rafmacalaba/datause-encoder-data huggingface dataset:
expansion of this city is apparent in Figure 5 it is part of an NSS region (Southern Kerala) that has three big cities (Kochi and Thiruvananthapuram are the other two) and so provides one of the few examples of big city multi-polar growth.

The approach to splitting big city growth from smaller town growth in Figure 5 raises a question of whether the dimmer fringe of a city should be considered as part of that city or as a separate, smaller, town. Such areas could be existing, distinct, towns that get engulfed by the expanding big city or they may be new parts of the city built on virgin land that are yet to be as brightly lit as the center of the city. To check if our method of putting dim areas on the edge of the city into the secondary towns component of total urban growth affects the results, we created another data set by using a ‘fixed mask’ approach. (This terminology is because our algorithm masks the area covered by the big cities when it calculates statistics on urban area and average DN values for the secondary towns within an NSS region.) Specifically, we take the area covered by each city when it is at its maximum extent, which is in 2012, and exclude that area when smaller town area and the average DN values in smaller towns are calculated for any of the satellite-years. In other words, land that eventually gets engulfed in the city is treated as never being part of the area that small towns could occupy and is reserved as only available for the city even if the city has not yet grown into that area at the time of a particular satellite-year observation. In contrast, our main results use what we call a ‘variable mask’ where the boundary between big cities and smaller towns changes satelliteyear by satellite-year.

# **IV. The Econometric Model**

Spatial autocorrelation in levels of, and changes in, poverty was noted in Section 3.1, and our econometric modeling recognizes this by using the spatial panel estimator of Belotti et al (2014). The regional _N_ -vector of poverty measures for date _t_ (=1,..., _T_ ) is denoted<sup>_P_</sup> _t_<sup>and</sup> the matrix of explanatory variables is _X t_ which includes our measures of urban economic activity as indicated by the night lights data. Our spatial Durbin model (SDM) can be written as:<sup>20</sup>

> 20 For an excellent overview of the spatial Durbin model see Elhorst (2012). Note that the model is static. A temporal lag of _Pt_ can also be included. We found that such a temporal lag of the NSS region poverty rate was statistically insignificant so we dropped it from the model. This allows us to keep the first year of our data.

13
18 Table 2. Actual Costs and Those Covered<sup>by Optimal Pricing Model: Tunisia 1982</sup> (Dollar Million at 1982 Prices)

||_tens_|_AcAl_<br>_Costs_<br>_Incurred_<br>_(I)_|_RevsGenerated_<br>_by Opdmal_<br>_PricingModel_<br>/2)|
|---|---|---|---|
|Recur|rent Expenditures:|||
|(i)<br>|Administration<br>|2.44<br>||
|(ii)<br>|Routine Maintenance<br>|22.48||
|(iii)<br>|Shortfall of Routine<br>Maintenance (a)<br>|'.62||
|(iv)|Resurfacing:|||
||<br>fixed (b)<br>|5.17<br>||
||variable|20.68|20.68|
|(v)|Sub-Total|56.39|20.68|
|CaDit|al Exoenditures: (c)|||
|(vi)<br>|Extension/Improvement<br>|9.80<br>||
|(vii)<br>|Expansion of Capacity<br>|19.60||
|(viii)|Annualized Construction Cost (d)<br>|||
||non-urban roads<br>||13.68<br>|
||urban roads<br>||20.00|
|(ix)|Fixed Resurfacing Cost (e)|||
||non-urban roads<br>||0.20<br>|
||urban roads||0.30|
|Finan|cin2 Char2es:**(f)**|||
|(x)|Debt Service/Repayment|5.83|_|
||Sub-Total|35.23|34.18|
||Grand Total|91.62|54.86|
||Unfunded Road Expenditure|-|36.76|

Notes:

- (a) Since road conditions were generally deteriorating,<sup>it is estimated that allocations for</sup> routine maintenance were about 20 percent lower than needed<sup>to maintain the road network</sup> in a svaia iong-tenn condiiion.

- (b) The. reesent weather related resurfacing costs which<sup>are unrelated to traffic. They</sup> comprise about 20 percent of total resurfacing costs in<sup>"dry non-freeze" countries like</sup> Tunisia.

- (c) Assuming onethird of capital expenditures are new construction<sup>and the remainder are to</sup> expand capacity. Real capital expenditure between 1976<sup>and 1982 was fairly stable and</sup> fluctuated within about 20 percent of the mean expenditure<sup>for the period.</sup>

- (d) See Annex 1 for derivation of these figures.

- (e) The additional fixed resurfacing costs associated with<sup>expanding capacity. They are</sup> calculated as 1.5 percent of initial costs.

- (f) This it refers to World Bank loans only. It appears elsewhero in<sup>the government's</sup> accoun. i and is usually omitted from financial statements<sup>of road revenues and</sup> expendiLures.

Source. World Bank, 1987 (b); Newbery et al, Table<sup>21, 1988.</sup>
# **_Extent of domestic price changes, 2006–08_**

The changes between 2006 and 2008 in domestic prices of each of these four key food items in each of the thirty sample countries for which we have information on both income sources and expenditure patterns are shown in Table 2, together with changes in nominal non-food prices as measured by the non-food component of the consumer price index for each country.<sup>2</sup> Clearly domestic prices of these key foods generally rose considerably relative to non-food prices during this period.

# **_Impacts of changes in price distortions on international prices_**

To obtain an indication of the impact of the observed changes in trade restrictions on domestic prices, we need first to estimate their impact on international prices. That involves taking account of the changes in price distortions in countries that collectively account for a large share of world consumption. Following Martin and Anderson (2012), to assess the implications of price insulation on a homogenous product’s international price, p<sup>∗</sup> , we begin with the global market equilibrium condition: where Si is the supply in region i; pi is the region’s producer price; vi is a random production shift variable for that region; Di is demand in region i (assumed to be not subject to shocks from year to year); and pi is the consumer price in region i. We assume that pi = �1 + tp�p<sup>∗</sup> where tp is the rate of distortion between the producer price and international price, and that pi =

> 2 Computed by using sample-average weights for food expenditures to remove the observed influence of the food CPI from the overall CPI numbers obtained from FAO.

6
5 <!-- Start of picture text --> Table 1<br>Health Care Use by Children 0-35 Months<br>Curative Preventive<br>Care Care<br>A Reported %-. Ill, and % Ill and I Ill, and d Used EW-lic-<br>Ill, Last 4 Used Health Used Public Used Public in Last<br>Weeks Care Health Care Primary Care 6 Months<br>,.Total 28 16 9 6 19<br>lOuintile<br>-<br>|<Poo'r*st < . 26 16 9 6 16<br>2 29 11 8 6 22<br>l 3 29 18 13 9 21<br>< < e - '28- 18 .91 X . 9> -<br>Richest 26 14 4 2 12<br>Nutritional Status*<br>Malnourished 38 20l a 7 21<br>.NXormal 27 -15S 9 : 6- 17<br>l *>Mlnourished children are those who have a weight for age of "ss than<br>l 80% of the standard used by the National Center for Health Statistics,<br>i.e., are moderately or severely malnourished by WHO standards.<br><!-- End of picture text -->

Source: Survey of Living Conditions, Round III.

17. Prior to the establishment of the MCSR, reporting was infrequent and much less systematic. In 1977, 1978 and 1979, for example, insufficient reports were recoived to estimate a reliable nationwide malnutrition rate. The 1978 health and nusrition survey results are therefore compared with the clinic data from 1976. In that year reliable clinic data were available from only seven of Jamaica's fourteen parishes. The comparison is thus only for the seven parishes.

18. The factors which affect the selection of children into the MCRS system provide biases in different directions. The predominance of preventive care in the observations should lead to an underestimate of malnutrition as these are the children with the best vaccination rates, most health education of their mothers, presumably good access to medical care, etc. The inclusion of some home health visits may counteract this bias to some extent. The exclusion of children who receive their care from private physicians excludes
panel A) is similar to the ...scal multiplier associated with decreases in government spending (Figure 4, panel A). Both multipliers are not signi...cantly di¤erent from zero. By the same token, the pro...le obtained for the ...scal multiplier in recessions (Figure 2, panel B) is similar to the ...scal multipliers associated with increases in government spending (Figure 4, panel B). Both ...scal multipliers have initial values that are less than 1 (but signi...cantly di¤erent from zero) and long-run values that are larger than 1.

A somewhat di¤erent pictures emerges for developing countries. The multiplier when government spending goes up (Figure 5, panel B) is barely signi...cant on impact, reaches a peak of 0.64 after 3 quarters but becomes insigni...cant after around 4 quarters. Hence, increasing government spending seems much less e¤ective in developing countries compared to industrial countries. As discussed below, this could be due to the fact that we see much less countercyclical ...scal policy in developing countries compared to industrial countries.<sup>14</sup> A similar pattern emerges for the multiplier when government spending is falling (Figure 5, panel A). While the impact multiplier is 0.37 (and signi...cantly di¤erent from zero), it becomes insigni...cant after four quarters.

# 3.4 Fiscal multipliers in recessions and expansions: Does it matter whether government spending is increasing or decreasing?

Motivated by the ...ndings from Sections 3.2 and 3.3, we now follow speci...cation (4) and look into the possible interaction between recession/expansion and government spending increasing or decreasing.

Since we know from Figure 4 that, all else equal, the multiplier is higher when government spending is going up than down, one would conjecture that we are underestimating the mul-

> 14 Recall that if we de...ne countercyclical ...scal policy as being in cell (1,1) or (2,2) in Table 1 or 2 (that is, contractionary policy in good times and expansionary policy in bad times), we can see that while industrial countries have spent 56 percent of the time in those cells, developing countries have spent only 36 percent of the time. The other side of the coin, of course, is that developing countries have spent 64 percent of the time pursuing procyclical ...scal policy – capture by cells (2,1) and (1,2) – compared to 44 percent of the time for industrial countries.

15
suggests that sexual activities should not be initiated in early teenage years. However, the reality demonstrates a very different picture; 61 percent of the youth answered that they had their first sexual relationship before age 16 compared to 29 percent of their parents (see Figure 4), indicating that the intergenerational risk transfer of early sexual engagement is at play and it is increasing across generations.

**Figure 4:  Age of First Sexual Relationship of Youth and Adults,** **<u>(percent)</u>**

<!-- Start of picture text --> 50<br>45<br>Youth Adult<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>age -11 age 12-14 age 15-17 age 18-20 age 21-23<br>Source: Data collected by authors in poor urban neighborhoods in the Northeast Brazil.<br><!-- End of picture text --> The survey reveals that 33 percent of the youth do not use any contraceptives, meaning that they are exposed to considerable risk of contracting HIV/Aids and other sexually transmitted diseases.

The youth are also at considerable risk of early pregnancy and fatherhood. Among the youth under 25 years of age, 31 percent had their first-born child before age 16. This is three times more than that of the adult population, where only 9 percent had their first-born child before age 16 (see Figure 5). Again, the intergenerational transfer of risk is strong.

17
that older women in the U.S., whose longer life spans make them more vulnerable to old-age poverty, are less financially literate than the older population as a whole, and less likely to plan for retirement.

# **_Financial literacy follows an inverted-U shape with respect to age_**

Financial literacy tends to peak among adults in the middle of the life cycle, and it is usually lowest among young people and the elderly. In the U.S., for example, those in the prime age group (25-65) tended to do about five percent better on the questions than those under 25 or over 65. Lusardi and Mitchell (2011a) note the difficulty of distinguishing between age and cohort effects in a single cross-section, but hypothesize that individuals likely accumulate knowledge over time which then decays as they age. Finke et al. (2011) likewise attribute lower financial literacy in older people to a decline in cognitive processes associated with old age, and rule out the effects of cohort and other demographic characteristics. A deeper examination of financial literacy among older Americans can be found in Lusardi and Mitchell (2009a).

However, there is a much larger literature on financial literacy and its determinants among young people, given low levels of literacy and the perceived need to equip young adults with the ability to manage their own finances. Lusardi et al. (2009b) find that less than a third of American teenagers (ages 12-17) possess basic knowledge of interest rates, inflation, and risk diversification. Women, African-Americans and Hispanics, and those with lower educational attainment are associated with lower levels of financial literacy. Mandell (2006) notes that there is even evidence that youth financial literacy has been declining since the late 1990s, based on the Jump$tart surveys administered to high school seniors. Beal and Delpachitra (2003), a study of Australian college students, also found low levels of financial literacy.

Various factors may account for different levels of financial literacy among youths. Valentine and Khayum (2005) find that working 10-20 hours a week, having a savings account, and plans to pursue post-secondary education are associated with high levels of financial literacy for high school students. Chen et al. (1996) find that college students’ knowledge of personal investment is low; business majors are more knowledgeable than non-business majors, and within business majors, finance/accounting majors are most knowledgeable. Chen and Volpe 11
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# **AGRICULTURE PRODUCTION AND TRANSPORT INFRASTRUCTURE IN EAST AFRICA: AN APPLICATION OF SPATIAL AUTOREGRESSION**

Atsushi Iimi,<sup>1 ¶</sup> Liangzhi You,<sup>2</sup> Ulrike Wood-Sichra,<sup>2</sup> Richard Martin Humphrey<sup>1</sup> 1 Sustainable Development Department, Africa Region The World Bank 2 International Food Policy Research Institute (IFPRI) Key words: Spatial autoregressive model; agriculture production; transport infrastructure.

JEL classification: H54; H41; Q12; C21; C34.

> ¶ Corresponding author.
**Table 3b: Growth in real per capita sectoral GVA and GDP by distance to major city**

|**Distance to Major City**|**Manufacturing **|**Construction**|**Services**|**GDP**|
|---|---|---|---|---|
|**2004-2006 Expansion**|||||
|Large City|2.98**|-1.44|1.49***|2.11***|
|25th Percentile|3.44***|-0.3|1.32***|2.29***|
|50th Percentile|3.81***|0.6|1.18***|2.44***|
|75th Percentile|4.42***|2.0|0.95***|2.68***|
|90th Percentile|5.03***|3.5|0.73*|2.92***|
|||**2008-2009 Rec**|**ession**||
|Large City|-12.30***|-28.83***|-7.62***|-6.80***|
|25th Percentile|-11.12***|-27.28***|-7.38***|-6.34***|
|50th Percentile|-10.21***|-26.07***|-7.18***|-5.98***|
|75th Percentile|-8.67***|-24.05***|-6.87***|-5.38***|
|90th Percentile|-7.15***|-22.02***|-6.55***|-4.78***|
|||**2010-2011 Reb**|**ound**||
|Large City|9.39***|2.95|-2.61***|3.63***|
|25th Percentile|9.25***|6.38***|-2.13***|4.48***|
|50th Percentile|9.14***|9.06***|-1.75***|5.14***|
|75th Percentile|8.96***|13.57***|-1.11***|6.25***|
|90th Percentile|8.78***|18.06***|-.48|7.36***|
|||**2013-2017 Exp**|**ansion**||
|Large City|2.76**|-6.30***|-2.63***|0.37|
|25th Percentile|2.93***|-6.36***|-2.34***|0.44|
|50th Percentile|3.06***|-6.39***|-2.11***|0.49|
|75th Percentile|3.28***|-6.47***|-1.73***|0.58|
|90thpercentile|3.50***|-6.54*|-1.36***|0.67|
|||**2018-2019 Rec**|**ession**||
|Large City|-1.09|-40.19***|-5.27***|-4.58***|
|25th Percentile|-3.19***|-38.46***|-4.45***|-3.85***|
|50th Percentile|-4.85***|-37.11***|-3.81***|-3.29***|
|75th Percentile|-7.61***|-34.84***|-2.74***|-3.24***|
|90thpercentile|-10.38***|-32.59***|-1.67*|-1.39*|

Notes: F-stat significance based on robust standard errors (clustered by province) in parenthesis *** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated combining major city (which corresponds to the main effect) and distance parameters (for each sector and time period separately) estimated in Table 3a. All regressions use data on all 81 provinces and employ province fixed effects.

Distance to major city is computed as the shortest distance between the province centroid and the centroids of two provinces that have the two major cities (Istanbul and Ankara) 21
We present four main findings. First, in contrast with the literature, we find that adolescent girls and boys in Uganda compete at similar levels. Second, we also do not find evidence that the empowerment effects of the program on girls were accompanied by increased competitiveness. Third, in line with previous evidence we document that having sisters, relative to brothers, is associated with a lower willingness to compete for boys in control communities. Fourth, strikingly, this pattern is reversed in treatment communities. There, we find that boys surrounded by (empowered) sisters are more willing to engage in competitive behavior.

The paper is organized as follows. Section 2 outlines the program and its impacts in order to sketch some of the theoretical background motivating the analysis. Section 3 describes the data and the lab‐in‐ the field experimental design. Section 4 presents the results. Section 5 concludes.

# **2. Background and Theoretical Context**

The Empowerment and Livelihood for Adolescents (ELA) program in Uganda, implemented by the NGO BRAC, provides adolescent girls with training on ‘hard’ vocational skills to help them successfully transition into the labor market, and on ‘soft’ life skills to enable girls to make informed choices about sex, reproduction and marriage. The program is delivered from development adolescent clubs, which are open five afternoons per week after school, and are led by a local female mentor. Participation is voluntary for girls aged between 14 and 20. To date BRAC has started 1,505 clubs in Uganda which have reached 70,000 girls.

Bandiera _et al._ [2015] evaluate the ELA program in Uganda using a randomized control trial methodology. The study took place in the urban or semi‐urban regions of Kampala and Mukono, and the mostly rural region around Iganga and Jinja. The research design delivered 100 treatment and 50 control communities. Each treatment community hosted an ELA club. A baseline survey was administered just before the establishment of the clubs to about 6,000 adolescent girls, 4,000 (2,000) of whom resided in treatment (control) communities. A first follow‐up survey took place in 2010. Bandiera _et al._ [2015] show that within two years the ELA program successfully empowered girls along several economic and social dimensions.<sup>2</sup> We list these impacts below in more detail in order to understand how they could translate into increased competitiveness for girls. We then discuss how these impacts could interact with sibling sex composition in shaping boys’ competitiveness.

## **2.1. Women’s Empowerment and Competitiveness**

There are at least two possible channels through which the program could foster competitiveness for girls. The first channel operates through the program’s impact on economic empowerment. Bandiera _et_

> 2 As club participation is voluntary, both Bandiera _et al._ [2015] and our analysis focus on intent‐to‐treat impacts.

3
which increases significantly under both treatment arms. In other words, the psychosocial value to employment appears to be driven largely by the non-pecuniary dimensions of the employment experience.

# **5.4 Impacts of employment on reported physical health, cognitive function, and economic decision-making**

The positive effects of employment extend to other measures beyond psychosocial health. Table 3 presents results on reported physical health, cognitive function, and incentivized measures of risk and time preference. We observe a significant increase in the days reported healthy. This effect may be due to ‘real’ health improvements from increased exercise (which has also been documented to translate to improved mental health (Herbert et al. (2020))) from the employment task or ‘perceived’ health improvements in which improved psychosocial well-being translates into feeling less physically ill. Should the channel be exercise, we may expect health improvements to grow over time. Our weekly data on days healthy suggests this is not the case: we observe the treatment effect on health from the first week of working, and the gap remains steady throughout the following two months (Appendix Figure A3).

The employment arm also significantly improves cognitive function as measured through an index of memory and basic arithmetic tests, a finding consistent with a large psychology literature documenting the relationship between cognitive processes and depression (Semkovska et al., 2019). As with physical health, improvements to cognitive function are unlikely to be a direct product of the employment task itself, which was specifically designed to require no literacy or mathematical skill. Rather, these results are suggestive of a downstream impact to reducing depression through the experience of employment.

Finally, we find no change in time preferences: treated individuals are no more or less likely to discount the future relative to control counterparts, although results may have differed had we engaged participants in an effort or consumption-based time preference game rather than a financial one. However, we find a substantial increase in risk tolerance among the employed. A greater preference for risk-taking may be indicative of employment serving as a form of psychological ‘insurance’ that allows participants the mental bandwidth to exercise greater risk. This is consistent with the positive impacts of employment on stability as well as with a key motive underlying universal basic income (UBI) in the developing world (Banerjee, Niehaus, and Suri, 2019). Interestingly, however, we document no parallel increase in risk tolerance in the cash transfer arm. Our result on risk preference also echoes a potential consequence of depression and anxiety described in Ridley et al. (2020), although empirical 15