rafmacalaba/data-use-mentions-extended huggingface dataset:
19 In a second robustness check, we check the implications of changing our measure of the returnee share of the population. In the main estimations, the share of returnees in the population is constructed with information from the survey roster. This information is more accurate for some communities than others. We interviewed 15 households in each community, but the number of households in each community ranged from 34 to more than 100. Therefore, we also present results with an alternative measure of the share of returnees. This information is constructed from the information provided by the community leaders, which includes information on the number of returnees in the community as well as overall population. The results are also similar with this alternative measure, although some more coefficients are statistically significant (see Table A10 in the Appendix). In particular, the coefficient of feeling reconciled with the war and participation in religious groups are now statistically significant (negative in both cases).

# **6. Summary and policy implications**

# **6.1 Summary of findings and overall implications**

Repatriation involves the reencounter of groups that were separated for many years and often decades. This process could lead to the reestablishment of old societal divisions or to the creation of new ones. Repatriation could also increase competition for scarce natural resources. These factors can have major implications for social cohesion at the community level. However, while there is a growing body of evidence on the impacts of forced migration on host communities (Becker and Ferrara 2019; Ruiz and Vargas-Silva 2013; Verme and Schuettler 2021), there is scarce research on the impacts of refugee return and particularly, on return and social cohesion. As explained by De Berry and Roberts (2018) “this absence weakens a programmatic ability to devise inclusive interventions in return areas.” In order to fill this gap, we explore these issues in Burundi, a country that experienced a major conflict and outflow of people, followed by large flows of refugee return a decade later.

Social cohesion is a
**Figure 12: Leadership approval ratings & local death count**

<!-- Start of picture text --> 60%<br>Erbil<br>50%<br>40%<br>Dhi Qar Diyala<br>Maysan<br>Najaf<br>30%<br>Basra Babil<br>20%<br>Baghdad<br>Sulaymaniya<br>10%<br>Nineveh<br>Wasit<br>Anbar<br>0%<br> - 100 200 300 400 500 600 700<br>Local death count per 1,000 adults (pooled 2013-2016)<br>Adults who approve of the job performance of the leadership in Iraq (pooled 2010-2012) (% of adults)<br><!-- End of picture text --> Source: Gallup World Poll and Iraq Body Count (IBC).<sup>3</sup>

# **5.  Quality of Service Delivery**

The Gallup World Poll includes questions on local public services. Among adults in Iraq, 71 percent are satisfied with services in the city or area where they live as of 2019, up from 59 percent in 2010 (figure 13). This trend holds across all ethno-religious groups. For each of the public services featured in the survey, women are significantly less likely than men to report satisfaction, and the poor are significantly less likely to be satisfied than the rich. Some of the largest income and gender gaps are reported for health care and quality of air.

> 3 Gallup World Poll data are only available for 13 of the 19 regions. Halabja was pooled with data from Sulaymaniya.

15
financial penalty if they fail to generate a minimum return relative to a peer- and market-based benchmark. Hence, it is not surprising that the portfolios of these funds replicate to a large extent the market index. Figure 3 shows the monthly time series of the share of AUM by investor type. The figure displays the upward trend of passively managed funds by domestic institutions and the sharp rise of foreign passive funds after 2008.<sup>11</sup> Given the unique nature of our transaction database with investor ID, we document how passive and active investors trade during the day. Table 4 summarizes the intra-day profile of traded value when we divide the day into eight time intervals.<sup>12</sup> Panel A in the table reports the proportion of the traded value by each group relative to the total traded value in the interval. Excluding the closing batch, domestic institutions account for more than half of the value traded during each period, with foreign investors increasing their relative importance in the afternoons. In the closing batch, foreign investors represent 39.9 percent of the interval, with passive funds accounting for most of those trades (30.9 percent of the interval). Panel B in the table reports the proportion of traded value in each interval relative to the total daily value traded by investor group. Domestic institutions and individuals execute most of their trades in the early hours of the day. On the contrary, passive foreign funds trade on average 39.8 percent of their daily traded value in the closing batch. While this behavior is more pronounced for the latter years in the sample, passively-managed foreign funds were consistently very active in the last five minutes of the trading session since the closing batch auction was introduced in 2009 (Table 4-Panel C).

Trading at or near closing prices might be an efficient strategy for open-end funds. For example, mutual funds are “forwarded priced,” which means that while investors can place orders to buy or sell shares throughout the day,
Figure 4: Extensive margin of foreign competition (N countries) faced by French firms <!-- Start of picture text --> 1999 2000 2001 1999 2000 2001<br>2002 2003 2004 2002 2003 2004<br>.5<br>.4<br>.4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>.5<br>.4<br>.4<br>.3 .3<br>Herfindhal index China (N° countries) .2 .2<br>.1 Herfindhal index Other Asian (N° countries) .1<br>0 0<br><!-- End of picture text --> Note: authors calculations based on BACI dataset and French customs dataset for 1999-2005.

China. To evaluate the net effect of Chinese unilateral liberalization on French firms’ export patterns, we should take into account explicitly the role of foreign competition in the liberalized market.

# _III. EMPIRICAL STRATEGY_

# _Data description_

We use individual export data on manufacturing goods for France, collected by French Customs.<sup>12</sup> These data contain the value of exports by product, firm and destination over the time period 1999-2005. This database classifies export flows at the firm level within 8 digit product categories. Our analysis is restrict to manufactured products.

We use MFN applied tariffs at the HS6 product level collected from TRAINS.<sup>13</sup> From this database, we consider the tariffs applied over the period 1999 to 2005 by our set of Asian countries, i.e. China, India, Indonesia, Korea, Japan, Pakistan, Philippines, Singapore and Thailand. We exclude Hong Kong and Taiwan from our analysis since they are financial and trade centers, where the wholesale activity is very important. The next section explains how we construct the firm level tariff. We also control
preferred method of plot-level soil testing, there is evidence that the geospatial data fails to adequately distinguish soil suitability levels. AFSIS-based classification results in more intense clustering of observations in the central, moderately suitable class (88 percent), with only 7 percent classified as highly suitable and 6 percent as marginally suitable. While 81 percent of observations are mapped to the same suitability class regardless of whether MAPS or AFSIS soil data informs the classification, the suitability class of 19 percent of plots varies with the source of soil data utilized (see Figure 2), which will have economic implications for the perceived production frontiers for these households.

||||**MAPS**||
|---|---|---|---|---|
|||S1|S2|S3|
|**IS**|S1|**34**|23|3|
|**AFS**|S2|72|**607**|57|
||S3|0|4|**43**|

_Figure 2._ Suitability Classification Matrix; MAPS- and AFSIS-based classifications.

A unique feature of using this method is the ability to identify specific constraints to suitability for each class. According to MAPS-based suitability classifications, cation exchange capacity is an overarching limiting factor, with 43 percent of plots being classified as _not suitable_ for that particular property. The perceived constraints differ when AFSIS-based classifications are made. For example, AFSIS-based classifications suggest that only 3 percent of plots have _not suitable_ levels of cation exchange capacity (compared to 43 percent when using MAPS-based data). Annex Table A1 identifies the limiting factors for each MAPS-based suitability class separately, enabling an assessment of what interventions would be most effective in increasing the suitability of plots from one level to the next.

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30.2|
|Rural|28.0|28.0|28.0|27.5|
|||Poverty headcount ratio (%)|||
|Cambodia|17.7|17.8|19.8|n.a.|
|Phnom Penh|4.1|4.2|5.0|n.a.|
|Other Urban|12.4|12.6|14.3|n.a.|
|Rural|22.6|22.8|25.1|n.a.|

Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

# **Fiscal Impoverishment and Fiscal Gains to the Poor**

Comparisons of poverty before and after taxes and transfers are applied only broadly indicates the gains or losses the fiscal system creates. But this can fail to capture an important aspect: that some poor are made poorer (or non-poor are made poor) by the tax and transfer system. Fiscal Impoverishment (FI) traces the number of people who are impoverished following the execution of the fiscal policy.

20
# **Table 8: Effect of Commodity Price Shocks on Tax Revenues**

|**Model**|**Country**|**and Year Fixed Effec**|**ts**|
|---|---|---|---|
|**Outcome (over GDP)**|**Tax revenues including**|**social contributions e**<br>**revenues**|**xcluding resource**|
||**(1)**|**(2)**|**(3)**|
|Log difference of commodity Net Export Price Index (historic rolling<br>weights)|-5.220***|||
||(1.450)|||
|Log Difference of Hydrocarbons Commodity Index||-0.345*||
|||(0.194)||
|Log Difference of Metals and Minerals Commodity Index||-0.500||
|||(0.321)||
|Log Difference of Agriculture, Food, and Beverages Commodity Index||-0.748*||
|||(0.442)||
|Log Difference of Crude oil Commodity Price Index|||-0.255|
||||(0.177)|
|Log Difference of Coal and Natural Gas Commodity Index|||-6.160**|
||||(2.411)|
|Log Difference of Base Metals Commodity Index|||-0.851***|
||||(0.307)|
|Log Difference of Agricultural Raw Materials Commodity Index|||-0.440|
||||(2.742)|
|Log Difference of Food Commodity Index|||-0.805*|
||||
in every country. The combined effects, that is, the lower intergenerational correlation and the rise in the dispersion of parental education, explain the slight increase in intergenerational correlation. These patterns are similar to those reported by Hertz et al. (2007) and Azam and Bhatt (2015).

Figure 3: Evolution of intergenerational educational persistence and standard deviations education by 5 years birth cohorts <!-- Start of picture text --> a. Comoros-2004 c. Ghana-2013 d. Guinea-2003<br>1939 1949 1959 1969 1979 1948 1958 1968 1978 1988 1937 1947 1957 1967 1977<br>5-years birth cohorts 5-years birth cohorts 5-years birth cohorts<br>e. Madagascar-2005 f. Malawi-2010 g. Nigeria-2010<br>1940 1950 1960 1970 1980 1946 1956 1966 1976 1986 1946 1956 1966 1976 1986<br>5-years birth cohorts 5-years birth cohorts 5-years birth cohorts<br>h. Rwanda -2000 i. Tanzania-2009 j. Uganda-2005<br>1935 1945 1955 1965 1975 1945 1955 1965 1975 1985 1941 1951 1961 1971 1981<br>5-years birth cohorts 5-years birth cohorts 5-years birth cohorts<br>Coefficient Correlation Parents SD-years of schooling Children SD-years of schooling<br>8 8 8<br>6 6 6<br>4 4 4<br>2 2 2<br>0 0 0<br>8 8 8<br>6 6 6<br>4 4 4<br>2 2 2<br>0 0 0<br>8 8 8<br>6 6 6<br>4 4 4<br>2 2 2<br>0 0 0<br><!-- End of picture text --> The general declining trend of intergenerational elasticity ( _β_<sup>ˆ</sup> ) across cohorts partly reflects the improvement in the education systems and policies in many countries in the region. The education systems in Sub-Saharan Africa expanded substantially after independence in the 1960s, which almost doubled primary-school enrollments in many
is to identify minimum wage workers in the tax administration data.** We identify minimum wage earners based on the value of the gross wage. As discussed above, in January 2020, three different minimum wage levels existed in Romania. We can directly identify construction sector employees entitled to the highest minimum wage (3,000 RON in 2020) because they are also entitled to special fiscal facilities and thus identified in our data by a special variable. The biggest shortcoming of our dataset is the need for more information on the education level to identify those with higher education. Therefore, we cannot distinguish between taxpayers entitled to a general minimum wage (2,230 RON in 2020) and employees with higher education entitled to a higher minimum wage (2,350 RON in 2020).

We categorize individuals as minimum wage earners in the sample according to the following criteria:

- If they are employed in the construction sector and their earnings fall within the range of 95% to 105% of the established minimum wage for construction sector employees (3,000 RON).

- If they are not employed within the construction sector, their earnings fall within 95% of the general minimum wage (2,230 RON) to 105% of the minimum wage applicable to higher-education employees (2,350 RON). Using the 105% threshold aligns with standard practices in minimum wage literature and

23
single cross section from Tanzania. Our goal is to unpack the result in this paper regarding how little weather matters once one controls for time-invariant household unobservables. While panel data is becoming more common in developing countries, many researchers still rely on cross-sectional data. We are interested in understanding the degree to which results using cross-sectional data may be biased because of the inability to control for household unobservables. If weather matters in cross-sectional contexts but no longer matters once time-invariant household unobservables are controlled for, it indicates that weather may fail the exclusion restrict necessary for its use as an instrumental variable.

Our third research question concerns the extent to which correctly specifying the growing season matters for the use of weather data. The growing seasons differ in northern and southern Nigeria, as do the growing seasons in different regions of Uganda. To account for these differences, we specify different seasons for households in these countries. But, even in a country like Malawi, with a single unimodal growing season, the start and end date of that season can vary depending on where a household is located in the country. Differences in Tanzania can be even more pronounced and our failure to account for this variation may explain why weather appears to matter so little in that country compared to the other five countries. The issue of defining the growing season is another example of potential measurement error in the use of remote sensing weather data. We plan to explore methods for defining the growing season at the household-level based on self-reported planting dates in the LSMS-ISA data along with using remote sensing data to determine the onset of rainfall. If the definition of growing season does matter, it suggest that researchers need to take much more care in defining what days or months to take weather data from.

Our final research question is whether machine learning methods can be effectively used to select weather metrics and remote sensing sources. Our approach has relied on traditional regression techniques to estimate a large contingent of regressions and then determine which metrics matter over a large set of results. However, these research
data, there are also issues with sectors needing to be surveyed in certain years, typographical errors and outliers, as well as consistency in variable definitions (Marquez-Ramos, 2020). More

> 4 In Indonesia, firm size is determined in two ways. The first approach, used by Statistics Indonesia, is based on the number of employees. Under this definition, microenterprises are those with 1 to 4 employees; small enterprises with 5 to 19 employees; medium enterprises with 20 to 99 employees; and large enterprises with 100 or more employees. The second definition is based on the National Law 20/2008 (Art. 6), and distinguishes between micro, small, and medium firms based on their net assets or annual revenues.

> 5 The seminal 2009 IMK Survey did not use a nationally representative sample of enterprises. The first nationally representative IMK survey was conducted the following year in 2010 (Rothenburg et al., 2016).

> 6 For this reason, the Manufacturing Survey functions as a census, although the term is not used to avoid confusion with the Economic Census (Marquez-Ramos, 2020).

6
Brown C., M. Ravallion and D. van de Walle (Forthcoming), “Most of Africa’s NutritionallyDeprived Women and Children are Not Found in Poor Households”, _The Review of Economics and Statistics_ , https://doi.org/10.1162/rest_a_00800 Case A., C. Paxson and J. Ableidinger (2004), “Orphans in Africa: Parental death, poverty, and school enrollment", _Demography_ 41.3, pp. 483-508.

Chesher A. and C. Schluter (2002), “Welfare Measurement and Measurement Error”, _Review of Economic Studies,_ vol. 69, April 2002, pp 357-378 Chiappori, P.-A., (1988). "Rational Household Labor Supply," _Econometrica_ , Econometric Society, vol. 56(1), 63-90, January Cogneau D. _et al._ (2006), « Inégalités et équité en Afrique », DIAL WP 2006-11.

Datt, G. and M. Ravallion (1992), “Growth and redistribution components of changes in poverty measures: A decomposition with applications to Brazil ans India in the 1980s”, _Journal of Development Economics,_ vol. 38(2), April 1992, pp 275-295.

Deaton, A., (1997), _The analysis of household surveys : a microeconometric approach to development policy_ . Washington, D.C. : The World Bank.

Deaton, A. (2005). "Measuring Poverty in a Growing World (or Measuring Growth in a Poor <u>World),"</u> _The Review of Economics and Statistics_ , MIT Press, vol. 87(1), pages 1-19, 04 Deaton, A. and M. Grosh (2000), “Consumption”, in _Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 Years of the Living Standards Measurement Study_ , edited by Grosh, M. and P. Glewwe, World Bank, Washington, D.C.

Dercon
Susenas 2017 data indicate that 12.3 percent of out-of-school girls dropped out because of marriage, while the percentage of out-of-school boys who dropped out is very small, at less than 1 percent (see Table 3). In poorer areas, girls are more likely to marry early and will often drop out of school even though this is not a legal requirement. In fact, married girls are entitled to continue to attend school, but this entitlement is sometimes not understood by school directors and teachers and, even if it is understood, the child may withdraw due to social norms.

Early marriage and school attainment are closely correlated, particularly for girls, as many drop out of school if they get married. Analysis from UNICEF found that, in 2015, girls who married before the age of 18 were six times less likely to complete senior secondary school than girls who married after that age. The analysis showed that of ever-married women aged 20-24, only 8.9 percent of those who married before age 18 completed senior secondary school, while 40.1 percent of those who married before age 18 had primary school as the highest level of education completed. In addition, girls with more education are less likely to enter into early marriage.

Contradictory laws on child marriage can create confusion at the local level. It was discovered on field visits that these laws are not well understood. According to the 1974 Marriage Law, under the age of 21, parental consent is required for marriage, with minimum ages stipulated as 16 for females and 19 for males, while the 2002 Child Protection Law states that the minimum age for marriage for both genders is 18. Susenas 2018 data show that these regulations have not been fully enforced, as 1.41 percent of girls aged 10-17 were married at the time of the survey. These rates are much higher than boys who have married in the similar age group (0.10 percent). Compared with this national figure, the early marriage rates for girls are higher in the
# **5 Conclusion**

This paper analyzed the level of disruptiveness of digital businesses in Africa along their life cycle and its association with funding. Using data from Crunchbase and through textual analysis, we identified which firms incorporate into their products and services technologies considered disruptive according to Bloom et al. (2021) classification, based on earning conference calls and patents information. To produce meaningful comparisons, we collected data on Africa, Latin America, and a group of cities at the technological frontier like Seattle, Palo Alto, London, and Tokyo.

Our results indicate that African digital firms incorporate a lower level of disruptive technologies into their offering than the frontier. Still, there is a great deal of sectoral heterogeneity, especially when we take into account mobile payments, particularly in the case of Fintech, where Africa is significantly closer to the frontier, particularly on the extensive margin (probability of incorporating at least one of these technologies). The region is significantly below the frontier on the intensive margin (number of different technologies incorporated) in all the subsectors analyzed.

Although startups in Africa tend to be more disruptive than older firms, the level is much lower than the frontier. Still, we observe a great level of heterogeneity in the lifecycle of disruptiveness by sector, with Fintech showing a different pattern, particularly for Kenya, South Africa, and Nigeria, which exhibit more disruption from older firms in this sector. These results are consistent with different characteristics of the sector, where the level of trust, technology infrastructure, and the possibility of bundling different products, along with potential regulatory frictions, can be critical factors.

We also find that, although disruptiveness positively correlates with funding, this relationship is weaker compared to the frontier. In addition, we find that even though being disruptive helps attract more funding compared to not being disruptive, this is preeminently occurring among older firms and mostly on the extensive margin of funding. These results are consistent with frictions affecting both the life cycle and the allocation of financing in the