rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Wildlife<br>PIKE (elephant poaching) N = 578<br>Control mean = 0.437<br>log(bird abundance) N = 145,200<br>Control mean = 3.003<br>Tourism<br>1{iNaturalist tourism} N = 3,625<br>Control mean = 0.462<br>1{eBird tourism} N = 3,625<br>Control mean = 0.510<br>Development<br>Standardized asset wealth<br>N = 2,755<br>Control mean = 0.034<br>Conflict<br>1{Civilian targeting} N = 3,625<br>Control mean = 0.163<br>1{Battles} N = 3,625<br>Control mean = 0.107<br>1{Protests and riots} N = 3,625<br>Control mean = 0.170<br>-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5<br>Average treatment effect on the treated (ATT) of African Parks management<br><!-- End of picture text -->

**Fig. 3** : **Average effect of AP management on wildlife, tourism, economic development, and conflict.** Each row presents the result of a separate regression. The y-axis specifies the dependent variable in each regression. The points display the Average Treatment effect on the Treated (ATT), the average effect of AP management on a given dependent variable. The thick and thin bars represent the 90% and 95% confidence intervals, respectively. Standard errors are clustered at the protected area level for each regression. The text boxes display the number of observations and the mean of the dependent variable among control group protected areas. Table S3 presents these results in numeric format.

whether AP management enhances local economic well-being. Atlas AI uses daytime and nighttime optical imagery to predict asset wealth as measured in the Demographic and Health Surveys (DHS) Program [63]. After training a machine learning model on DHS data, Atlas AI predicts asset wealth for the continent of Africa. The data are produced at an annual frequency and delineated by second-level administrative divisions, spanning the years from 2003 to 2021. We filter the data to include only 9
has been $0.24 per kWh historically in Uganda, while the average effective tariff stood at $0.15 as of 2009. While the underpricing of power remains a macroeconomically significant issue in Uganda, the problem is even more pronounced in Uganda’s peer group of other LICs in Africa (figure 28). In the water sector, average tariffs charged by NWSC stand at $0.76 per m<sup>3</sup> versus an estimated benchmark tariff of $1.20 per m<sup>3</sup> for full recovery of both operating and capital costs. The consequent macroeconomic burden of undercharging for water services—at 0.1 percent of GDP—is significantly lower than that for power, and again comparatively lower than for the peer group.

**Figure 28. Underpricing of power and water in Uganda is comparatively less burdensome, 2001-2009**

Financial burden of underpricing, as percentage of GDP <!-- Start of picture text -->
1.5<br>1.0<br>0.5<br>0.0<br>Power Water<br>Uganda LIC-Non-Fragile<br>Percenatage of GDP<br><!-- End of picture text -->

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ GDP = gross domestic product; LIC = low-income country.

_Because of inequitable access to power and water services in Uganda, subsidized tariffs are highly regressive._ Close to 100 percent of those that have electricity or piped water connections belong to the top 20 percent of the expenditure distribution; such connections are non-existent for poorer households (figure 29). Only the richest quintile has access to piped water. Most of the poorest quintile still rely on untreated surface water. This distribution of connections virtually guarantees that any price subsidy to piped water services will be extremely regressive.

46
# Diversity Matters *

## **The Economic Geography of Industry Location in India**

_Somik V. Lall Development Research Group The World Bank, Washington, DC, USA slalll@worldbank.org_ _Jun Koo Development Research Group The World Bank, Washington, DC, USA kkyojun@worldbank.org_ _Sanjoy Chakravorty Department of Geography and Urban Studies Temple University, Philadelphia, PA, USA sanjoy@temple.edu_ JEL Classification: R120, R340, L600 This paper has been funded by a World Bank Research Grant 77960, "The Economic<sup>Geography and Political</sup> Economy of Industrial Location." We are grateful to the Central Statistical<sup>Organization (CSO),Governmentof</sup> India, for making the firm level data available for this study. We would like to thank Zmarak<sup>Shalizi,Anthony</sup> Venables, and participants of the 2003 UNU/WIDER Tokyo conference<sup>on "Spatial Inequalityin Asia" for useful</sup> comments and suggestions.
non-eligible households in the treatment localities relative to those in the control localities.

The measure of welfare used in our analysis is income per capita.<sup>16</sup> For households in treatment villages receiving PROGRESA cash transfers, total income per month was adjusted upwards by the cash transfer per month received by the household. The actual amount of cash transfers received per month was obtained from the records of payments sent out each month since May 1998 by the PROGRESA administration headquarters in Mexico City. The monthly income measure calculated for each round of the survey was then converted in November 1998 pesos by dividing by the corresponding adjustment ratio of the national consumer price index.

We calculate different poverty measures using two different poverty lines: the value of the basic food basket ( _canasta basica_ ) and the median of the per capita value of consumption in November 1998. The first poverty line (basic food basket) yields a poverty rate of 90.1% in the base year. The median of per capita consumption in November 1998 yields a poverty rate of 59% which is only slightly below the fraction of the population in the treatment villages that actually received the benefits of the program (see section 2 above).

The choice of a poverty line is a major concern when poverty measures are estimated. We try to deal with this issue by means of a test of stochastic dominance, up to order three, between the distribution of monthly income in treatment and control areas for each round. As far as the program impact on poverty is concerned, stochastic dominance of one distribution on the other can have important implications. For instance, if the distribution of income in treatment areas first-order stochastically dominates that in control areas, this implies that the fraction of poor households (income below the poverty line) will be always smaller in treatment areas than that in control’s no matter which poverty line is chosen. Other orders of stochastic

> 16 The absence of reliable information on household consumption prior to the start of the program precluded the use of consumption as a measure of the poverty impacts of the program. For more details on the consumption impacts of PROGRESA see Hoddinott and Skoufias (2004).

20
# II. Description of Sudan’s Rural Population

## Rural Population and Poverty

The majority of Sudan’s population is rural. There has been little change in the rural/urban composition of Sudan since 2009 (Figure 8a). Unlike most other developing countries in the development stage of their economic transition, the share of the rural population has increased, though only slightly, from 64 percent to 65 percent. While the poverty rates in urban areas have increased, the poverty rate in rural Sudan has decreased dramatically from 58 percent to 35 percent, and is now comparable to poverty rates in urban areas (Figure 8b). As the majority of Sudan’s population is still rural, almost twice as many poor people reside in rural areas compared to urban areas (Figure 8c). By focusing on the welfare of these households, Sudan can achieve further substantial reductions in national poverty.

Figure 8: Rural/Urban Composition of Sudan’s Population and Poverty, 2009 and 2014/15 <!-- Start of picture text --> (c) Proportion of Total Poor,<br>(a) Proportion, Rural/Urban (b) Poverty Rate, Rural/Urban<br>Rural/Urban<br>Urban Rural 2009 2014/14 Urban Rural<br>64% 65% 64%<br>80%<br>36% 35% 36%<br>20%<br>2009 2014/15 URBA N RURA L 2009 2014/15<br>58%<br>37%<br>35%<br>27%<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15 and NBHS 2009.

### **The increase in urban poverty and decrease in rural poverty might be explained by the relatively high**

**prices of staples (Figure 9a).** At the time NHBPS 2014/15 was conducted, both sorghum and millet prices had risen significantly, though they have since reduced. As the urban population is a net consumer of food and the rural population is a net producer of food, this increase in food prices may have had an adverse effect on urban households while having a beneficial effect on rural households. These price increases might have exacerbated the already prevailing negative effect on urban areas from the reduction in oil revenues. As noted in the introduction, increasing agricultural production can bring down food prices, thereby benefitting urban consumers. Food prices have been a contributing factor to the recent political instability, as prices have increased rapidly since 2018 (Figure 9b). Increasing Sudan’s food production is therefore as vital for meeting Sudan’s domestic consumption needs as it is as a source of foreign revenue.

10
households are significantly more likely to sell assets to cope, while urban households are more reliant on family and friends and reduction of non-food consumption.

While male and female-headed households use common coping strategies, female-headed households are significantly more reliant on family and friends’ assistance (Table 7, column 2, significant at 1 percent level). Even in urban areas, where the most widely used coping strategy for any type of shock is to draw on savings (49 percent), a smaller proportion of female-headed households relied on savings during the survey period, whereas a higher proportion relied on assistance from family and friends compared to male-headed households. In rural areas among male-headed households, the more common methods are to rely on savings (25 percent) and to decrease consumption (26 percent); however, the most widely adopted coping strategy by rural female-headed households is reliance on assistance from family and friends.

Table 7: Adopted coping strategies during COVID-19 in response to difference shocks

|Variables|(1)<br>Relied on<br>savings|(2)<br>Help from<br>family/friends|(3)<br>Reduced<br>consump.|(4)<br>Bought<br>cheaper<br>food|(5)<br>Sale of animal<br>livestock|
|---|---|---|---|---|---|
|Gender of household head||||||
|(1=female hh, 0=male hh)|-0.070|0.659***|-0.294*|-0.073|-0.096|
||(0.154)|(0.150)|(0.164)|(0.172)|(0.230)|
|Education level of household||||||
|head (1=primary)|-0.525***|-0.104|0.299*|0.124|0.598***|
||(0.148)|(0.160)|(0.156)|(0.185)|(0.202)|
|Education level of household||||||
|head (2=secondary)|-0.243*|-0.364**|0.154|-0.129|0.555***|
||(0.145)|(0.155)|(0.142)|(0.180)|(0.193)|
|Education level of household||||||
|head (3=post-secondary)|-0.373**|-0.324**|-0.020|0.086|0.738**|
||(0.168)|(0.145)|(0.221)|(0.312)|(0.334)|
|Sector (urban=1, rural=0)|0.742***|0.168*|-0.323***|-0.076|-1.027***|
||(0.090)|(0.093)|(0.095)|(0.118)|(0.188)|
|Household size|0.024|-0.030|0.015|-0.020|0.041**|
||(0.016)|(0.020)|(0.017)|(0.020)|(0.019)|
|Constant|-0.592***|-0.821***|-0.593***|-1.092***|-1.630***|
||(0.130)|(0.157)|(0.138)|(0.172)|(0.184)|
|Observations|1,578|1,578|1,578|1,578|1,578|

Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10). Each column reports results from a single probit regression of a binary variable equal to 1 if the respondent’s household adopted the coping strategy, 0 otherwise.

# 3.3. Other impact channels

There are several indirect transmission channels on the economic impact that we have not modeled in our CGE simulation or econometric regression due to complexity and unavailability of data, but it is worth discussing and could potentially have a long-lasting impact on an individual women’s life.

16
So far we have focused on consumption poverty, which has been our preferred approach to global poverty measurement in past work. However, given that discussions of poverty in China have typically focused on income poverty, we also present results for this measure in Table 2. The first column includes an update of the income poverty series we estimated in Ravallion and Chen (2007). The rest of the table corresponds to Table 1.

Table 3 gives our estimates of the number of poor, comparing the old 1993 international poverty line and the new one. We give results for both consumption and income. Comparing 2005 with 1981, there were 635 million fewer people with household consumption per person below our new 2005 poverty line, as compared to 566 million fewer poor using the old 1993 line. Comparing the old and new “$1 a day” international poverty lines, we find that an extra 133 million people in China live in poverty in 2005. Using income instead, the difference drops to 64 million.

# **Conclusions**

A careful scrutiny of the new PPP for China does not suggest that its implications for the extent of poverty in that country (by international standards) are anywhere near as dramatic as some casual observers have suggested. On _a priori_ grounds, it was plain that the 300 million count for the increase in the number of China’s poor was a gross exaggeration because it ignored the (documented) fact that the 2005 ICP price survey is not representative of the cost-of-living in rural China, where prices (particularly for the goods such as food for which the poor have a high budget share) are appreciably lower than in urban areas. Instead of an extra 300 million people deemed to be poor by the standards of what “poverty” means in low-income countries, our calculations suggest the figure is closer to 130 million for consumption poverty and about half that figure for income poverty.

Of course, there can be no denying that this is a large upward adjustment in our assessment of China’s poverty. Given that China had never agreed to participate in the International Comparison Program prior to 2005, it is possibly not too surprising that the prior estimates of China’s PPP rate from non-ICP sources were so far off the mark. This reaffirms the importance of global participation in the ICP.

However, even if we had not done any of the calculations reported in this paper, it should have been obvious enough that the new PPP rate alone cannot entail the sort of 11
# **Table 12: Receiving SMS vs. Reporting Wearing Masks**

|||Wear M|asks=1; Do|not=0||||Wear M|asks=1; D|o not=0||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)||(6)|(7)|(8)|(9)|(10)|
|SMS primary||||||Gov. SMS past||||||
|source|0.013***|0.012**|0.007|0.026***|0.026***|week|0.001|0.002|-0.007|0.028|0.028|
||(0.005)|(0.005)|(0.005)|(0.006)|(0.006)||(0.015)|(0.015)|(0.015)|(0.021)|(0.021)|
|Urban||0.017***|0.017***|||Urban||0.018***|0.017***|||
|||(0.005)|(0.005)|||||(0.005)|(0.005)|||
|Ln(income pc)||0.002*|0.002**||-0.001|Ln(income pc)||0.002*|0.002**||-0.001|
|||(0.001)|(0.001)||(0.001)|||(0.001)|(0.001)||(0.001)|
|Constant|0.952***|0.942***|0.862***|0.948***|0.951***|Constant|0.954***|0.944***|0.863***|0.953***|0.956***|
||(0.002)|(0.004)|(0.023)|(0.001)|(0.004)||(0.002)|(0.004)|(0.023)|(0.000)|(0.003)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.001|0.002|0.021|0.002|0.002|R-squared|0.000|0.002|0.021|0.000|0.000|
|Unique individuals|2,153|2,153|2,153|2,153|2,153|Unique individuals|2,153|2,153|2,153|2,153|2,153|
|Robust standard err|ors in parent|heses; *** p|<0.01, ** p<|0.05, * p<0|.1|||||||

18
the other hand, many non-food items are either imported or produced in Abidjan and are thus more likely to be more expensive in rural areas. An index relying only on food prices would therefore overestimate price lifferences and hence underestimate regional welfare differences.

With the new index, N, tjie figures show that prices in other urban areas are on the average about 8 percent below those in Abidjan. In rural areas, price differences are greater - with the biggest difference being recorded for the Savannah about 25 percent below Abidjan. The figures of the N index thus confirm, by and large, our suspicion that the G index would tend to underestimate urban-rural price differences, while the M index would tend to overestimate them. In any event, the comprehensive commodity coverage of the N index give us greater confidence in its validity.

But does all this make a difference? A major use to which the regional price index has been put is in the calculation of real household expenditures and, thence, in the calculation of poverty. The next section investigates alternative estimates of poverty with the N, G and M regional prices indices.

# 4. **Implications for Poverty Estimation**

This is not the appropriate place to go into the details of how poverty is measured at a conceptual level, or the problems with operationalizing measures of the "standard of living" from household income and expenditure surveys (see Kanbur, 1987). Suffice it to say that a common procedure for measuring the standard of living for an individual is to take the real per capita expenditure (with suitable imputation for items such as home produced consumption) of the household in which he or she lives. When a single snapshot is being considered, the term "real" refers to adjustments of nominal expenditure by a regional price index. There is also the vexed question of what poverty line to choose. All these questions are discussed further in the literature and, in the context of Cote d'Ivoire, in Grootaert (1992). The poverty line chosen there is 128,600 CFAF per person per annum in 1985 6
**Table 4:  Radio’s Impact on Citizen Support for Jobs versus Public Health**

||No Radio<br>|X Children<br>|interaction<br>|Radio<br>|X Children<br>|interaction<br>|
|---|---|---|---|---|---|---|
||Bivariate|Radio<br>controls|All controls|Bivariate|Radio<br>controls|All controls|
|Number of stations:|||||||
|Public||-0.0489<br>|-0.051<br>||-0.051<br>|-0.052<br>|
|||(0.20)|(0.19)||(0.18)|(0.19)|
|Private commercial||0.0165|0.034||0.017|0.033|
|||(0.45)|(0.09)||(0.44)|(0.09)|
|Religious||0.0582|0.050||0.0570|0.049|
|||(0.11)|(0.19)||(0.12)|(0.20)|
|**Private non-**|**-0.0288**|**-0.0326**|**-0.0276**|-0.007|-0.011|-0.006|
|**commercial**|**(0.02)**|**(0.01)**|**(0.04)**|(0.63)|(0.44)|(0.71)|
|**(community)**|||||||
|**Private non-**||||**-0.008**|**-0.008**|**-0.008**|
|**commercial X**||||**(0.01)**|**(0.01)**|**(0.01)**|
|**number of children**|||||||
|Number of children in|||0.007|.015|.015|.026|
|household|||(0.19)|(.08)|(.07)|(0.004)|
|Number of children|||-0.024|||-0.023|
|aged 0 – 5|||(0.02)|||(0.02)|
|Respondent has|||0.049|||0.49|
|secondary education|||(0.10)|||(0.10)|
|Respondent Yoruba|||.098|||.094|
||||(0.01)|||(.01)|
|Number of adults|||-0.039|||-0.041|
|older than 60|||(0.09)|||(0.08)|
|Respondent Catholic|||-.078|||-.076|
||||(.02)|||(.02)|
|Household income|||0.144|||0.144|
|below 30,000 CFA|||(0.00)|||(0.00)|
|Dwelling character|istics||||||
|Wall - Brick/Semi-|||-0.119|||-0.12|
|rigid|||(0.00)|||(0.00)|
|Floor – Cement|||0.068|||0.069|
||||(0.00)|||(0.00)|
|Rooms - Five or|||-0.027|||-0.0270|
|more|||(0.29)|||(0.29)|

22
payment behavior on a daily basis. Notably, men reacted more quickly to the tax letters, with the treatment effect appearing earlier and stabilizing after the due date. In contrast, treated women continued to make overdue payments in the days following, rapidly closing the initial gender gap in compliance. Second, the impact of the personalized tax letters extended beyond the targeted October 2020 bill, but the response is similar for men and women. Despite receiving the letters in early October, the intervention prompted some treated taxpayers to clear past-due debt from previous months and also pay their November and December bills. Third, while women present homogeneous responses across quintiles of property valuation, men exhibit a slightly negative gradient. Notably, in the lowest quintile, the personalized tax letter boosted timely payments of men by 8 percentage points—twice the increase observed for women.

Lastly, the inclusion of the recent pandemic in our analysis period allows us to examine gender-specific responses to a macroeconomic shock.<sup>3</sup> Although payment rates dropped significantly—by approximately 25 percentage points—at the onset of the COVID-19 pandemic, we observe no noticeable gender-based differences in response. This indicates that macroeconomic shocks seem to affect property tax delinquency behavior similarly for both men and women.

Overall, our findings suggest that gender-targeted strategies may have limited potential for improving tax compliance in the context of property taxes, as men and women exhibit similar baseline payment rates and respond in comparable ways to personalized tax letters.<sup>4</sup> Notwithstanding, the pronounced gender disparities in higher-value property ownership documented in our analysis raise important questions about wealth inequality and underscore the need for more research into the underlying causes and broader economic implications (e.g., differences in credit access).

We contribute to several strands of the literature studying asset ownership and the link between wealth taxation (including property taxes) and gender. Many countries recently highlighted the need to improve the collection of gender-disaggregated data on taxation in general, and on men’s and women’s property and capital ownership in particular, which is less commonly available (OECD, 2022). We add to a growing literature documenting gender patterns in property ownership and wealth (Holden and Tilahun, 2020; Kotikula and Raza, 2021; Komatsu et al., 2021; Gaddis, Lahoti, and Swaminathan, 2022; Scot et al., 2023), corroborating previous findings that women own fewer properties than men, particularly as property values increase. We document a substantial wealth gap, with women’s ownership shares declining significantly for higher-valued properties.

> 3. For recent work on gender and business cycles, see Gomes (2024).

> 4. However, we acknowledge that our study does not evaluate the effectiveness of gender-specific interventions implemented elsewhere, such as Awasthi, Pyle, Aggarwal, and Rakhimova (2023).

3