rafmacalaba/datause-encoder-data huggingface dataset:
This version: 15 Feb 2024 26 _After struggling and overcoming the poverty stage, they have come to the middleincome stage. There are still poor Indians. But there are also those who took a lot of effort to study and become upper middle-income._ [Indian, Selangor] Newer job opportunities have transformed the life of rural Malaysians as well.

_Physically-wise and opportunity-wise they had it tougher than us... you can’t deny. After all, we were a very poor nation 40 years ago...now you hear of villagers becoming doctors and lawyers..._ [Multiple respondents, Urban Sabah]

**Improvements in infrastructure**

There has been significant improvement in infrastructure development, particularly in road connectivity, exemplified by the experiences of the narrator below.

_My nephew died 25 years ago. We took him to the hospital through jungle roads. It took too long. The roads are better now. There’s development._ [Indian, Selangor] Recent improvements in rural roads and bridges have enhanced living standards by making interior villages more market-accessible.

_To come to this village from town might have taken you...3 or 4 hours. We had to walk everywhere. But by 2014, we received new roads. And that has really changed our living standards. It really helped in getting our produce out to sell._ [Rural Sabah] Respondents mentioned the availability of different modes of transportation and it is now common for almost everyone to own cars.

_You didn't see any cars here...if you had a motorcycle, you were really considered quite_ _well off, what more a car. The difference is quite stark._ [Rural Sabah] 26
Our strategy implicitly assumes that the young adult men in our sample were born in the same district in which they lived at the time of our data. This is a reasonable assumption, because permanent migration for adult _males_ in India is relatively uncommon (Rosenzweig and Stark, 1989); in contrast, women often migrate at the time of marriage to join their husbands’ households.<sup>5</sup> Appendix A uses various numerical strategies to demonstrate that our result would not be likely to be importantly different if we knew the actual birth district of the adults in our sample, rather than the district in which they now live. In particular, excluding the small fraction of men who have ever moved residences does not meaningfully change our coefficient estimates.

# **2.2 Variables and Data Sources**

We match data from two different sources. For our dependent variables and control variables, we use data on individual adult males from the India Human Development Survey (IHDS), a nationally representative 2005 cross-sectional survey of 40,000 households (Desai _et al_ , 2007). Our primary dependent variable is the log of hourly wages in rupees, as computed by the IHDS.<sup>6</sup> As an input to our welfare computations, we also compute the effect of the early-life disease environment on household consumption per capita.

For our independent variables, we use two measures of the disease environment, both standard in the existing literature. First, we present results using infant mortality rates (IMR): the number of deaths in the first year of life per 1,000 live births. Economic historians have long used infant mortality as a measure of the disease environment. Early-life infant mortality rates are increasingly well understood to be an important determinant of height in developing countries today (Bozzoli, Deaton, and Quintana-Domeque, 2009), and historically in now-rich European countries (Hatton, 2014).<sup>7</sup> Second, we use district-level sanitation rates, operationalized as the percent of households in a district who use a toilet or latrine, rather than defecating in the open. Early-life exposure to open defecation in India has

> 5Even so, Cutler _et al_ (2010) also document that, in the 1991 Census, only 7.5% of all rural residents live outside their district of birth.

> 6In cases where a respondent is not paid an hourly wage, the IHDS computes one from detailed questions about earnings at all jobs and hours worked.

> 7In a similar empirical strategy, Almond, Currie, and Herrmann (2012) have recently used state-by-year variation in infant mortality rates in U.S. states to identify effects of the early-life disease environment on adult health among future mothers.

7
- Samson, D., Wirth, A. and Rickard, J., 1980. The Value of Information from Multiple Sources of Uncertainty in Decision- Analysis. _European Journal of Operations Research_ , 29, 254-260.\

- Siikamäki, J., Krupnick, A., Strand, J. and Vincent, J. 2015. Willingness to Pay for Amazon Rainforest Protection. Unpublished, Resources for the Future/World Bank.

- Strand, J., 2007. Public-Good Valuation and Intra-Family Allocation. _Environmental and Resource Economics_ , 38, 527-543.

- Strand, J., 2013. Strategic Climate Policy with Offsets and Incomplete Abatement: Carbon Taxes Versus Cap-and-Trade. _Journal of Environmental Economics and Management_ , 66, 202-218.

Strand, J., Carson, R. T., Navrud, S., Ortiz-Bobea, A. and Vincent, J., 2014. A “Delphi Exercise” as a Tool in Amazon Rainforest Valuation. World Bank Policy Research Working Paper WPS7143. Washington DC: The World Bank.

Tanner, M. and Wong, W. H., 1987. The Calculation of Posterior Distributions by Data Augmentation. _Journal of the American Statistical Association_ , 82, 528-540.

- World Bank, 2014. _Economic Valuation of Changes in Amazon Forest Area_ . _Project Document Covering Six Project Elements_ . Development Research Group, World Bank, April 2014.

- Yohe, G., 1996, Exercises in Hedging Against Extreme Consequences of Global Change and the Expected Value of Information. _Global Environmental Change_ , 6, 87-101.

- Yokota, F. and Thompson, K. M., 2004. Value of Information Analysis in Environmental Health Risk Management Decisions: Past, Present, and Future. _Risk Analysis_ , 24 no 3, 635-650.

# **Appendix A: Derivation of the distribution function for B-V**

To derive the distribution for _B_ – _V_ = Z, call the cdf of the random variable B, _F_ ( _B_ ), and denote its pdf by _f_ ( _B_ ). Considering the cdf for realized values of Z (thus for cases where the forest is saved), conditional on H, this is found as the convolution Substituting B = Z+V in (11) and taking its derivative with respect to Z, we get the conditional pdf for Z for given H, as follows: Figure 1 shows a simulation of the pdf given that B and V are both independent and normal, and given EB = 1 = H, EV = 0.5, σB = 1, σV =1.

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CONSUMPTION SUB-AGGREGATES 15 The goods included in the consumption aggregates follow the guidelines for consumption aggregates found in Deaton and Zaidi (2002).<sup>6</sup> In all three countries, FGT measures rely on consumption data collected by recall questions with probing for each consumption good. The number of consumption goods probed for in the questionnaire varies across countries, with 112 in Tanzania, 126 in Uganda, and 284 in Rwanda.<sup>7</sup> Beegle et al. (2012) review recall-based consumption surveys and report ranges from 37 to 305 goods, with a mean of 137 goods in total. Thus, the sample of countries seems to represent typical recall consumption surveys, even if the total number of goods doesn’t actually encompass all non-durable consumption. In each country, consumption goods are valued in year one’s prices corrected by the same price adjustment as used to evaluate the trend in poverty in the surveys. In each year values are spatially and temporally deflated.

The three countries are chosen to display variation in level and trend in poverty headcount. Headcount poverty stood at 15 percent in Tanzania in year one, compared to 57 percent in Rwanda, and poverty fell substantially in Uganda and Rwanda, while it increased in Tanzania (Development Research Group of the World Bank, Downloaded 2017). The five year time span between surveys in Rwanda can also be seen as an upper bound for the approach, as most countries implement full consumption surveys every five years or less.

4.2. **Looking for Linearity.** Moving from theory to practice, we would like to gauge the consumption patterns of households, as some goods might be more suitable than others. Figure 2 illustrates how a sum of linear goods will always lead to a linear aggregate, but Figure 5 also shows that a sum of expenditures on non-linear goods can be linear for a particular set of prices. To gauge properties of goods in relation to the total aggregate, we run an OLS regression of householdlevel log consumption expenditures for each good on a constant and the log total aggregate: 6. The included goods in the aggregates are very similar to those defined by the national statstical agencies, but are not necessarily identical.

7. In Tanzania and Uganda, the questionnaire is designed so that each row captures consumption of own production, gifts and purchased, summed into total consumption of items. In Rwanda, own consumption, and gifts and purchased items are split into two separate questionnaires. Hence, in the data it appears as if Rwanda has twice as many items (2× 284). For the analysis we have combined the values of each item from both questionnaires into one value, following the same principle as in Tanzania and Uganda.
75 TABLE 16: Proportion of Cattle Herd used as Work Anknals

||(1)|(2)|(3)|
|---|---|---|---|
||All cattle<br>('000 head)|Work anknals<br>('000 head)|(2)/(1)<br>(X)|
|ALL FARMS||||
|Private||||
|1970|13,003|549|4.2|
|1981|14,067|287|2.0|
|Ejldo||||
|1970|4,735|562|11.9|
|1981|7,613|406|5.3|
|FARMS UP TO<br>5 HECTARES||||
|Private||||
|1970|2,633|258|9.8|
|1981|<br>855|82|9.6|
|Ejido||||
|1970|na|na|na|
|1981|2,378|222|9.3|

SOURCE: Agricultural Census, 1970 & 1981.
The General Statistics Office of Vietnam recently published the 2009 Population and Housing Census and the 2010 Vietnam Household Living Standard Survey. These datasets permit a new updating of the Vietnam poverty and inequality maps. This paper presents new estimates of expenditure poverty at the province and district level of Vietnam. The estimates are based on the 15-percent sample of the 2009 Population and Housing Census in combination with the 2010 Vietnam Household Living Standard Survey. In addition, we estimate poverty at the provincial and district level for different groups including rural and urban, Kinh/Hoa and ethnic minority people. We also report small area estimates of inequality and examine in which provinces and districts the richest 15% of Vietnam are to be found. Finally, we compare the 1999 and 2009 poverty maps and analyze how evidence on local level changes can inform social policy making in Vietnam.

We simulate the impact of spatial targeting of a hypothetical budget, directed in turn to localities defined in terms of increasing spatial disaggregation to assess the gains from geographic targeting. The departure point of this analysis is a paper by Ravallion (1993) showing how spatial disaggregation to the regional level in Indonesia improves targeting, albeit to only a modest extent. In contrast, Elbers, Fujii, Lanjouw, Özler, and Yin (2007) apply an approach identical to the one employed here and find that in the case of Ecuador, Madagascar and Cambodia, when the geographic disaggregation is to a considerably finer level the gains from spatial targeting become very pronounced.

Vietnam has previously employed geographic targeting based on a fairly arbitrary list of poor communes, and is currently using a bottom-up approach to define who is poor and eligible for social assistance. Doubts about the effectiveness and transparency of both methods have been raised (Litvack, 1999; Baulch and Minot, 2002; van de Walle, 2002, Nguyen et al., 2010). Although poverty reduction in Vietnam has been impressive over the last decade, geographic targeting based on more clearly objective criteria may be needed to maintain further progress. Our simulations for 1999 and 2009, involving two snap-shots of the spatial distribution of poverty and a variety of alternative administrative levels of targeting, allow us to track the effectiveness of geographic targeting at different levels of spatial disaggregation as well as under alternative settings of the spatial distribution of poverty. At 4
and significant spillovers effects in the third ring at 2-3km. In the second ring at 1-2km the two forces seem to offset each other. And by ring 4 beyond 3km any effects have dissipated. These results are similar to those for advertising in Manhattan in Arzaghi and Henderson (2008), except the succession as we move away from the own firm of competition, spillover, and dissipation occurs at much shorter distances than in Kamapala.

# **7 Conclusions**

This paper evaluates the use of different measures of economic density in assessing urban agglomeration effects. These density measures are based on Landscan data. While Landscan measures ambient population at the grid square level with error, a ground-truthing exercise for Nairobi and Kampala suggests that Landscan does a better job of capturing within city clustering than other measures derived from smearing population into grid squares based on ground cover such as Landsat type data. The empirical work focuses on Africa, where there is a perception that African cities have low economic density due to a lack of clustering of economic activity. We find that Africa as a region has economic density and cluster measures similar to other developing countries in Asia and North Africa and higher than those found in the developed world or Latin America.

To assess economic density measures, we examine how well they explain household income differences across cities and neighborhoods. We have simple scale and density measures and more nuanced ones which capture in second moments the extent of clustering within cities. Noting that the extent of clustering is measured with error in Landscan resulting in attenuation bias, the evidence we have suggests that a simple density measure explains income differences across cities as well as or even better than more nuanced measures attempting to capture within city differences in the extent and nature of clustering. However, simple city scale measures such as total population are inferior to density measures and to some degree misleading. On the big picture side, there are very large household income premiums from being in bigger and particularly denser cities over rural areas in Africa, indicating migration pull forces remain very strong. Second, the marginal effects of increases in density on household income are very large, with density elasticities close of 0.6.

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that consumption expenditures fall with isolation. This means that geographically isolated households are surrounded on average by households with a low consumption level — and can thus be expected to have a lower reference point. This should raise the subjective welfare of isolated households — and hence cannot account for the negative relationship between isolation and subjective consumption adequacy. From this we conclude that habituation and reference point considerations are very unlikely to account for our fi ndings: if anything, they should bias the coe ffi cient of dk upwards, i.e., towards zero.

# 7. Conclusion

Using 1995/96 household survey data from Nepal, we have estimated the relationship between geographical isolation and subjective welfare. This estimation rests on the assumption that responses to questions about income and consumption adequacy capture utility rankings. Nepal is a perfect country to study isolation because road construction is recent and much of the country remained inaccessible by road at the time of the survey.

We fi nd the relationship between isolation and subjective welfare is signi fi cant and large in magnitude. Comparing the welfare cost of isolation across categories of consumption goods indicates that respondents factor the utility gain from product variety in their reported adequacy of consumption. Geographical isolation is associated with lower subjective consumption adequacy also for schooling and health care. In fact, for health care, total expenditures are not a signi fi cant determinant of access to health care, but isolation is. These fi ndings suggest that welfare assessments based on geographical poverty maps (e.g. Ferreira, Lanjouw & Neri 2003, Alderman, Babita, Demombynes, Makhatha & Ozler 2002, Mistiaen, Ozler, Raza fi manantena & Raza fi ndravonona 2002) may underestimate the subjective welfare cost of isolation since these maps typically focus solely on monetary income and consumption.

33
response to the illness of others in their household, we acknowledge that other explanations are possible. For instance, perhaps wage workers fill in for each other in order to make sure timesensitive work gets done. Even if one of these alternate mechanisms is behind the labor supply result for wage workers, our results nonetheless provide new insight linking illness to labor supply in low-income countries.

# **4. Household-level outcomes**

We now examine the overall effects of a member’s illness-related absence on household level income and labor supply, including both the direct effects of the missed work and the compensatory responses of other household members documented in the previous two subsections. We also look separately at households for whom these compensatory responses are likely to be strongest – those with two or more earners at baseline<sup>22</sup> and those with at least one male worker – and households in which there are no male earners or a female is the highest earner, given the minimal net response by females shown in table 2.

In each case, we conduct a back-of-the-envelope exercise that estimates the fraction of the expected income loss of the sick individual covered by the household income inclusive of compensatory behavior. Specifically, we compute the expected income loss of a sick individual in a specific class of households by taking the average usual reported income from the baseline survey of individuals in those households who are unexpectedly sick over the course of the survey. We then compute the fraction of the expected loss covered by the coefficient estimate

> 22 These are also the households providing the majority of the identifying variation in the individual-level results, since a worker in the regression would need to have another family member who was planning to work in order to have identifying variation in the _Other Worker Ill_ variable.  While we estimate equation 1 on the whole sample of workers employed at baseline (workers who are the only worker in their family over the entire survey help to identify the week fixed effects), results are almost identical if we use only the sample of workers in households with two or more earners at baseline.

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Policy Research Working Paper 9516

# **Abstract**

This paper presents a novel method for estimating the likely welfare effects of competition reforms for both current and new consumers. Using household budget survey data from 2015/16 for Ethiopia and assuming a reform scenario that dilutes the market share of the telecommunications stateowned monopoly to 45 percent, the model predicts a 25.3 percent reduction in the price of mobile services and an increase of 4.6 million new users of mobile phone services. This reform is expected to generate a welfare gain of 1.37 percent among all consumers. Poverty rates are expected to decline by 0.31 percentage point, driven by a reduction of 0.22 percentage point for current consumers and 0.09 percentage point among new users. Inequality would increase by 0.23 Gini point since better-off consumers are more likely to reap the benefits of greater competition. This method represents a powerful tool for supporting the analysis of competition reforms in developing countries, particularly in sectors known for excluding significant segments of the population due to high consumer prices.

This paper is a product of the Poverty and Equity Global Practice. 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 crodriguezc@worldbank.org and emalasquez@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
**Table 4: Truncated Gauss-Hermite parameters for each temperature target.**

|_T _<sup>_∗_</sup>|Υ|_κ_|
|---|---|---|
|1.5|1.8809|1.04536|
|1.7|2.3254|1.04534|
|2|2.3262|1.04505|

and the 3<sup>rd</sup> percentile for the 1.5<sup>_◦_</sup> C target. We give the numerical values for Υ and _κ_ in each case in Table 4.

## **4.2 Solving the model**

We solve our model using CVXPY, a domain-specific language written in Python and Julia for convex optimization problems (Diamond and Boyd, 2016). CVXPY uses disciplined geometric programming to interpret model equations and constraints and cast them into generic forms that can be used by open-source solvers (Agrawal et al., 2019). We use the GUROBI solver (Gurobi Optimization, LLC, 2023), which can be accessed for free by academics and researchers, that solves the base model in under 10<sup>_−_2</sup> seconds and models with uncertainty in the range of 10<sup>_−_1</sup> seconds, depending on how many quadrature nodes we use or samples we draw from the RCB distribution. Using this software allows us to circumvent solving the Bellman equation (Bellman, 1957) directly using techniques such as value function iteration (Judd, 1998), which can be computationally challenging (Cai, 2019), especially with high-dimensional state spaces and constraints on each state variable as is present in our model setup. Note that we use Gauss-Hermite quadrature methods to evaluate the expectation operator in our objective functions, and use 8 approximation nodes throughout (Cai, 2019); only in generating Figure 5 do we take a large number of samples (500) from the RCB distribution to solve the model for illustrative purposes. The results of this study were checked to be the same using both methods.

# **References**

- A. Agrawal, S. Diamond, and S. Boyd. Disciplined geometric programming. _Optimization Letters_ , 13(5):961–976, July 2019. ISSN 1862-4472, 1862-4480. doi: 10.1007/s11590-019-01422-z. URL `http://link.springer.com/10.1007/s11590-019-01422-z` .

- R. E. Bellman. _Dynamic Programming_ . Princeton University Press, 1957. ISBN 9781400835386. doi: 10.1515/9781400835386. URL `https://www.degruyter.com/document/doi/10.1515/ 9781400835386/html` .

- Y. Cai. Computational Methods in Environmental and Resource Economics. _Annual Review of Resource Economics_ , 11(1):59–82, Oct. 2019. ISSN 1941-1340, 1941-1359. doi: 10.1146/annurev-resource-100518-093841. URL `https://www.annualreviews.org/doi/10.1146/ annurev-resource-100518-093841` .

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