rafmacalaba/datause-encoder-data huggingface dataset:
# **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 African market. Additional results indicate that being disruptive is associated with earlier financing but with lower tickets and higher growth in valuation and future funding deal size. Once again, these results point to some market imperfections (e.g., information failures) related to the assessment of risk among these potentially disruptive and high-growth firms, which limits their funding.

Our analyses can inform the intervention of multilateral development institutions in terms of their role in creating markets, contributing to de-risking some of these investments, and, therefore, promoting growth in underdeveloped markets.

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observations lie within this range. The very large fraction of empirical observations near zero has important implications for the limited-dependent data-generating process of interest to us. For any given distribution of errors, the presence of a large share of the observations of the driving variable near zero makes it much more likely that the outcome will be transformed into a zero observation.

We next use a continuous explanatory variable with this distribution and the simplest model previously examined—the ET Tobit model—and repeat our Monte Carlo analysis. When we do this, we find that the absolute value of the _k_ parameter needed to obtain any given share of zeros falls precipitously. Specifically, with a tiny value for k of 0.01, the percentages of zero observations in heteroscedasticity cases 1, 2, 3 and 4 are 71 percent, 64 percent, 53 percent and 64 percent, respectively. The 53 percent share with heteroscedasticity of type 3 is almost exactly the share of zeros observed in the SST data for total trade between 136 countries. It is much higher than the fraction of zeros reported in Table 1 using k values of 1.0 and 1.5 with the log standard normal distribution. Moving to slightly larger values of _k_ would allow us to replicate the shares of zeros observed in studies using disaggregated trade data.

More importantly, we obtain results, shown in Table 11, that have features that are consistent with our empirical findings. The estimated values on the continuous variable are very similar for both the PPML and the truncated PPML models. And the ZIP and the truncated PPML models are identical. Another interesting feature of these results is that the Negative Binomial model—which performed so poorly in our earlier analysis appears to perform very well, although it remains subject to the concerns expressed by SST (2006) about its parameter estimates depending upon the units of measurement.

# **VIII. Conclusions**

The purpose of this paper was to evaluate the performance of different estimators of the gravity equation in the presence of zero trade flows, which are a common feature of trade data both at the aggregate and disaggregate level. We adopt two different data-generating processes in our Monte Carlo simulations— the Tobit and the Heckman sample-selection models. For the Tobit DGP we used Monte Carlo simulations based on the design of Santos Silva and Tenreyro (SST 2006), modified to include a threshold level of trade that must be surmounted before positive trade levels are observed. For the Heckman sample selection 26
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make some additional resources available for smaller areas (say to create and maintain a critical minimum level of public education and health services or to compensate for the dispersion of population), it would be better to do so on the basis of estimated costs of compensating for smallness. The differential gains and losses for various jurisdictions cited above is not related to wealth or poverty in any systematic way and cannot be justified on any rational basis.<sup>15</sup> 28. A second flaw in the application of the formula is the continued use of the 1964 census for the population-based part of the formula. This would favor those jurisdictions whose populations have grown relatively slowly at the expense of rapidly growing areas. It would be desirable to use the 1985 census when applying the formula.

29. The third flaw in the sharing formula is that it fails to encourage greater resource mobilization efforts at the local level. The formula needs to be altered such that the amount of transfer received by any jurisdiction dependa in some degree on the amount of resource it contributes. As it stands, the tax allowance is a conditional, closeended, non-matching grant.1<sup>6</sup> Conditional because the funds must be used for education and health. Close-ended because the total amount to be 15/ Some acditional noticr. of the arbitrariness of the formula may be obtained from recognition of the fact that the cutoff population share--separating jurisdictions receiving above- and below-average per capita transfers--depends solely on the number of jurisdictions. The number presently stands at 3.03: because there are 33 jurisdictions, but if--for whatever reason--Colombia should decide to consolidate to (say) 16 jurisdictions, the cutoff population would become 6.251 and areas would begin receiving higher or lower per capita amounts for no reason other than a change in the number of departments/territories. 16/ See R.M. Bird, Intergovernmental Finance in Colombia--Final Report of the Mission on Interaovernment Finance (International Tax Program, Law School of Harvard University, Cambridge, Massachusetts, 1984), pp. 148-153 and N.E. Slack and R.M. Bird "Local Response to Intergovernmental Fiscal Transfers--The Case of Colombia" Public Finance (No. 3, 1983), pp. 429-437.
discrimination is offset by the effect of taste-based discrimination.

This conclusion is supported by previous studies concluding that immigrants are _more_ employable (Crush (2008)) and by evidence from the domestic worker survey: immigrants are more educated and younger than South Africans. (Section 4 found that employers prefer younger job seekers.)

# **6 Supply Side Response to Discrimination: Spatial Analysis**

Models of discrimination and job search can be divided into discrimination with random search and discrimination with targeted search. In random search models, the discriminated groups have lower reservation wages and accept jobs with lower match quality given that they expect to receive fewer job offers. In models of targeted search, job seekers decide where to apply after observing firms’ wage offers which provide information on the level of discrimination. The question whether job seekers adjust their search strategy in response to discriminatory practices of firms has important implications for how to interpret empirical results in the existing literature. In particular, Heckman (1998) points out that audit studies make the critical assumption that workers employ random search strategies. If job search is costly and workers are able to direct applications to non-discriminatory firms, this should increase their reservation wage, improve match quality and thus reduce racial wage gaps in equilibrium.

Partly due to a lack of data, there is very little evidence whether discrimination induces people to direct their job search. South Africa provides a context conducive to testing these questions. One of the legacies of apartheid is that workers tend to live far away from where jobs are located.<sup>27</sup> The average distance of African townships from the central business districts (CBDs) of the seven largest South African cities is 28 km. Combined with relatively high costs of public transport, this results in high search and commuting costs both monetary and time-wise.<sup>28</sup>

> 27In an attempt to claim city centers and marginalize the black and coloured population, the apartheid regime forcefully removed large parts of the urban population to homelands or townships outside urban areas; the most famous cases include Sophiatown in Johannesburg and District 6 in Cape Town. Townships suffered from poor infrastructure and provided few employment opportunities as they were located far from business and industry.

> 28Recent evidence shows that people with employment spend on average R215 (7.3% of net salary) per month on transport to and from work (SALDRU (2009)). The mean amount spent by the actively searching unemployed on transport costs related to the job search was R105.75 in the previous week. 42.5% of the unemployed refrain to local job search and report not spending anything.

22
**Table 1: Summary Statistics and Tests of Randomization**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Full Sample Mean|Standard<br>Deviation|Control Group (C)|Autonomous<br>Selection of<br>Trainers (T1)|Mandatory<br>Selection of<br>Trainers (T2)|P-value<br>T1 = C|<br>P-value<br>T2 = C|
|**Administrative Data**||||||||
|_Number of SACCOs_|_200_||_70_|_65_|_65_|||
|Log of Total Deposits (August 2015)|18.68|0.53|18.67|18.74|18.64|0.41|0.68|
|Log of Total Deposits (March 2016)|18.61|0.51|18.58|18.67|18.61|0.25|0.74|
|**Survey Data**||||||||
|_Number of Respondents_|_4,030_||_1,403_|_1,319_|_1,308_|||
|Gender (Male = 1)|0.46||0.47|0.46|0.46|0.90|0.77|
|Completed Primary School|0.56||0.56|0.55|0.56|0.86|0.76|
|Age|45.57|13.51|46.01|44.83|45.36|0.11|0.33|
|Total Household Size|5.41|2.28|5.44|5.36|5.39|0.48|0.71|

This table presents summary statistics for administrative data at the SACCO level and weighted summary statistics for survey data at the household level. Administrative data for periods prior to the intervention are used. Since we do not have household survey data prior to the intervention, the table presents summary statistics for fixed individual level attributes. Columns (1) and (2) present mean and standard deviations for the full sample. Columns (3)-(5) present average values by treatment status and subsequent columns present p-values for equality of means tests across treatments.
strictions in the land market. We provide several pieces of evidence that relates to the effects of higher migration costs.<sup>25</sup> First, we find that propensity of outmigration is lower (see Figure 1 based on population census 2012) and proportion of split households where some members are left behind to hold on to the land is higher (see FIgure 2 based on HIES 2002) in villages with land retrictions. The census did not provide the out-migration data by gender. To check if female migration is also less in the areas with higher land restrictions, we use census data on female share in total population. The census data show that share of female in total population is higher when land restrictions are higher (Figure 3). This is true if we focus only on adult women (graph omitted for brevity). Using population census data from 2002 and 2012, we compute the change in female share of population for the same age cohort (15-49 year old in 2002 and 25-59 year old in 2012). Figure 4 plots this variable against proportion of land under restrictions. The positive slope confirms that more female are staying back in areas with more land under restrictions. Third, female share in the labor force –defined from HIES 2002 data – is also positively correlated with the incidence of land restrictions (see Figure 5). Taken together, these can be interpreted as strong direct evidence in favor of the proposition that women stay back in the village in response to higher migration costs arising from land restrictions. Finally, using HIES 2002 data, we find that farm size is negatively correlated with land restrictions, as one would expect if more women (and possibly some men) stay back in the village facing higher migration costs (Figure 6).

We also find suggestive evidence that support the model where heterogeneity in labor force participation and the consequences of higher migration costs depend on productivity in home goods production. Due to cultural norms, women are responsible for providing home goods in Sri Lanka, and this gender role creates productivity differences between male and female in home goods production. It is also common that the older women bear most of the responsibility for home, and accumulate advantages due to learning effects and experience. If home production is important as postulated in our model, then increased migration costs due to land restrictions will affect the men much less than the women, and among women, the effect will be smaller on the

> 25 We are grateful to an anonymous referee for suggesting these additional test for the migration mechanism. As mentioned before that the migration mechanism has prediction for farm size was also noted by Andrew Foster.

25
Lambert, Sylvie, Martin Ravallion, and Dominique van de Walle (2014). "Intergenerational Mobility and Interpersonal Inequality in an African Economy." _Journal of Development Economics_ 110: 327-344.

Lambert, Sylvie and Pauline Rossi (2016). "Sons as widowhood insurance: Evidence from Senegal", _Journal of Development Economics,_ 120: 113-27, Lambert, Sylvie and Dominique van de Walle (2012). “Transcription des entretiens menés avec des veuves et des divorcées au Sénégal “ World Bank.

Lefèvre, Cécile and Alexandra Filhon (ed.) (2005) : _Histoires de familles, histoires familiales Les résultats de l’enquête Famille de 1999_ , INED, Collection : Cahiers, n° 156, 2005.

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Rivière, C. (1990). _Union et procréation en Afrique_ . L’Harmattan, Paris.

Sevak, Purvi, David Weir and Robert Willis (2003). “The Economic Consequences of a Husband’s Death: Evidence from the HRS and AHEAD,” _Social Security Bulletin_ 65(3).

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Yade, Awa (2007). “Stratégies matrimoniales au Sénégal sous la colonisation”, _Cahiers d’études africaines_ , 187-188: 623-642, URL : <u>http://etudesafricaines.revues.org/8342</u> 26
Figure 1: Firm performance across cohorts, 2005 <!-- Start of picture text --> age 1−5<br>age 6−10<br>age 11−20<br>age 21−30<br>age >30<br>0 2 4 6 8 10<br>log employment<br>age 1−5<br>age 6−10<br>age 11−20<br>age 21−30<br>age >30<br>0 5 10 15 20<br>log sales<br>export−age 1−5<br>export−age 6−10<br>export−age >10<br>0 5 10 15 20<br>log export revenue<br>.4<br>.3<br>.2<br>density<br>.1<br>0<br>.3<br>.2<br>density<br>.1<br>0<br>.2<br>.15<br>.1<br>density<br>.05<br>0<br><!-- End of picture text --> Notes: The figure depicts non-parametric estimates from a kernel density smoother with bandwidth 0.7, using data from 2005. Age is the number of years since birth of the firm. Export-age is the number of consecutive years the firm has recorded positive export revenue.

35
scribes the ENLACE national examination and the steps followed to construct the longitudinal data. Section 3 of the paper describes the empirical approach followed to study the relationship between test scores and future education and labor market trajectories. Section 4 presents the results and, finally, Section 5 presents policy implications and recommendations.

# **2 Constructing education trajectories and the transition to the labor market**

We construct two longitudinal datasets for this paper: (1) the ENLACE panel and (2) the ENILEMS-ENLACE panel. The first panel is formed by a cohort of students’ test scores at the end of primary school (Grade 6) in 2007, end of lower-secondary school (Grade 9) in 2010 and end of upper-secondary school (Grade 12) in 2013. The second panel merges a special module of the Mexican labor survey, _Encuesta Nacional de Empleo y Ocupaciones_ , ENOE, applied to individuals aged 18, 19 and 20 years during the third quarter of 2010, with students that sat for the ENLACE test in Grade 12 in 2008, 2009, and 2010.

## **2.1 The ENLACE test**

From 2006 to 2013, the Mexican Secretariat of Public Education (SEP) applied the census-based standardized test _Evaluación Nacional de Logro Académico en Centros Escolares_ , ENLACE. The test gathered information at the end of each academic year on student performance in math, Spanish, and a rotating subject for all third to ninth graders in private and public schools. Starting in 2008, ENLACE was also applied to students finishing upper secondary school (Grade 12). ENLACE was originally designed as a lowstakes assessment and had no bearing for students on GPA, graduation, or admission. Nonetheless, the take-up of the test was consistently close to 90 percent (Figure 1). A total of 15.1 million students in 136,000 schools took the examination in 2013, the last year when ENLACE was applied.

6
Rulli, M. C., A. Saviori, and P. D'Odorico. 2013. "Global land and water grabbing." _Proceedings of the National Academy of Sciences of the United States of America_ 110 (3): 892-7.

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Vollrath, D. 2009. "How Important Are Dual Economy Effects for Aggregate Productivity?" _Journal of Development Economics_ , **_88_** (2), 325-334.

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As seen from table 9, impact estimates for absolute change do not vary significantly when using interaction of program status with literacy to account for heterogeneity and adding quadratic and cubic terms to account for non-linearity (column 2 and 3). Those for percent change, however, are sensitive to alternative specifications. For instance, none of the estimates using DDD are significant. Failure of some of these estimates to hold up with possible non-linearity and heterogeneity need not be concerning, it simply implies we do not need to introduce extra terms in our specifications.

Finally, use of non-parametric approach in RD design strategy produces interesting results. Close to the program eligibility cut-off point (e.g. using smaller bandwidth), impact estimates in terms of absolute changes are larger in magnitude (table 10). For instance, within 4 percentage points of the cut-off literacy rate (between 36 and 44), average impact estimate is an increase of 20 students for DD and 10 students for DDD estimators. Increasing the bandwidth to 12 percentage points on each side, estimates are reduced by half for each estimator. Estimates for percent change vary less across both DD and DDD and across choice of bandwidth, ranging from 16 to 21 for DD and 14 to 17 percent for DDD.

## _District level estimates_

Table 11 reports program impacts in terms of percent change in enrollments at the district level. Results, in general, support those obtained from our school level analysis for grades 6-8 individually and combined. While district-level analyses suffer from problems associated with grouped data, they provide an alternative check to our overall results. Moreover, the results for grades 5 and 9 indicate that DDD estimates are statistically insignificant implying absence of “pull” and “push” effects at early stages of the stipend program.

# **6. Supplementary Evidence from Repeated Cross-sectional Household Surveys**

We use child-level data from PIHS 2001-02 and PSLM 2004-05 household surveys to provide an additional set of estimates for the impact of girls-stipend program on school participation rate for 10-14 year old children. Because of limited sample size, the estimates of gross enrollment and net enrollment rates are not representative at the district-level (the surveys were meant to be representative up to the province level only). This does not allow us to do an in-depth impact analysis at the district-level. However, the aggregate impact estimates of the stipend program on school attendance that we provide in this section are statistically reliable assuming the sample 19