, in Iganga district that adjoins Kampala, transport improvements increase the share of establishments that would locate in the district from 5.8 percent to 10.5 percent. However, there are only small gains from additional investments in Mpigi 21 -
immediately. Ninety-three percent of those offered a test consented to a test; of these, only five respondents opted to not learn their status.<sup>9</sup> There are only minimal differences between those who refused the test and those who accepted: men with no schooling were less likely to accept the test; women from wealthier households were more likely to accept the test. Both men and women who had been tested in the past were more likely to accept the offer of a test. Of those tested, less than one percent of the men (2 out of 237) and two percent of the women were HIV positive (4 out of 274).

In Table 1 we report sample characteristics at the 2008 round, by gender and by whether respondents were offered an HIV test. The results reflect the randomization process, whereby the principal investigators generated a random number program in Stata for selection. The variables are well-balanced across the control and treatment groups. Variable-by-variable individual tests reported in the table cannot reject that the means are the same for the two groups for almost all the variables.

Women in the sample range in age from 14 to 21 (by design), with a mean age of 17. Nearly all women have some schooling, and 32 percent have attended secondary school. At the time of the testing offer in 2008, 43 percent were still attending school. Fifty-nine percent of women reported ever having sex, and 44 percent reported having had sex in the 12 months prior to the 2008 interview. Fifteen percent reported ever being pregnant and 21 percent were married. The average age of the men in the sample is 20, ranging from 14 to 26.<sup>10</sup> Thirty-one percent of men have attended at least some secondary school, and 62 percent have attended some primary school. Twenty-four percent of men were currently attending school. The lower

> 9 For comparison, Trinitapoli and Yeatman (2011) had testing rates of 80 percent for a sample of young adults in southern Malawi in 2009; acceptance rates were 80 percent. DHS 2010 had testing rates of about 90% for women and 83% for men (15-24). Baird et al (2013) tested 98% of their sample of adolescent women.

> 10 Because of the later age of marriage for men, the MTM project purposefully aimed for the sample of young men to be older than that of young women.

9
increasingly decouple agricultural production from its environmental impacts. Central to this will be the design of agricultural subsidies that profoundly influence the structure, function and trajectory of agriculture-food systems. As this paper has demonstrated, even when eminently justifiable, poorly designed subsidies are enmeshed in tradeoffs suggesting the need to clearly analyze the benefits and costs of whether, what, and how to subsidize.

# References

Abay, K.A, G. Blalock, and G. Berhane. 2017. “Locus of Control and Technology Adoption in Developing Country Agriculture: Evidence from Ethiopia”. _Journal of Economic Behavior and Organization_ 143: 98115.

Barrett, C. B., & Bevis, L. E. (2015). The self-reinforcing feedback between low soil fertility and chronic poverty. _Nature Geoscience_ , _8_ (12), 907-912.

Basu, N. B., Van Meter, K. J., Byrnes, D. K., Van Cappellen, P., Brouwer, R., Jacobsen, B. H. et al. (2022). Managing nitrogen legacies to accelerate water quality improvement. Nature Geoscience, 15(2), 97-105 Bevis, L. 2018. “Invisible Heterogeneity in Crop Zinc Concentration and Child Zinc Intake in Rural Uganda” HarvestPlus Working Paper Bevis, L. E., Kim, K., & Guerena, D. (2022). Soil zinc deficiency and child stunting: Evidence from Nepal. Available at: https://leahbevis.files.wordpress.com/2021/12/bevis-kim-and-guerena-2021-4.pdf Carter, M. R., Laajaj, R., & Yang, D. (2013). The impact of voucher coupons on the uptake of fertilizer and improved seeds: Evidence from a randomized trial in Mozambique. _American Journal of Agricultural Economics_ , _95_ (5), 1345-1351.

Carter, M, Lybbert, T and Tjernström, E (2015), "The Dirt on Dirt: Soil Characteristics and Variable Fertilizer Returns in Kenyan Maize Systems" Working paper, USAID Feed the Future Innovation Lab for Assets and Market Access, Davis, CA Carter, M, Laajaj, R and Yang, D (2019), “Subsidies and the Green Revolution in Africa: Direct and Social Network Effects of Randomized Input Subsidies in Mozambique”, NBER Working Paper 26208.

Cassou, E., S. M. Jaffee, and J. Ru. 2018. _The Challenge of Agricultural Pollution: Evidence from China, Vietnam, and the Philippines._ Washington, DC: World Bank.

Chatterjee, S., Kapur, D., Sekhsaria, P. and Subramanian, A. 2022. Agricultural Federalism: New Facts, Constitutional Vision, Economic and Political Weekly, 57(36) 2022 Chilimba, A. D., S. D. Young, C. R. Black, M. C. Meacham, J. Lammel, and M. R. Broadley. 2012. “Agronomic Biofortification of Maize with Selenium (Se) in Malawi.” Field Crops Research 125: 118–128 Cole, S., & Sharma, G. (2017, July). The promise and challenges of implementing ICT in Indian agriculture. In _India Policy Forum_ (pp. 11-12).

Cox, Cindy L and Kwon, Ho Young and Koo, Jawoo, 2015, The Biophysical Potential for Urea Deep Placement Technology in Lowland Rice Production Systems of Ghana and Senegal. IFPRI Discussion Paper 1448, Available at SSRN: https://ssrn.com/abstract=2631732 17
may have been, there is significant evidence that they are not particularly successful in protecting poor households, and that they can have undesirable consequences.

To put these matters into context, households in Guatemala spend around 10% of their household budget on water, energy and telecommunications services. Over 50% of this expenditure goes on energy for cooking and heating, and over 25% goes on energy for lighting and powering appliances, while barely 0.5% of income is spent on water services. The overall budget share is relatively constant across consumption quintiles, although the composition of the budget shifts away from cooking fuels and towards telecommunications for richer households (Figure 4.1). Although only a tiny fraction of the poorest households have access to telephones, those that do so spend as much as 5% of their income on the service.

**Figure 4.1: Expenditure on modern utilities as a percentage of consumption**

<!-- Start of picture text --> 14<br>12<br>10<br>Cooking and heating<br>8 Lighting and appliances<br>6 Water<br>Telecoms<br>4<br>2<br>0<br>1 2 3 4 5 Total<br>Consumption quintile<br>Percentage of household consumption<br><!-- End of picture text --> _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala

# **4.1 Electricity**

As a consequence of the electricity reforms, the newly privatized distribution companies were allowed to pass on to customers the changes in the cost of purchasing energy. Since Power Purchase Agreements signed between generators and distributors were indexed to the US$ and the price of oil, prices began to rise substantially from the end of 1998 (Figure 4.2). EEGSA experienced the steepest rises, with tariffs increasing 85% over the three year period 1998/01. While for DEORSA and DEOCSA the increases were somewhat lower at 55%-60%.

In order to protect domestic consumers from rising electricity prices, the government introduced a social tariff (‘tarifa social’), which held the price of electricity at around US$0.08 per kWh for all residential customers consuming up to 500 kWh. The cost of this subsidy, estimated at over US$57 million per year, was met by INDE on the basis of state transfers. It is noteworthy that even with the social tariff, about a quarter of all complaints received from consumers by the regulatory agency CNEE during 2000 are about tariffs being excessively high.

29
direction. Wages decline by 46 percent decline in villages where no houses have access to radios (6 percent of the sample), compared to a 26 percent decline in villages which are at the 75th percentile of the radio access distribution. While encouraging, these results are at best tentative. Endogenous variation (these variables are only available in the 1998 and not baseline, i.e. 1981, census), as well as the suitability of these two indicators as proxies for the restrictiveness of the female labor market, requires that they be viewed with some caution.

One may posit more nuanced demand-side explanations for such wage eects that introduce heterogeneity in the quality of teachers. We believe such stories are neither plausible nor empirically supported. For example, if increased demand spurs perverse competition across (private) schools (with parents unable to judge/evaluate quality), this may result in a race to the bottom. In such a story, wages drop in villages with a GSS not because of the supply shifter but because the increased demand causes so much school entry/expansion that teacher quality (and hence wage) drops. However, given the large average wage drops we nd, this would imply that the quality of the marginal teacher is substantially worse. Yet, not only is this implausible since parents are reasonably aware of teacher quality (Andrabi et al. 2009) but our regressions control for the number of schools and show that villages with more schools have higher wages. In other words, competition raises, not perversely lowers, wages.

# 4.4 Discussion

The wage estimates we obtain are also broadly consistent with a set of arbitrage conditions that should hold in equilibrium under a supply-side explanation. To see this, consider an entrepreneur who plans to set up a private school in a village without a GSS. She has several potential options, and for our results to be plausible, it must be (as we argue below) that these options are not viable.

First, she could hire a male instead of a female teacher. If we assume that men have fewer/no occupational and geographic mobility restrictions, this suggests that (equivalent) 27
empirical growth literaturs. The relationship between growth and a particular variable of inter.st is considered robust if it remains statuisticaliy significant when other variables are included. Even thcugh we try not to include variables that on a priori grounds measure the same phenomenon as the variable of interes:, almost all identified relationships become insignificant with the inclusion of only one additional variable, and many publicized coefficients actually change sign when other explanatory variables are added. Two themes emerge from our investigation. First, measures of economic policy are related to long-run growth. Taken individ: ally or in groups, there are many cross-country econometric specifications in which macroeconomic policy indicators are significantly correlated with long-run average growth rates. The second theme is that the cross-country statistical relationships between long-run average growth rates and almost every particular macroeconomic indicator considered by the profession are fragile: small alterations in the "other" explanatory variables overturn past results. In particular, the broad array of fiscal expenditure variables, monetary policy indicators, political stability indexes, human capital and fertility measures considered by the profession are not robustly correlated with growth; and newer indicators that we have assembled to capture exchange rate, tax, and fiscal expenditure policies are also not robustly correlated with growth. This implies that there is sot a strong independent statistical relationship between most popular macroeconemic indicators and growth even though “policy” - defined broadly - appears to be importantly related to growth. Besides demonstrating the fragile relationship between many policy indicators and growth, this paper clarifies the conditions under which one finds evidence of convergence of per capita output levels, confirms the positive 2
**Table 3.** Aggregate statistics of natural hazards and exposure by country.

|||Brazil||Do|minican|Rep.||Ecuado|r|E|l Salvad|or||Hondura|s||Mexico|||Perú||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|<br>Mean|SD|
|_Hazards_||||||||||||||||||||||
|TH index at AD|M 2|||||||||||||||||||||
|Floods|27|2.66|0.7|134|3.10|1.1|219|2.07|1.2|211|1.74|1.1|225|1.97|1.2|879|1.98|1.2|181|<br>1.64|1.1|
|Landslides|27|1.53|0.4|135|2.90|0.8|222|3.27|1.0|225|3.23|0.8|227|3.48|0.7|883|2.45|1.2|182|<br>3.36|0.8|
|Earthquake|27|1.19|0.3|136|3.00|0.0|223|3.28|0.5|225|4.00|0.0|230|3.29|0.5|881|2.09|1.0|182|<br>3.23|0.5|
|Cyclones|5|1.00|0.0|22|4.00|0.0|82|1.00|0.0|2|4.00|0.0|28|4.00|0.0|903|3.94|0.3|4|1.00|0.0|
|Droughts|27|1.59|0.7|136|3.00|0.0|223|1.00|0.0|225|3.00|0.0|230|2.11|0.3|903|2.31|0.9|182|<br>1.41|0.7|
|EM-DAT: Num|ber of eve|nts (2000-2|021) at AD|M 1||||||||||||||||||
|Floods|23|6.87|6.36|32|6.75|2.51|24|5.08|3.49|14|5.50|1.70|16|6.69|2.63|<br>32|3.19|3.16|25|9.20|3.74|
|Landslides|23|0.39|0.94|32|0.00|0.00|24|0.29|0.55|14|0.07|0.27|16|0.00|0.00|<br>32|0.25|0.80|25|0.56|0.87|
|Earthquake|23|0.04|0.21|32|0.13|0.34|24|0.67|0.82|14|1.86|0.66|16|0.50|0.73|<br>32|0.78|1.39|25|1.60|1.53|
|Cyclones|23|0.09|0.29|32|11.66|1.77|24|0.00|0.00|14|5.64|1.28|16|4.00|2.22|<br>32|6.63|5.97|25|0.00|0.00|
|Droughts|23|1.57|1.53|32|0.00|0.00|24|0.29|0.46|14|2.07|1.07|16|3.38|1.78|<br>32|0.50|0.67|25|1.20|0.41|
|_Exposure at AD_|_M 2_|||||||||||||||||||||
|_Density_|27|75.7|119.9|135|350|1173|25|84|80|225|599|1290|230|124|135|902|746|2094|182|<br>68|296|
|Ln. density|27|3.4|1.5|135|4.8|1.2|25|3.9|1.3|225|5.6|1.1|230|4.4|0.9|902|4.9|1.9|182|<br>3.0|1.4|
|Area|27|314,318|376,072|135|325|302|223|1,131|1,884|<br>225|86|91|230|387|743|902|1,462|3,201|<br>182|<br>6,829|13,195|
|% cropland|27|5%|7%|135|6%|11%|223|3%|10%|225|5%|11%|230|2%|6%|902|18%|25%|182|<br>2%|4%|
|% urbanization|<br>27|1%|2%|135|3%|9%|223|2%|6%|225|2%|8%|230|0%|1%|902|4%|15%|182|<br>1%|4%|

Note: density in Brazil and Ecuador is at the state level. The statistics are computed before imputations. Source: authors' calculations based on ThinkHazard! EM-DAT, and SEDLAC database.

11