rafmacalaba/data-use-mentions-extended huggingface dataset:
therefore provides us further insights on which specific part of innovation that firms perform data restrictions have an impact.

We start with the cross‐country approach. Equation (1) is used in our baseline regression which is specified in equation (2) below. Equation (2) measures the correlation over time between the data linkage index as described above and several variables of innovation (see below) measured at the level of the firm. Hence, we regress our variables of firm‐level innovation that is recorded for each firm _f_ ̧ for country _c,_ in sector _j,_ at year _t,_ on the data linkage (DL) index which itself is specified at country‐sector‐year level. As a result, the baseline specification for our regressions as correlations takes the following form: (2) In equation (2), the vector INNO consists of four firm‐level innovation variables across our selected group of East Asian countries. These variables are: (1) whether the firm has introduced a new product / services over the last 3 years; (2) whether the firm has introduced a new process over the last 3 years; (3) whether the firm uses technology that is licensed from a foreign company; and finally (4) whether the firm has spent on new R&D (excl. market research) in the last 3 years. The four variables are respectively indicated by h1, h5, e6 and h8, which is consistent with the labeling of the World Bank Enterprise Survey database from where the data are sourced. Note that these firm‐ level data are cross‐sections for each year between 2009 and 2018 with intervals and as such do not record data for the same firm each year. Tables A1, A2 and A3 and Figure A1 in Annex A provide an overview of the cumulative firm distribution of the four innovation variables and gives summary statistics by country and sector.

Note that our dependent variables are formulated for which responses are only allowed in a
Insuranc- Institute (NII), for each of the yea:. 1969-1976. The NIX was asked *o supply names and earning& data for each individual in the sample, on the basis of identity number. The *arnings records maintained by the Institute are based on employee earnings reported by employers for purposes of national insurance payments. The earnings data made available to us by the NII gave info_.mati **on** the total annual earnings for each individual, accumulated across employers, **as** well **as the** number of months in the **year** for which earnings **were** reported. Since the NII records **are** recorded only by the last **six** digits of identity numbers, multiple earnings records **were** provided for each identity number requested. The matching of the Individual trainee data with appropriate earnings data on the basis of **a** common name proved to be no easy task. Name changes, death, war casualty and migration were among the factors militating against any straightforward matching of the training data with the NII earnings records. In all, 1233 matchings were achieved - **some** 72 percent of the original male trainee sample (Table 1).

Table 1 Lenath of Course. Four Training Modes

|Length of<br>Course|Vocational<br>Secondary<br>School|Apprenticeship<br>|Industrial<br>School|Short<br>Teenage<br>Course|All<br>r Modes|
|---|---|---|---|---|---|
|1 year or less|<br>-|...-<br>5|----------------------|-----------
.8|
|North|1,503|949|306|248|3.114|24.4|6.6|36.4|78.0|
|South|4,828|3,994|523|311|6.215|15.2|7.4|35.7|80.1|
|West|4,135|3,344|435|356|4.827|13.8|4.5|34.5|75.4|
|**Panel B: By or**|**ganizational**|**form**||||||||
|Individual|13,340|12,270|845|225|9.225|8.2|5.5|30.7|66.3|
|FOP|2,519|1,185|695|639|7.279|34.1|9.8|35.1|78.1|
|Legalentity|3,801|858|1,413|1,530|15.976|49.6|15.9|37.9|79.3|

_Source:_ Own computation based on PSG records.

Note: The total number of approved and paid applicants under the land program were 21,050 farms. Data reported in this table is only for 19,660, i.e., the number of PSG beneficiary farms used in the regression analysis after data cleaning.

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labor and capital income are not necessarily subject to the same tax rate. Starting in the 1990s, a number of economies have adopted dual-income tax systems, whereby labor and capital incomes are subject to different schedules, with labor income typically subject to a progressive tax schedules and (some) capital income subject to flat tax rates.

To account for these facts, we proceed as follows. We start with the baseline assumption, consistent with US data, that 15% of PIT revenues derive from capital and 85% from labor. We then use data from Jensen (2022), which documents the location of the PIT threshold across countries, to adjust for the share of capital income in the PIT at the country-year level. Since richer taxpayers derive a larger share of their income from capital, countries with a high PIT exemption threshold—and thus more exempted taxpayers—have a larger capital share of PIT revenue. Finally, we use data from the OECD to account for dual income tax systems. Specifically, when dividends face a lower tax rate than ordinary income, we compute the ratio of the statutory rate on dividends to that of the top rate on labor income, and adjust the capital share of PIT revenue down when this ratio is below one. Appendix B.2 discusses these adjustments in further detail.

The resulting share of PIT revenue allocated to capital varies between 7% and 35%, depending on countries and years. Over time, this share falls from a global average of 19% in 1965 to 14% in 2018, due to both a reduction in PIT exemption thresholds and to the adoption of lower tax rates on dividends in some countries.

**Effective tax rates.** The total tax revenue assigned to labor and capital is:

where _λic_ is the allocation to labor of each type of tax _τi_ in country _c_ (see Table A1).

The effective tax rates (ETR) on labor and on capital, _ETRL_ and _ETRK_ , are computed by dividing tax revenue collected by the size
# **1 Introduction**

Digital technologies have been seen as a potential driver of leapfrogging in Africa. The African digital start-up ecosystem, though still nascent, is among the fastest-growing in the world. It is the only region where the median deal size did not decline during 2022 (CB Insights, 2023). Although framed as a footloose sector, digital business providers are widely perceived as an economic activity with the potential and opportunity to bring growth to deprived or lagged regions and enabling technological catching up (Friederici et al., 2017, 2020). The remarkable speed of mobile payment adoption by individuals and firms in Africa are practical examples of the potential implications of disruptive technologies. This process has been driven by leading digital tech platforms and firms, such as M-pesa and Wave, by incorporating innovative and disruptive technological solutions into their offerings, allowing them to disrupt markets through better and cheaper solutions for customers. However, apart from a few well-known successful cases, there is little evidence available on how digital startups in Africa have been incorporating disruptive technologies into their offerings, a key condition to play an enabling role for digital disruptiveness in the continent, or on the role of finance in this process.

This paper analyzes the extent to which digital tech firms in Africa incorporate disruptive technologies and how this relates to access to finance along the firm life cycle. First, it compares the level of disruptive technologies incorporated by digital businesses in Africa with the Latin American market,<sup>1</sup> and a global frontier from which a substantial proportion of digital technologies have emerged, constructed using business data for cities such as Seattle, Palo Alto, Tokyo, and London. Second, the paper documents the variation in the incorporation of disruptive technologies into their offerings along the life cycle of digital tech firms in Africa. Following the literature on firm dynamics and entrepreneurship, which suggests that young firms (startups) are more innovative (Decker et al., 2014), the paper compares the differences in the incorporation
Figure B9: Distributions of poor populations across urban versus rural areas by country, $2.15 poverty line <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, GHSPOP 1km is used.

Figure B10: Distributions of poor populations across urban versus rural areas by country, $3.65 poverty line <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, GHSPOP 1km is used.

51
shrinks. The number of recall ALT hours is 3.7 times higher than the number among the weekly visit group at the person-plot level, but is only 1.9 and 2.5 times higher at the person and plot levels, respectively.

In looking at total time from the household perspective (in hours, days, weeks), we see too few statistically significant or economically meaningful differences between the four survey designs. It appears that three wrongs make a right: the competing manifestations of recall bias (that is, the misreporting by recall households of days or weeks, plots, and workers) offset each other in average household labor. While the reliability of the labor data generated by recall surveys may be sufficient for household labor supply measures, it may be problematic for other applications, such as plot productivity analysis or the calculation of labor force participation in agriculture.

# _4.2. Irregularity in work patterns_

The dramatic difference between the recall and weekly surveys in the number of hours worked, as well as in the people and plots active in agriculture, is clear, worrisome evidence that recall bias exists in both the intensive and extensive margins of end-of-season labor reporting. What drives these large and systematic gaps? In our context, the competing forms of recall bias, the overreporting of hours per person-plot, and the underreporting of the people and plots active in agriculture nearly cancel each other out in the aggregate. These manifestations of recall bias can be explained by the same phenomenon: the cognitive burdens of irregular work patterns. We discuss in turn the mechanisms by which irregularity drives each of these results.

## **The reporting of the amount of time worked**

If forgetting were the chief mechanism by which recall bias becomes manifest in the person-plot labor data, one might expect weekly interviews to yield higher season-total estimates than end-ofseason interviews. As the direction of the bias runs counter to this explanation, forgetting does not drive our results. Instead, we propose that the hours discrepancy arises from reliance on
<!-- Start of picture text --> wt aR abr eI" 72" IBRD 33595<br>‘ ~, x SEPTEMBER 2004<br>yZs :me”~1 EN, : **<br>2 A ro a =a) ( 4 b Toate HOUSEHOLD SURVEY REGIONS<br>a f Thokugacn ‘ -ne i t : Fy<br>é 2 Nibtamer Me clit Noe e o DISTRICT CAPITALS<br>— ; ik dle a @ DINISION CAPITALS<br>mG - Rangpur eee oe : @ NATIONAL CAPITAL<br>+ DIVISION BOUNDARIES<br>\ieonanTlYi, I : Gaibandiband h : a,, Rel; :aMe/ . ---+-——- INTERNATIONAL BOUNDARIES<br>* ae ae .<br>ed 7} ee TT<br>25); { sJoypurhat H 1 5t, peid , Sunanganj: . ey ~ "<br>% T aA "ee oSerpur © ‘. V 25<br>+) ane _Naogaan Y v _ hoesmt aNetrokona. ae ‘, zs co SS<br>E d J Ney ofeae aalt- , ¢ Myrmansingh neSe' “ty Sylhet. : }<br>{Nowtgan) ; Kaloo ;<br>oI Saraiggnj ) ° re sear vl<br>PBN Rajshahi‘- > Naor a *eweHabiganja 25° d<br>asia | a . Tangail Ai “aeoiitewad .NS7 }<br>as ‘ fi : a] % } ‘ a. 3<br>( Pabna : “= (Gazipur i bene INDIA Mt<br>= :<br>- ; uihtiae Sy a, 3 niko & t F eo va ar<br>_Maherpur 4 «SEE of<br>( reChuodanga Sg‘atCr 2r Fanta s o." safhSLA& 4AF yf=, Fal= K ow4 ’<br>y ghenaicah oe Farid pur 4 aie \ \ \<br>r°
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Because of the poor condition of its road network and delays in the port of Douala, Cameroon’s ability to move goods and connect manufacturers and consumers with international markets is one of the lowest in the world. Feedback on the logistics ―friendliness‖ of countries is reflected in the Logistics Performance Index (LPI). The Logistics Performance Index is based on a worldwide survey of global freight forwarders and express carriers, who provide feedback on the logistics ―friendliness‖ of the countries in which they operate. In 2010, Cameroon’s LPI, at 2.55, ranked 105 out of the 155 countries assessed by the LPI. Even so, it was still above the Sub-Saharan Africa average(2.42)and the highest among Central African countries (figure 5).

Zooming in on the components of the LPI, in the case of Cameroon the quality of trade- and transport-related infrastructure (ports, railroads, roads, information technology) and the efficiency of clearance (speed, simplicity, and predictability of formalities) by border control agencies, are the dimensions that received the lowest scores by operators in the country.

Transporting goods from port to final destination across Central Africa is even more costly than in other regions of SubSaharan Africa, which are already costly compared to the rest of the developing world. Moving freight along intraregional

**Figure 5. The Logistics Performance Index inCentral African countries**

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Source : World Bank 2010a.<br>Logistics Performance Index<br>Chad Rep. Gabon Rwanda SSA<br>Cameroon Congo, Dem. Congo, Rep.<br><!-- End of picture text --> corridors in the Central African region<sup>2</sup> costs twice
- 3 -

particularly difficult to address in the transport sector because there are few available time series data in the sector.

4. The current paper’s contribution to the literature is at least twofold: First, the paper applies a dynamic panel regression model to obtain an unbiased estimate of the transport connectivity impact on firm agglomeration. In recent years, the instrumental variable (IV) method has been used to deal with the endogeneity issue (e.g., Datta, 2012; Jedwab and Maradi, 2012; Donaldson, 2018; Banerjee et al., 2020). It is shown that quasi-random (or quasi-exogenous) factors, such as geographic conditions, historical events and “unintended” benefits from the network nature of infrastructure, can be good instruments because they are less relevant to the underlying economic performance but may have influenced infrastructure development in subsequent years. Notably, however, the selection of instruments is still dependent on the judgement of researchers and subject to post statistical tests. The current paper applies a dynamic panel data regression model where valid instruments can be explored more systematically.

5. Second, the current paper develops a way of generating detailed spatial data to measure transport connectivity based on micro shipping data. A challenge to apply the above-mentioned dynamic panel data approach is that detailed panel data are needed for a relatively long period of time. A practical norm of the length of time, _T_ , may be five to ten rounds (Labra and Torrecillas, 2018). It means that it takes 5-10 years if annual data are used. This is significantly costly, although some earlier studies collected a series of household surveys. For instance, Khandker et al. (2009) use panel data collected over a period of 7 years. Dercon et al. (2009) analyze five rounds of household surveys carried out for 10 years. However, this approach is costly and often impossible. In the transport sector, there are few data that are regularly collected.

6. In the literature, in addition,
crisis. As a consequence, the unemployment rate was only 5.8 percent<sup>3</sup> at the peak of the crisis in 2016, and it declined to 5.6 percent in 2017.

_Figure 3: Employment dynamics of the working age and above working age population_ <!-- Start of picture text --> 100 thsd pers. 78.0 % 19.0<br>77.5 18.5<br>77.0 18.0<br>0<br>76.5 17.5<br>76.0 17.0<br>75.5 16.5<br>-100<br>75.0 16.0<br>74.5 15.5<br>74.0 15.0<br>-200<br>73.5 14.5<br>2011 2012 2013 2014 2015 2016 2017<br>73.0 14.0<br>change in employment, working age population<br>2010 2011 2012 2013 2014 2015 2016 2017<br>change in employment, above working age population<br>change in the working-age population (employable) working-age population (employable)<br>total change in employment population above working age (right axis)<br>(a) Change in employment, thsd pers. (b) employment rate, %<br><!-- End of picture text --> 3 Unemployment estimates from the labor force survey according to ILO methodology are only available since 2016 in Belarus, and as such we are not able to compare with the pre‐crisis levels.

4
identical across the consumption experiment as well as the TZNPS 020/21 and 2019/20 rounds. The sample sizes were 4,644 in 2020/21 (following up with a panel sample that was first interviewed during the 2014/15 round) and 1,179 households in 2019/20 (following up with a subset of an older panel sample that had been interviewed as part of the TZNPS 2008/09, 2010/11 and 2012/13). As discussed above, there are differences in terms of food and non-food consumption modules that were introduced in TA 2 and TA 3 to understand the potential for using lighter version of these modules to obtain accurate poverty predictions.

Finally, in TA 1 and the TZNPS 2020/21 and 2019/20 rounds, the total consumption is taken to be the sum of food (consumed at and away from home) and non-food consumption (health, education, utilities, furnishing and household expenses, transport, communication, retreats, and other). We provide more detailed discussion on the food and non-food consumption expenditure aggregates for the TZNPSs and the three TAs in Appendix B.

# **2.2. Descriptive statistics**

We spatially and inter-temporally deflate all the consumption aggregates in the three TAs and the TZNPSs. The spatial and temporal price differences in nominal household consumption expenditures within all survey rounds are corrected using Fisher price indices. These price indices are estimated within each survey round by stratum and quarter (or month, in the case of the experiment), and the base period in each estimation comprises the entire period of each round.

The across-survey intertemporal deflation is in turn conducted using the annual inflation series for various consumption groups, as obtained from the World Bank Global Database of Inflation (Ha _et al._ 2023).<sup>7</sup> Specifically, food expenditure is deflated using the consumer price inflation for

> 7 To access the database, visit: https://www.worldbank.org/en/
**Figure 6: Actual and simulated annual poverty changes, ca. 1995-2008**

<!-- Start of picture text --> GMB<br>TJK<br>MDV<br>KAZPOL RUS<br>MLIMLICAFCAFBDIBDIMWIMWIMOZMOZNERNER TZA SWZSWZMDGNPLNPLNGANGAUGAUGAVNMVNMGNBGNBBGDBGDINDGMBTJKETHETHZMBPAKPAK IND LAOLAOSENSENIDNIDN CHNCHNGHAGHAKHMKHM MRTMRTMNGMNGCMRCMRCIVCIVLKALKAPHLPHLAZEAZEMDAMDAEGYEGYROMROMARMARMNICNIC HNDJAM MARMARNAMNAMJORJORALBALBTUNTUNZAFZAFKGZKGZVENVENPERGTMGTMMKDMEXMEXSLVSLVECUECU PER TURTURMDVUKRUKRBOLBOL DOMDOMMYSMYSCRICRIPANPANBGRBGRPRYPRYRUSBRABRACOLCOL ARGARGCHLCHL<br>MDG ZMB KEN<br>KEN MKD<br>1 5 10 15<br>Average daily income per capita, 1995<br>Actual Equal initial conditions<br>15<br>10<br>5<br>0<br>Annual percentage change in poverty<br>-5<br><!-- End of picture text --> Figure 7 shows that the reversal of the relationship, and the large magnitude of such reversal, extends to other dimensions of poverty, such as the poverty gap. Again, had Mali, Nepal, Niger and Tajikistan had the same initial per capita income as Jamaica in 1995, they would have literally closed the poverty gap by now. In Chile, on the other hand, due to its high income inequality, a significant amount of the population would still remain well below the poverty line (i.e., a poverty gap of 0.14).

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younger and older respondents in the United States. Their findings suggest that age is not a strong predictor of environmental concern or climate change attitudes. Instead, other factors, such as political ideology and education, play more significant roles in shaping these attitudes. <u>Tranter and Booth (2015)</u> find that age poorly predicts climate change skepticism in 14 advanced industrial nations in 2010−2011. Using the World Values Survey (2010–14), which covers 51 countries, <u>Echevarria</u> (2017) finds that age is positively associated with environmental concerns.

Better-educated individuals are usually more concerned with climate change than people with less education (Poortinga et al. 2011, <u>Milfont et al. 2015, Echevarren 2017, Poortinga et al. 2019). The</u> level of education has increased considerably over the past century. In both advanced and developing countries, younger cohorts tend to be more educated than older cohorts, which may mediate the relationship between age and climate change beliefs.

Political conservatism is associated with climate change skepticism (McCright and Dunlap 2011, <u>Whitmarsh 2011, Milfont 2015), but conservative beliefs tend to be negatively associated with</u> educational attainment (Pew Research Center, 2016). <u>Poortinga et al. (2019)</u> show that individuals on the right side of the political spectrum have lower levels of education, are more likely to prioritize self-enhancement over self-transcendence, and are more likely to be skeptical of climate change or its anthropocentric links than others.<sup>4</sup> Conservatism is more prevalent among older individuals (Cornelis et al. 2009; Whitmarsh 2011).

In a meta-analysis of climate change beliefs across 25 polls and 171 academic studies spanning 56 countries, <u>Hornsey et al. (2016)</u> find ideology, world views, and political orientation to be among the strongest predictors of climate beliefs. Free-market
<!-- Start of picture text --> Trust People In Community G<br>Plan to Vote 2022 G<br>Member of Association G<br>Control Over Life G<br>Contribute to Events in Community G<br>Ability to Change Life G<br>−0.50 −0.25 0.00 0.25 0.50<br>Estimate<br>Displacement G Camp<br>Iraq<br>Variable<br><!-- End of picture text --> Figure 7: ATT of Being in a Camp Based on Match Sample in Iraq (Brookings 2010).

In this paper, we use a multi-country approach to understand the seventh criteria for durable solutions in a context of forced displacement: the potential for social and political participation of those forcibly displaced. We collect and analyze survey data in three situations of internal displacement to examine the conditions under which IDPs become socially or politically empowered by examine factors associated with such engagement, in particular with regards to displacement settings, timing, and broader context.

We find that, perhaps against expectations, displacement experiences are associated with perceptions of empowerment and engagement with local communities and local governance institutions, but this is not consistent across place and time. Generally, our findings challenge simple assumptions of post-traumatic growth leading to enhance pro-social behaviors among IDPs in a forced displacement contexts. Across countries, we find that different manifestations of participa24