rafmacalaba/datause-encoder-data huggingface dataset:
sistencies in the measurement of light intensity between the DMSP and VIIRS satellites, the combination of nightlights data from these satellites is a challenge hence limiting the application of nightlights data over the long time horizon. To address this issue, Li et al. (2020) have derived consistent and integrated nightlights data from the two satellites by harmonizing the inter-calibrated nightlights data from the DMSP and a simulated DMSP-like nightlights data from the VIIRS satellite.<sup>12</sup> Using this data, I compute yearly average nightlights intensity for each subnational administrative district boundary as a proxy for the level of economic activity in each administrative boundary per year. I then compute the annual growth in nightlight intensity as the log-differences in the annual average nightlight intensity for each district. In the online Appendix, I conduct several robustness checks to ascertain the robustness of the baseline results using the harmonized data. First, Figure A1 in the online Appendix clearly shows a very high correlation between the harmonized and non-harmonized nightlights data for the period 1999-2013. Also, relying exclusively on DMSP nightlights data between 1998 and 2013, I show in Table A4 that the baseline results hold even if we restrict the data to the pre-VIIRS data.

# **2.3 Complementary data**

I complement the analysis with data on indicators such as natural resource endowments from the IMF, quality of governance from the Polity IV database,<sup>13</sup> distance to the coast,<sup>14</sup> and average annual temperature and total precipitation obtained from the ERA5 Global Reanalysis Database by the Copernicus Climate Change Service.<sup>15</sup> Data on fixed line telephone penetration and access to electricity are obtained from the World Bank’s World Development Indicator database. Summary statistics of indicators used in the analysis are presented in Table A1 in the online appendix.

> 12See Li et al. (2020) for details and access to the dataset using https://figshare.com/articles/dataset/ Harmonization_of_DMSP_and_VIIRS_nighttime_light_data_from_1992-2018_at_the_global_scale/9828827/2 13 https://www.systemicpeace.org/polityproject.html. Also available for download here https://tcdata360. worldbank.org/indicators/h6906d31b?country=BRA&indicator=27470&viz=line_chart&years=1800,2018

> 14https://oceancolor.gsfc.nasa.gov/docs/distfromcoast/

> 15https://cds.climate.copernicus.eu/cdsapp#!/home

7
infrastructure, weather patterns, and exchange rates, among many others. For these reasons, food dependency ratios will not reveal the extent to which international food price movements are transmitted to domestic food prices.

Importantly, with substantial increases in international food prices in the 2000s and fast-growing domestic food demand due to high population growth rates, fiscal and inflationary pressures have grown as well. Fiscal pressures vary by country and reliable data is generally patchy, though estimates suggest that most MENA countries may be spending several percentage points of their GDP on food subsidies.<sup>4</sup> These are costly and, if targeting is poor, arguably not the most effective way of protecting vulnerable populations. Recognizing this dilemma, over the past two decades, countries in the MENA region have attempted to reform their food subsidy systems. Some governments have been more successful than others in cutting subsidies and improving targeting. Measures such as self-targeting, increasing prices by stealth, subsidy rationing and replacing subsidies with cash transfers, sometimes succeeded in reducing the government‟s fiscal burden. But many other reforms fell short or needed to be reversed after public pressure. The result has been partial reforms, with all countries still offering at least some food price subsidies, while social assistance schemes have in general not adequately channeled sufficient resources to the needy.

During political transitions, distributional and public policy challenges are exacerbated in countries dependent on imported food. A World Bank (2011a) report found that many MENA governments responded to discontent with economic and social problems at the onset of the Arab spring by extending food and fuel subsidies. Countries that increased food subsidies and/or imposed price controls during the first half of 2011 include Bahrain, Kuwait, Oman, UAE, Algeria, Yemen, Jordan, Egypt, Tunisia, and Morocco (World Bank, 2011b).

High international food prices can complicate macroeconomic management. Subsidy costs take away fiscal resources that can be used to finance growth-enhancing investments. Because of the high food shares in the consumption basket for many MENA countries,<sup>5</sup> and second-round effects, high domestic food inflation can have

> 4 In 2009 food subsidies ranged from 0.8 percent of GDP in Jordan to 3.5 percent of GDP in Iraq (World Bank 2011a).  See also Albers and Peeters (2011).

> 5 Food consumption shares are close to 0.6 in low-income MENA (e.g. Yemen and Djibouti), between 0.35 and 0.45 in middle-income MENA (Morocco, Algeria, Lebanon, Jordan, Syria, Egypt, Tunisia, Iraq) and below 0.25 in high-income MENA (Saudi Arabia, Bahrain, Kuwait, Qatar, UAE).

-4-
Canada Lands Company, _Acquisition of Landmark Site in Ottawa_ , May 9, 2011 [available at above website] National Executive Forum on Public Property, <u>www.publicpropertyforum.ca</u> [convenes senior managers of public land assets and other public property from federal, provincial, and municipal levels, with academics and private sector property managers.]

# **UNITED STATES**

Defense Base Closure and Realignment Commission: _Final Report to the President_ , vols 1 & 2, 2005 [recommendations and rationale for base closures and realignment] _Federal Land Asset Inventory Reform {FLAIR] Act,_ introduced in Senate, 2011. [repeats legislation introduced annually since 2009 to mandate a comprehensive inventory of federal government landholdings and valuations] Government Accountability Office, _Federal Real Property: Excess and Underutilized Property Is an Ongoing Problem,_ Testimony before Subcommittee on Federal Financial Management, Government Infrastructure and International Security, Senate Committee on Homeland Security and Governmental Affairs, 2006. [one of a series of reports by GAO highlighting land asset management as a “high risk” area of federal management.]

# **CHINA**

Nelson Chan, “Land Acquisition Compensation in China: Problems and Answers,” International <u>Real Estate Review. [2003, vol 6, No. 1, pp136-152] [reviews past acquisition practices and land</u> value arbitrage] Deng, Yongheng, Randall Morck, Jing Wu, and Bernard Young. _Monetary and Fiscal Stimuli, Ownership Structure, and Chinese Housing Market._ National Bureau of Economic Research, Working Paper 16871. March 2012. [impact of China’s 2008-09 stimulus package on urban land and housing markets, and SOEs’ role in property demand] Peterson, George E., “Land Leasing and Land Sale As an Infrastructure Financing Option,” in George E. Peterson and Patricia Clarke Annez, (eds), _Financing Cities: Fiscal Responsibility and Urban Infrastructure in Brazil, China, India, Poland, and South Africa,_ World Bank 2007.

Wu, Jing, Joseph Gyourko and Yongheng Deng, _Evaluating Conditions in Major Chinese Housing Markets._ National Bureau of Economic Research. 2010. [Reports increase in land prices and change in structure of property market] 25
Since 2005, net aid inflows averaged US$ 306 million (32 percent of GDP). The period 2005 to 2008 is therefore referred to as aid-surge period.

# 3.3. <u>Absorption and spending in Burundi</u>

The following analysis focuses on net aid inflows as they reflect the actual aid inflows that are available as financing source. Burundi’s policy response to the increase in net aid inflows has been to mostly absorb the additional resources. This is demonstrated by the ratio of the change in the non-aid current account balance to change in aid of 86 percent (Table 2). It implies that most of the received foreign exchange has been sold and used for imports. The increase in imports reflects not only the direct government imports associated with higher aid, but also second-round effects attributable to higher aid-induced spending by government and the private sector. Reserves have only increased slightly and a small improvement of the non-aid capital account balance was recorded.

**Table 2:** Has aid been absorbed? (percent of GDP, annual averages)

||Pre-Aid Surge<br>2001-2004(average)|Aid Surge<br>2005-2008(average)|Difference|Absorption of<br>incremental aid|
|---|---|---|---|---|
|Net aid inflows|14.9|32.0|17.1||
|Non-aid current account balance|-18.1|-32.9|-14.7|Mostly absorbed|
|Non-aid capital account balance|-1.4|1.2|2.6|86%|
|Change in reserves (- =increase)|4.6|-0.3|-4.9||

_Source:_ Authors’ calculation based on IMF data; change in reserves includes exceptional financing that could not be allocated to the current or capital account balance.

A closer look at the non-aid current account deficit confirms that imports have been the main cause of its widening from 18 (2001-04) to 33 (2005-08) percent of GDP. The import bill for both goods and services increased substantially (Table 3 and Table A-2). The ratio of goods imports to GDP increased from 19 to 28 percent while services imports as share of GDP recorded an increase from 7 to 18 percent. Exports of goods and services, on the other hand, did not increase significantly in absolute terms and remained fairly constant as percentage of GDP between 2001 and 2008.

Services imports averaged about 30 percent annual growth whereas goods imports grew by about 16 percent annually in the same period. The share of service imports in total imports of goods and services has consequently increased from 25 percent on average between 2001 and 2004 to 40 percent on average between 2005 and 2008. The main components of services imports are transport and travel costs. The high transport costs are attributable to the fact that Burundi is a landlocked country. Increasing spending on the travel subcomponent can be traced to frequent international travel of the government as well as the payment for international consultants for technical assistance.

9
2000, falling to 7.5 percent of the population by 2019. The share has also fallen if one includes the surrounding region of Tashkent together with the city: this figure totaled about 4.5 million in 2000 (18.3 percent of the total population) and 5.4 million in 2019 (16.3 percent).

**Figure 6: Place of Origin and the Primary Reason for Migration**

<!-- Start of picture text --> Place of Origin Primary Reason for Migration<br>2% 1% 3%<br>4% 2%2%<br>19%<br>6% 4%<br>9%<br>21%<br>50%<br>77%<br>Work Related To Join Family<br>Uzbekistan Kyrgyzstan Moving with Family Study<br>Tajikistan Russian Federation Security Government moved<br>Kazakhstan Other CIS Other<br><!-- End of picture text --> _Notes: Derived from survey responses in the L2CU Baseline. Author’s Calculations. Migration is defined as moving from one district (or another country) to their present location in Uzbekistan. This information is presented for the set of people who report ever having moved in the nationally representative baseline survey (2018)._ Thus, according to official estimates, the city of Tashkent was, by far, the slowest growing “region” in the country between 2000 and 2019. Tashkent’s population only grew by 17 percent, while the Tashkent region (surrounding the city) was the next slowest growing, at 23 percent. The total population of Uzbekistan grew by much more over this time; it was about 36 percent larger in 2019 than in 2000, more than double the increase recorded in Tashkent. This trend is driven by higher fertility rates in urban areas paired with very low rates of migration.

But despite these low rates of migration and urban population growth, labor markets are remarkably stronger in urban areas of Uzbekistan, and particularly in the city of Tashkent. In July 2018 when the L2CU baseline data were collected, the employment rate among people aged 15-65 stood at 41 percent in rural areas, at 46 percent in urban areas, and at 57 percent Tashkent city. The median wage in Tashkent city was 61% higher than the national average in 2018, and 88% higher than among those 17
Given this limitation, we proceed by combining two extreme assumptions on intergenerational mobility. First, we assume zero intergenerational mobility, which implies that, first, children have the same level of education of their parents and, second, that internet access in childhood can be approximated by the current availability of internet in the household where adult _i_ lives. The other alternative extreme assumption is of perfect mobility. In that case we randomly assign internet access to each adult in a way that is consistent with the overall internet coverage among the population, and also randomly impute parental education, independent of their own level of education.

Finally, we combine these two extreme alternatives to obtain our estimates. Weights for the two alternatives are assigned based on country intergenerational mobility estimates provided by Neidhöfer, Gasparini, and Serrano (2018). In particular, we take the typical measure of intergenerational mobility of education, which arises from a regression of children’s education on their parents’ education. The lower is educational mobility, the higher is the weight we assign to the scenario of zero mobility described above.<sup>15</sup>

# **Dropouts**

As discussed in section 2, the COVID-19 pandemic had a more dramatic effect on young people that go beyond the interruption of classes during several months: it may have implied even a dropout from the education system. We adopt the following strategy to measure the effect of this factor. As above, we assume that without the pandemic the educational structure in the future (2045)

> 15 For instance, the intergenerational educational mobility measure based on regressing children’s education on parental education estimated by Neidhöfer _et al_ . (2018) in Peru is 0.51. This is, in fact, a measure of _persistence_ , so in our simulations we assign the weight 0.51 to the zero-mobility case and the complement (0.49) to the perfect mobility case. For robustness, we also (i) consider the correlation coefficient as a measure of persistence rather than the regression coefficient, and (ii) alternatively use intergenerational mobility parameters estimated with Latinobarometro and national household surveys microdata, both provided in the original study by Neidhöfer _et al_ . (2018). Our results are robust to all different specifications.

18
ulation are aged under 25 (Karkee and Niall, 2023) and the youth NEET rate is 26 percent. — Our sample is drawn from NEET youths in urban and peri-urban Tanzania where the youth NEET rate is 14 percent. We investigate the extent and correlates of gender differ— ences in SES, as measured by self-reported scales and behavioral measures which consist of situational judgement tests (SJTs) and tasks.

Our main results are threefold. First, we find a significant male advantage in all 12 selfreported SES measures that are positively framed – i.e., where answering more affirmatively (e.g., agree, strongly agree) leads to a higher score. The gender gap is 0.20 standard deviations in the aggregate self-reported SES measure. This male advantage is associated with higher social desirability bias, gender-biased beliefs on abilities, and lower cognitive ability. Second, gender differences disappear for the majority of the skills when using behavioral measures. Of the 14 skills, a small gender gap in behavioral measures is found for three skills and two sub-skills with a magnitude of 0.04 to 0.08 standard deviation units. Third, the gap in self-reported and behavioral measures is also higher at higher levels of social desirability, lower levels of cognitive ability and among individuals holding more regressive gender beliefs. The gap between self-reports and behavioral measures is also higher among men who view men as generally better equipped with problem-solving and decision-making skills than women, while the gap is not correlated with such beliefs among women. We take this as suggestive evidence that the gap between self-reported skills and behaviorallymeasured skills among men partly reflects men’s overestimation of their own skills due to biased beliefs around men being more skilled in certain domains than women, rather than women’s underestimation of their skills.

We expand existing literature in three ways. First, this paper contributes to a wider discussion on gender gaps in SES. There is mixed evidence on gender differences in self-reported SES levels. A recent cross-country study using data from more than 40,000 individuals across 17 African countries, including the self-reported data in this study, documents a significant male advantage in all self-reported skills except self-control (Ajayi et al., 2022). However, 2
Andr ́ee

# 6.2.1 Poisson-type regressions for case incidence

In table 4, the case counts are used in regression instead of case density. Model 8 estimates a standard Poisson regression. Model 9 estimates a Poisson regression with stepwise selection following the AIC. Model 10 presents the same model, allowing for over-dispersion. Finally, Model 11 allows for a zero-inflated Negative Binomial distribution and model 12 performs step-wise AIC under the Negative Binomial.

Table 4: Dependent Variable: Confirmed COVID-19 cases.

|**Variable**|**Model 8**|**Model 9**|**Model 10**|**Model 11**|**Model 12**|
|---|---|---|---|---|---|
|(Intercept)|-13.75***<br>(2.23)|-13.75***<br>(1.64)|-13.75***<br>(5.12)|-9.89**<br>(4.86)|-4.14<br>(2.72)|
|Population density|-0.35***<br>(0.02)|-0.35***<br>(0.02)|-0.35***<br>(0.08)|-0.30***<br>(0.08)|-0.29***<br>(0.07)|
|Share of male population|-22.38***<br>(1.90)|-23.12***<br>(1.85)|-23.12***<br>(5.77)|-16.07***<br>(4.96)|-13.29***<br>(4.41)|
|Share 14 to 24|0.07***|0.07***|0.07|0.04||
||(0.02)|(0.01)|(0.04)|(0.05)||
|Share 25 to 44|0.16***|0.16***|0.16***|0.17***|0.15***|
||(0.02)|(0.02)|(0.05)|(0.05)|(0.04)|
|Share above 65|0.13***|0.13***|0.13**|0.09*|0.05*|
||(0.02)|(0.02)|(0.05)|(0.05)|(0.03)|
|Share of unmarried|0.14***|0.14***|0.14***|0.11***|0.10***|
||(0.01)|(0.01)|(0.04)|(0.04)|(0.02)|
|Share of household without children|0.06***|0.06***|0.06|0.04||
||(0.02)|(0.01)|(0.04)|(0.04)||
|Average household size|2.75***|2.58***|2.58***|1.04||
||(0.44)|(0.25)|(0.77)|(0.68)||
|Share of western immigrants|0.03***|0.03***|0.03|0.00||
||(0.01)|(0.01)|(0.02)|(0.02)||
|Share of non-western immigrants|0.03***|0.03***|0.03**|0.02||
||(0.00)|(0.00)|(0.01)|(0.02)||
|Share enduring noise above 50km|0.03*|0.04***|0.04|0.01||
||(0.02)|(0.01)|(0.04)|(0.04)||
|Share of obese|0.06**|0.05***|0.05|-0.03||
||(0.02)|(0.02)|(0.05)|(0.04)||
|Share of non-drinkers|-0.05***<br>(0.01)|-0.05***<br>(0.01)|-0.05**<br>(0.02)|0.00<br>(0.01)||
|Share of smokers|0.11***<br>(0.01)|0.12***<br>(0.01)|0.12***<br>(0.04)|0.06<br>(0.04)||
|Mean PM2_._5|0.52***<br>(0.05)|0.47***<br>(0.02)|0.47***<br>(0.06)|0.45***<br>(0.05)|0.44***<br>(0.04)|
|R2|0.59|0.59|0.59|0.47|0.47|
|AICc|3995.20|3983.77|3983.77|2242.19|2227.05|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 The residuals are checked for spatial autocorrelation, and significant residual clustering was still found. Spatial autocorrelation is not easily addressed in count data with standard regression implementations, hence the results are simply from a mis-specified model. For this reason, the models that relax distributional assumptions (10-12) should provide improved indications of significance, with Model 10 being proffered. The main results of the paper presented in section 4 take spatial processes explicitly into account and are in turn preferred over Model 12.

A few results using the count data echo the main findings. In particular, the slope of population density is negative, indicating that the more populous areas in the Netherlands 16
|Ethnolinguistic|0.16|0.00090|0.00268|
|---|---|---|---|
|fractionalization||||
|Absolute latitude|0.13|0.00002|0.00005|
|Fraction speaking<br>foreign language|0.11|0.00038|0.00144|
|Fraction Catholic|0.10|0.00041|0.00180|
|British colony<br>dummy|0.09|0.00026|0.00133|
|Ratio of workers to<br>population|0.08|0.00059|0.00295|
|Public education<br>share|0.08|0.00754|0.03897|
|**Private credit**|**0.07**|**0.00025**|**0.00138**|
|Number of years<br>of open economy|0.06|-0.00030|0.00179|
|Spanish colony<br>dummy|0.06|-0.00016|0.00115|
|Fraction Jewish|0.05|0.00045|0.00319|
|Primary school<br>enrollment|0.05|0.00027|0.00214|
|Fraction Protestant|0.04|-0.00006|0.00108|
|Degree of<br>capitalism|0.04|0.00002|0.00018|
|Age|0.03|-5.5e-07|0.00001|
|Outward<br>orientation|0.03|-0.00004|0.00043|

26
9 changes accompanying urbanization play a generally positive role in overall poverty reduction.” (pp 690‐ 691).

We will discuss specific mechanisms underlying these types of findings, which are quite common in the literature. However, they still only address urbanization in the aggregate. What about the composition of urbanization, in particular, the relative growth of small towns and big cities? This is more difficult to assess—the greater is the level of disaggregation, the weaker is the availability and quality of relevant data. However, Christiaensen and Todo (2014) use such disaggregated data from 51 countries to ask the following question: “....if two economies were to grow at the same speed, would the rate of poverty reduction be faster when people move out of agriculture to larger cities (empirically defined as exceeding 1 million people) or when they move out of agriculture into small towns and the surrounding rural economy?” (Christiaensen, De Weerdt and Kanbur, 2015, p. 3). For 1980‐2004, Christiaensen and Todo (2014) find that there is indeed an additional effect on poverty reduction when people move into secondary towns and the rural non‐farm economy from agriculture (Table 1). Of course growth matters for poverty reduction. But for given growth rate migration to secondary towns matter more than migration to large cities.

But even when leaving growth out, using a totally reduced form as in Ravallion, Chen and Sangraula (2007) cited above, the authors find that it is moves to secondary towns that reduce poverty, not population expansion of the cities. This suggests that the inclusive nature of growth patterns associated with secondary town expansion outweighs the possible loss in terms of growth, at least on average, in this sample of experiences from across developing countries. This is somewhat surprising as larger cities are considered to yield faster economic growth because of larger agglomeration economies, as emphasized by the New Economic Geography School. Yet, recent evidence looking at the effect of average city size on economic growth suggests that this may not hold to the same degree in developing