rafmacalaba/data-use-mentions-extended huggingface dataset:
differences.<sup>9</sup> Furthermore, even though firms’ geographic distribution is representative for the country, representativeness at the city level is not automatically assured. Because this paper focuses on the correlation between pollution and firm performance -at the firm level, representativeness at

> 8 For more details on WBES, see http:\\www.enterprisesurveys.org.

> 9 Results appear to be slightly stronger for a subsample of firms located in cities with a population between 250,000 and 1 million people.

6
Younkin and Kuppuswamy 2018), or other characteristics of a founder or venture. Future research is needed to assess the effect of systematizing inquiry in these contexts. Similarly, we designed this research to isolate the effects of gender and treatments on investment decisions. As such we do not theorize other inputs to investment decision-making such as organizational preferences (Tyebjee and Bruno 1984, Cohen et al. 2019), individual investor preferences (e.g., Huang 2018), or extraneous conditions such as weather (e.g., Dushnitsky and Sarkar 2022).

# **8. Discussion**

_What is the effect of investment organizations’ evaluation practices on gender disparities in funding innovation?_ We hypothesized that investment organizations could change their evaluation systems to invest into female-founded startups and designed and tested interventions in a two-stage field experiment. In the first stage, we tested whether prompting investors to systematically inquire about risk and reward would reduce gender disparities in investment decisions early in an investor-founder relationship. An analysis of 1,341 decisions taken by 278 investors suggested that prompting investors to inquire about both prevention (risk) and promotion (growth) in an evaluation template resulted in more consistent inquiry – investors asked more prevention-focused questions to all startups, but particularly those with all-male founding teams. This reduced gender disparities in evaluation in the treated group compared to a control.

In the second stage, we tested whether systematizing inquiry could affect collective investment decisions that allocated $320,000 to 16 of 69 startups over three months. Leveraging a panel dataset of 1,530 decisions, we tested the effects of a bundled treatment of prompting investors to systematically inquire about: 1) risk and reward; and 2) startup progress. Treated investors more positively assessed startups with female founders than the control. This resulted in meaningfully higher scores that affected the likelihood of investment for startups with female founders. Differential effects between treatment and control appeared to be driven by how the investors assessed startups’ venture 33
sup>21</sup> Furthermore, a lack of up‐to‐date and accurate spatial data on these areas adds to the challenge. In order for policy makers and planners to make well informed decisions, high quality maps are essential.

This study uses high resolution satellite imagery to detect and identify land that appears to have slum‐ like characteristics, or more accurately put, ‘irregular’ neighborhoods with tightly packed roofs, surrounded by minimal vegetation, and often irregular rooftop angles. This identification was achieved using a semi‐automated classification approach that examines the texture and structural composition of various neighborhoods, and then groups land with similar patterns to the same class. This method was originally developed to accurately detect shanties in cities throughout the world, and has been proven effective in a diverse set of cities.<sup>22</sup> It has since been adopted by the US Census Bureau and US Department of Energy’s Oakridge Laboratory.<sup>23</sup> In this work, we also explored its ability to detect commercial/industrial land, barren land, and vegetation. Commercial and industrial areas were 21 UN Habitatat. Urban Patterns for a Green Economy: Leveraging Density.

22 Jordan Graesser et al. 2012 Image based Characterization of formal and informal neighborhoods in an urban landscape. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (4) August:1164‐ 1176.

23 High‐Resolution Urban Image Classification Using Extended Features: Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference Dec. 2011. Author: Vatsavai, R.R. Published by IEEE.

10
with recent information about the distribution of energy subsidies between the richest and poorest quintiles, estimated using detailed Household Income and Expenditure Survey in their country. This information was selected as a very simple way to illustrate the regressivity of energy subsidies, as it was expected that even respondents with very low levels of education would be able to understand it. The environment treatment provided respondents with information about the amount of money (in local currency units) that energy subsidies cost their country due to their contribution to air pollution and climate change (based on IMF [2023]). This number was complemented by a generic figure across countries highlighting some of the likely consequences of climate change, specifically extreme temperatures, floods, droughts (according to IPCC [2023]), and air pollution (which is identified as a key environmental externality in the IMF [2023] estimates of the implicit costs of subsidies). Examples of each of these information treatments for Indonesia are shown in Figure 1 below.

# **[Insert Figure 1]**

# **3.1.4 Additional design features**

The survey included two additional layers of randomization to test whether there were any differences between the amount of information provided to respondents about subsidies in their country and the level of energy price rises that would occur from the removal of energy subsidies. The first additional layer of randomization occurred in the second section of the survey, where respondents were informed that energy subsidies do exist in their country. Half of the respondents were randomly allocated to be informed about the share of energy prices that the government effectively pays on their behalf (the average subsidization rate that is presented in IEA [2023]) and the other half were informed only about the existence of subsidies (i.e., they were not told the amount of subsidization). The second additional layer of randomization occurred in the third section of the survey, where respondents were asked about their levels of support for a reduction in energy subsidies that would result in a
partitioning of the data. However, geography matters for women also, only the women in urban areas and developed states experienced substantial decline in sibling correlations. As a result, the gender gap in sibling correlation has disappeared in urban areas though it remains virtually unchanged in rural areas. We also find that among the urban women, it is the lower caste women who experienced the largest decline in the sibling correlation. The evidence on improvements in educational mobility of women is similar to the available evidence on China and Malaysia (see Emran and Sun (2011) on China and Lillard and Willis (1994) on

> 16 The recent evidence using NSS data shows that the influence of caste and religious identity on the strength of intergenerational link between parents and children has become much weaker (Hnatkovska et al. (2011)). Our results also show a similar pattern. It is however, important to note that the direct effect of lower caste and Muslim dummy remains significantly negative on children‘s educational attainment.

> 17 Note that a formal test of equality of the estimates in 1993 and 2006 rejects the null because of very small standard errors due to the large sample sizes (number of observations is 34000 in 1993 and 38000 in 2006). However, statistical precision is largely irrelevant here, because the difference in the numerical magnitude of the estimates between 1992/93 and 2006 is very small in most of the cases, suggesting the lack of any substantial change in intergenerational mobility over a period of almost a decade and a half of impressive economic growth.

6
dummy|(0.33)|(-0.85)|(-0.99)|
|Time trend|0.0172|0.323***|0.288***|
|(=0 in 2000)|(0.68)|(7.19)|(6.11)|
|n|1,378|1,392|1,378|
|R-squared|0.134|0.300|0.232|
|RMSE|4.75|8.03|7.97|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

11
the interaction between the spatial effects and the factor loadings. To tackle this issue, Shi and Lee (2017) develop a bias correction that yields an asymptotically normal, properly-centered estimator.<sup>5</sup> The estimations reported below for the specifications including common factors employ the bias-corrected estimator; Appendix B provides a brief description of the correction.

# **3 Data**

We use a large cross-country data set drawn from the United Nations National Accounts database. To circumvent potential outliers and data errors, we exclude (i) very small economies with total population less than 500,000, owing to their often extreme volatility; (ii) countries featuring any observations with annual real GDP growth in excess of 40%; and (iii) countries with standard deviation of real GDP growth exceeding 10%. This yields a balanced panel of 117 countries over the period 1970-2016.<sup>6</sup> The sample countries account for more than 90% of world GDP in 2016.

> 5Vega and Elhorst (2016) also employ QML estimation in a setting featuring both spatial effects and common factors, specifying the latter as cross-sectional averages as in Pesaran and Tosetti (2011), and assuming that each cross-sectional unit is too small to affect the averages, so that they can be assumed exogenous – an assumption unlikely to hold in our setting as the sample includes some very large economies.

> 6We employ GDP growth data from the United Nations National Accounts database because it reaches up to 2016. In contrast, PWT data only reaches up to 2014. Over the common time sample, the correlation between the GDP growth rates derived from both sources exceeds 0.99.

7
feasible generalized least squares (FGLS) were employed. The null hypothesis of homoscedasticity is not rejected at conventional levels of signi...cance.<sup>13</sup> However, the test derived by Wooldridge (2002), suggests that the null hypothesis of no ...rstorder autocorrelation is rejected in favor of AR(1).<sup>14</sup> The inferences drawn from the 1982, 1987, and 1992 temporal cross-sections are supported by the pooled data. Both OLS and FGLS regressions produce statistically signi...cant parameter estimates. We again ...nd that ...rm size dispersion decreases with increasing levels of rental and resale market activity and higher rates of capital depreciation. When (15) and (18) are estimated with OLS, the maintained hypotheses H01 : b1 = b2 and H02 : c1 = c2 are not rejected at the 5 percent level.<sup>15</sup> However, we do note a di¤erence in the estimated parameters when equations (15) and (18) are estimated with OLS and when they are estimated with FGLS. This is not surprising since OLS and FGLS are di¤erent estimation procedures; and, in any case, it is not easy to determine whether such di¤erences are statistically signi...cant.<sup>16</sup>

> 13 We perform the Bickel (1978) version of the Breusch-Pagan test on (18). This tests for both within and between heteroscedasticity. We regress the squared residuals on ��powers of the predictions: for � = 5; F =1:03 and Prob > F = :39: Thus for � = 5, the null hypothesis of homoscedasticity cannot be rejected at the 5% level. For � = 6 to 10, we ...nd that the null hypothesis of homoscedasticity cannot be rejected at the 10% level.

> 14 When we apply the Wooldridge test on (
be partly due to the lower average age, as the age bracket definition differs from the rest of the studied countries; however, anecdotal evidence suggests a similar relevance of private rental for young households, as in Poland.

Excluding Poland, where the private rental share is almost invariant over age brackets, housing starters in the ECA region are between 2.6 and 6.5 times as likely to be private rental tenants as the population as a whole. For such households, subsidized rental housing such as university dormitories and company housing is available only in isolated cases. Consequently, starters in the region are relegated to seek rental tenure in the private market, with its frequently much higher rents (see below).<sup>50</sup> In the category of young families (Table 6 and Figure 5), with household heads between ages 30 and 44, the owner-occupied ratio rises; but still the private rental sector is of high relevance, catering to between 5 and 27 percent of households. Given the likely high degree of underreporting of private rental status in the surveys for Romania and Russia discussed earlier, one should consider the figures obtained for these countries in all age brackets as their lower boundaries.

**Table 6. Urban Tenure by Young Households in Studied Countries**

|||**Armenia**|||**Lithuania***||
|---|---|---|---|---|---|---|
|**% of households**|**Total**|**Starters**|**Young**<br>**families**|**Total**|**Starters**|**Young**<br>**families**|
|Owner-occupied|91.6|68.2|85.8|82.6|60.4|83.0|
|
.801|
|Cities and<br>Districts|0.630|0.678|0.699|0.710|0.690|0.718|0.694|

Source: Ministry of Finance, Government of Indonesia Fiscal needs of provinces and cities/districts are determined separately for each of this group by developing a composite index based upon relative population, relative area, relative construction price index, inverse of human development index (comprising arbitrary weights for life expectancy, literacy rate, mean years of schooling and purchasing power adjusted relative real GDP per capita) and inverse of relative nominal per capita GDP. The weights for the above mentioned factors vary for provinces and districts/cities and over time for each group based upon the specified value to be achieved for the Williamson‘s index (see Table 8). The resulting indexes are multiplied by the per capita aggregate spending for the past year to arrive at numerical values of the expenditure need component. DAU allocation for each jurisdiction is then determined as follows: DAU= Basic Allocation + Fiscal Gap (Fiscal needs minus Fiscal Capacity) The DAU is a gross program and compensates a jurisdiction for excess needs but does not tax regions with excess fiscal capacity. The jurisdictions displaying negative fiscal gap (surplus fiscal capacity) e.g. Jakarta metropolitan region receive only the basic allocation and the negative fiscal gap is ignored.

15
In what follows, this paper illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi‐automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, 16, provided the lower cost.

# URBAN DENSITY

Urban economists have long argued that a compact and well‐connected city is necessary for the development of an economically strong and livable city. The benefits of dense cities are well documented and empirically proven. Advantages include, among many others, higher firm and worker productivity<sup>10</sup> and improved consumer access to goods and services.<sup>11</sup> In dense cities, firms face stiff competition, thus making only the most efficient and productive likely to survive. When nestled among many other businesses, firms are also encouraged to interact more frequently with their neighbors, promoting a drop in cost and an increase in speed of business transactions.<sup>12</sup> Consumers can also benefit from this density, with the opportunity for shorter commutes, lower travel costs, and more variety of available products.<sup>13</sup> Additional benefits include reduced per capita energy consumption
# **4.5 Exports and Economic Diversification**

**This section presents an overview of the export structure and behavior in recent years, since it adds important information on Togo’s stage of economic transformation and growth prospects.** Export growth of 5.6% per year explains almost half of the increase in aggregate supply between 2000 and 2007 (see Table 1)<sup>24</sup> . The ability of Togo to sustain the higher growth rates observed between 2000 and2007 therefore hinges on its capacity to sustain a growing exporting sector.

**Growth of exports has been accompanied by diversification of the export basket, as shown by the decrease in the Herfindhal‐Hirschmann index of export diversification (** Figure 14 **)**<sup>**25**</sup> **.** Togo is actually the second most diversified country within the WAEMU, something that is owed to a substitution of traditional products such as cotton, coffee and phosphates in favor of an array of new emerging products (Table 4).

**Figure 14: Non‐Oil Herfindahl Index for Togo, 1980‐2006 (LHS), and Compared to WAEMU Countries, 2006 (RHS).**

<!-- Start of picture text --> 0.4 0.9<br>0.8<br>0.35<br>0.7<br>0.3 0.6<br>0.25 0.5<br>0.4<br>0.2<br>0.3<br>0.15 0.2<br>0.1 0.1<br>0<br>0.05<br>0<br>Scale 0‐1 (1=no diverisifcation)<br>Scale 0‐1 (Lower = Higher Diversification)<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
8 entire period to the replacement year not covered by the other phases. All countries with initial TFR under 3.0 exhibit only this phase. All other countries also go through this phase, though it may begin later.

The age pattern of fertility was allowed to change with its level. The age patterns when TFR is 7.6, 4.1, or 2.8 are shown in Figure 4. The - mean age at childbirth declines from 28.86 years when TFR 7.6, to 28.45 years when TFR - 4.1, to 27.98 years when TFR - 2.8. For intermediate TFRs, the age pattern falls between these curves.

# Hijration level and trend

Estimates of international migration come from the U.N. 1984 assessment and the U.N. 1988 revision, documents prepared by the U.N. for the 1984 International Conference on Population, the U.S. Bureau of the Census (1985), Eurostat (1987), :.-d Bank sources. To deal with the volatility of refugee movements in various parts of the developing world, only those are counted as migrants who are believed likely to remain permanently in the receiving countries.

Available data seldom include age-sex distributions of migrants, which were instead chosen in this manner. For each country, some estimate was made of the sex ratio among migrants. Based on this sex ratio, an appropriate age-sex distribution was chosen from among alternative models. This distribution was assumed to be constant over time, regardless of the total number of migrants.

Trends in the volume of migration are based on past trends, but are still essentially speculative. For most countries, net migration is assumed to decline to zero by the year 2000; for the remainder, net migration declines to zero by the year 2025.

# Summary of Results

Summary projection results are provided in Tables 2-12. (Tables 4-12 appear at the end of this Introduction.) In discussing these tables, we will
suggest that Kinshasa and Brazzaville firms are not significantly different in their predicted responses to lower trade costs. No such differences appear between manufacturing firms and pure trading firms, and between large and small firms, either. The regression analysis confirms the finding of Table 7 that transport costs and administrative trade costs are bigger obstacles to trade than border delays, but the difference between those two is not statistically significant. Finally, we find some evidence that formal firms are more responsive to falls in overseas trade costs than informal firms.

**Trade facilitation across Malebo Pool appears to hold particular promise for promoting local manufacturing and small-scale currently informal activity** . In the last two columns of Table 9, we concentrate on trade flows across Malebo Pool, without distinguishing different types of trade facilitation. Unlike in the case of total trade, formal firms do not stand to expand their activities more than informal ones in response to trade liberalization across Malebo Pool. The estimated regression coefficient on firm size even suggests that smaller firms anticipate expanding their activities proportionally more than larger firms, perhaps because larger firms are already better able to circumvent trade barriers. Finally, we find very large and statistically significant regression coefficients on the binary variable “manufacturing firm”. This means that local producers anticipate much larger scope for inter-city trade expansion than pure trading firms.

**The predicted impact of trade facilitation across Malebo Pool is larger for Brazzaville-based firms than for Kinshasa-based firms.** This is not surprising given that Kinshasa is a considerably larger market, and hence the scope for cross-border expansion relative to their home market is significantly larger for RC firms than for DRC firms. However, we find no significant difference in predicted flows from Kinshasa to Brazzaville relative to those in the reverse direction (see the coefficients on _Kinshasa_ → _Brazzaville_ in Table 9). This implies that trade facilitation between Kinshasa and Brazzaville would lead to a balanced expansion of trade flows in both directions.

_Results: Qualitative Findings_

**The non-quantifiable survey answers shed additional light on the existing patterns of
|0.0343|
||||(0.0597)|(0.0705)|||(0.0723)|(0.0717)|
|Trained employees|||0.0932|0.0828|||-0.0602|0.0362|
||||(0.0614)|(0.0712)|||(0.0763)|(0.0721)|
|**Information from follo**|**w-up surveys**||||||||
|Changed practices||||-0.0110<br>(0.0721)||||-0.0624<br>(0.0747)|
|Receive gov support||||0.0440||||0.562***|
|||||(0.198)||||(0.166)|
|Observations|2918|578|578|434|2918|578|578|434|

Note: This table is based on Equation (1) in Section 3 of the paper. Columns (1) and (5) present the results for the tax administrative data. Columns (2)-(4) and (6)-(8) present the results for the survey data.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. 1 The omitted category is manufacturing. 2 The omitted category is Small. _Manager experience:_ Defined as the number of years experience of the top manager in the firm. _Competitive market:_ Dummy variable for whether a firm has had more than five competitors entering their market in the previous