rafmacalaba/datause-encoder-data huggingface dataset:
transaction, the gender and age of the customer who executed the transaction as well as the gender and the age of the agent who assisted with the transaction. The data also include information on the type of transaction (cash deposit, withdrawal, funds transfer, or school fees payment) and the currency used (USD or CDF). Market level information such as the branch, the town and the dominant financial institution where the branch was located was also available.

About 61% of the FINCA DRC customers are males, a distribution similar to that of many other institutions in developing countries (Chamboko et al., 2018). In addition, males comprise 77% of FINCA DRC agents. This limited participation of females as agents may be explained in part by the fact that FINCA targets former or current clients as prospective agents (Cull et al., 2018). Targeted clients are individuals for whom FINCA has a reliable financial activity record and who have an existing shop which can double up as an agent (Cull et al., 2018).

Figure 1 shows the distribution of male and female agents across the 21 FINCA branches (henceforth referred to as markets). The representation of female agents ranges from 7% in Boma to 34% in Matete, indicating that there are both male and female agents in every market. Thus, while in every market the majority of agents were men, clients always had a number of male and female agents to choose from.

Panel A of Table 1 reports agent characteristics from the transaction data set. To complement these data, the authors were able to obtain the paper-based registration forms of the first 404 FINCA agents that signed up. These registration forms contain additional information that could be matched for 131 agents in the transaction data. Columns 1 and 3 of Table 1 report data on the sample of 131 matched agents, while Columns 2 and 4 report data from the remainder of the 1,119 unmatched agents. Column 5 reports the p-values for group differences between the sample of matched and unmatched female agents. Column 6 reports analogous p-values for male agents. Finally, Column 7 reports p-values of differences between male and female matched agents.

The comparisons in Columns 5 and 6 indicate significant differences between the matched and unmatched samples with respect to age of the agent and their business, as well as the frequency of some types of transactions. Significant differences in the age of agent and the business should not come as a surprise since we obtained registration records for the first agents that signed up, which therefore, would tend to be older.

Panel B of Table 1 reports the additional characteristics from registration records for the sample of matched agents. According to Column 7, male and female matched agents are similar along most characteristics. The only statistically significant difference is in the mean number of opening hours per day (female agents were open 10.7 hours and males 12.3 hours on average, p = 0.042). Both male and 5
An additional method of testing the robustness of the results is to compare the weighted and unweighted results to assess the sensitive of the results to the weighting strategy. Figure 17 clearly demonstrates that the effect of weighting the results is trivial. Thus, the core observations of the paper with respect to the popularity of ending propiska limits (the primary application of the monthly panel in this application) is not affected by attrition in a material way.

**Figure 17: Support for Removing Propiska Limitations, Comparison of Weighted vs. Unweighted Results**

<!-- Start of picture text --> 98%<br>96%<br>94%<br>92%<br>90%<br>88%<br>86%<br>84%<br>82%<br>80%<br>78%<br>76%<br>Weighted Unweighted<br><!-- End of picture text --> 62
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Roads

## **Achievements**

Burkina’s 15,202 km of classified road network length seems, in general, adequately designed given the level of traffic and the connectivity it provides; it is also, for the most part, in good condition. Even if Burkina Faso’s classified road density indicators look relatively low by some standards, the classified network provides basic regional, national, and international connectivity, linking Ouagadougou to international border crossings and provincial capitals in the interior (table 5).

**Table 5. Burkina Faso’s road indicators benchmarked against Africa’s low- and middle-income countries**

||Unit|Low-<br>income<br>countries|**Burkina**<br>**Faso**|Middle-<br>income<br>countries|
|---|---|---|---|---|
|Total road density|km/1,000 km<sup>2</sup>of arable land|132.1|**81.5**|318.4|
|Classified road density|km/1,000 km<sup>2</sup>of arable land|88.2|**55.6**|278.4|
|Rural accessibility index—household<br>survey|% of rural population within 2 km of all-season road|34.1|**25.0**|62.7|
|GIS rural accessibility|% of rural population within 2 km of all-season road|23.1|**23.7**|31.5|
|Paved road traffic|Average annual daily traffic|1,341.1|**867.7**|3,797.7|
|Unpaved road traffic|Average annual daily traffic|38.5|**35.8**|74.7|
|Paved classified network condition|% in good or fair condition|86.2|**96.1**|82.0|
|Unpaved classified network condition|% in good or fair condition|55.8|**90.3**|57.6|
|Perceived transport quality|% firms identifying roads as major business constraint|27.6|**55.8**|18.2|
|Overengineering|% of main road network paved relative to low traffic|29.6|**25.9**|18.4|
|Underengineering|% of main road network paved relative to high traffic|13.5|**1.0**|20.0|

_Source:_ Gwillliam and others 2008. Derived from the AICD national database (http://www.infrastructureafrica.org/aicd/tools/data). _Note_ : GIS = geographic information system.

The selection of road standards matches traffic levels (figure 6), despite the fact that paved road traffic is very low when compared to the average for nonfragile LICs (table 5). Less than 1 percent of its classified road network is underengineered and only one-third is overengineered, but this is above the average for the region, at 27 percent.<sup>1</sup>

> 1 Here the definition of over- and underengineering is very basic. A segment of road is overengineered if it is paved and its daily traffic is below the 300-vehicles-per-day (vpd) threshold. On the contrary a segment of a road is considered underengineered if it is not paved and its traffic is over 300 vpd.

14
|Observations|10,502|4,950|5,552|4,183|3,369|2,950|
|---|---|---|---|---|---|---|
|Adjusted R square|0.297|0.291|0.337|0.291|0.276|0.355|
|Entertainment|0.495***|0.342**|1.065***|0.383*|0.600***|0.016|
||(0.143)|(0.142)|(0.356)|(0.215)|(0.156)|(0.291)|
|Observations|10,477|4,940|5,537|4,167|3,363|2,947|
|Adjusted R square|0.284|0.287|0.305|0.275|0.282|0.315|
|Automobiles|0.310**|0.158|1.465***|0.213|0.458**|0.905***|
||(0.120)|(0.105)|(0.305)|(0.160)|(0.196)|(0.318)|
|Observations|10,372|4,885|5,487|4,116|3,325|2,931|
|Adjusted R square|0.235|0.245|0.230|0.231|0.239|0.245|
|Other vehicles|0.057|-0.023|0.818**|-0.103|0.304|-0.022|
||(0.143)|(0.128)|(0.387)|(0.123)|(0.372)|(0.591)|
|Observations|10,461|4,936|5,525|4,159|3,357|2,945|
|Adjusted R square|0.397|0.403|0.396|0.402|0.396|0.398|
|Durable goods|0.178|0.025|0.929**|0.125|0.172|0.220|
||(0.114)|(0.101)|(0.426)|(0.112)|(0.195)|(0.917)|
|Observations|10,670|5,049|5,621|4,230|3,423|3,017|
|Adjusted R square|0.430|0.427|0.439|0.422|0.447|0.422|
|Medical|0.163**|0.124|0.407**|0.085|0.327**|0.429|
||(0.082)|(0.097)|(0.192)|(0.135)|(0.123)|(0.391)|
|Observations|10,562|5,004|5,558|4,173|3,395|2,994|
|Adjusted R square|0.378|0.366|0.397|0.387|0.382|0.359|
|Health and fitness|0.221**|0.059|0.563***|0.148|0.217|0.519|
||(0.106)|(0.093)|(0.200)|(0.138)|(0.144)|(0.327)|
|Observations|10,673|5,055|5,618|4,243|3,422|3,008|
|Adjusted R square|0.303|0.288|0.366|0.301|0.321|0.289|
|Education|-0.151|-0.156|0.095|-0.185**|0.010|-0.248|
||(0.117)|(0.130)|(0.274)|(0.085)|(0.201)|(0.369)|
|Observations|10,604|5,026|5,578|4,208|3,406|2,990|
|Adjusted R square|0.221|0.232|0.222|0.227|0.221|0.232|
|Home repair|-0.121|-0.202**|0.077|-0.121|-0.209|-0.458|
||(0.091)|(0.095)|(0.319)|(0.095)|(0.158)|(0.616)|
|Observations|10,505|4,961|5,544|4,182|3,372|2,951|
|Adjusted R square|0.449|0.433|0.468|0.473|0.456|0.409|
|Gifts|-0.028|-0.023|0.353|-0.031|0.074|0.121|
||(0.107)|(0.107)|(0.286)|(0.156)|(0.128)|(0.565)|
|Observations|8,716|4,143|4,573|3,377|2,817|2,522|
|Adjusted R square|0.112|0.113|0.116|0.077|0.121|0.171|

Note: Alternative extended model, with additional variables of control, including provincial CPI of the corresponding consumption category, lagged value of log household per capita in consumption category in question, household average years of schooling household gender ratio, average age, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 44
# 6 Conclusion

This paper explores the impact of existing diasporas on the number, skill composition and concentration of international migrants. We ...rst develop a simple theoretical framework emphasizing the role of diasporas which operates through the lowering of both migration costs (due to information and assimilation di¢ culties) and visa costs (due to government policies). Diasporas lower migration costs through network effects and visa costs by increasing the probability of non economic migration through family reuni...cation programs. These two e¤ects increase the size of migration ‡ows and reasonably reduce the selection of high-skill migrants. We then evaluate the implications of the theoretical predictions using new bilateral migration data by educational level. We estimate the role of existing networks in 1990 on the migration ‡ows between 1990 and 2000, on their skill composition and on their relative concentration across educational levels. We account for potential problems related to the nature of the data and check the robustness of initial OLS estimates. First, we abstract from the bias induced by the log linearization of gravity models. Second, we account for the occurrence of a potential selection bias due to the large number of zeros in the country pairs. Third, we take into account a potential endogeneity problem of existing diasporas through instrumental variable estimations. Our results are extremely robust across estimation methods.

In short, we ...nd evidence of a strong impact of existing diasporas. Regarding size, diasporas are by far the most important determinant of migration ‡ows even after accounting for the usual variables a¤ecting bilateral migration costs such as distance, colonial links and linguistic proximity. Extracting the explained partial sum of squares, we ...nd that 71 percent of the observed variability of the migration ‡ows is explained by diaspora e¤ects. Regarding selection, diasporas are found to favor more the migration of the low-skill than migration of the highly skilled. It therefore exerts a strong negative impact on the selection of migrants. We ...nd that diaspora e¤ects explain 47% of the total variability of the selection ratio in 2000. Disregarding diaspora externalities but using much more detailed data on base wages and returns to skill (captured in our ...xed e¤ects), Grogger and Hanson (2008) ...nd that, on average, wage di¤erences explain 58 percent of the immigrant skill gap. This suggests that diaspora e¤ects and wage di¤erences leave little space for education-based selective policies in determining the quantity and quality of immigrants. Our results suggest that policies aiming at increasing the educational quality of the migrants might be highly constrained by the existing migrant’s network. In the presence of large diasporas, more selective migration policies might fail unless family reuni...cation programs are deeply reformed and limited. The same holds for policies that would aim to favor ethnic diversity of the migrants.

33
10 slowly than income, paved road networks expand at the same rate as income. At the urban level, per capita road length is positively associated with per capita income across world cities but changes little over time.

The empirical results from the current analyses are summarized in Table 3. At the national level, income elasticities of road length are 0.5 for total roads and 1.0 for paved roads. The variable income elasticity estimates are statistically insignificant even at the 0.10 Ilevel for both total roads and paved roads, suggesting that the income elasticity of road length is constant. The piecewise regression analysis produces a statistically significant non-linear relation for total road length, but not for paved road length (Appendix Table 6, Eq. 2 and 3). Both total road length and paved road length are unit elastic with population, and the variable population elasticities are not significant, indicating that the constant elasticity specification for population is consistent with the data. Again, population demonstrates a constant scale relation with road length because its elasticities are 1. These estimates are based on the cross-sectional variation in panel data, but fixed effect estimates based on first differences over time in the same panel data produced generally similar results for income. The first difference results differ for population: The population elasticity is insignificant for total roads (value of 0.4) and significant and larger for paved roads (value of 1.3) compared to the cross section results..

**Table 3. Elasticities of Road Length with Respect to Per Capita Income, Population, and Population Density**

||El<br>|asticity Estimat|es<br>||
|---|---|---|---|---|
|Type of Elasticity Estimates|Per capita<br>income|Population|Population<br>density|R-squared|
|**INational: iengtn o0 total roac**|||||
|Constant|0.5*|1.0*|-0.3*|0.89|
|Varying: at the mean of low-income group|0.6|1.1|-0.3|0.90|
|Varying: at the mean of high-income group|0.5|1.1|-0.3|0.90|
|First difference|0.5|0.4|n.a.|0.30|
|**National: length of paved road**|||||
|Constant|1.0*|1.0*|0.0|0.86|
|Varying: at the mean of low-income group|0.8|1.3|-0.1*|0.87|
|Varying: at the mean of high-income group|1.0|1.2|-0.2*|0.87|
|First difference|0.8 *|13*|n.a.|0.32|
|**Urban: length of urban road**|||||
|Constant|0.7 *|0.8*|-0.8 *|0.92|
|Varying: at the mean of low-income group|0.6 *|n.a.|n.a.|0.93|
|Varying: at the mean of high-income group|1.2 *|n.a.|n.a.|0.93|
|First difference|0.1 *|0.5*|-0.4 *|0.18|
|n.a.NOt applicable.|||||

* Statistically significant at the 0.05 level.
# **Long-Term Evolution of Inequality of Opportunity**<sup></sup>

Maurizio Bussolo (World Bank), Daniele Checchi (University of Milan) and Vito Peragine (University of Bari)

**Keywords:** Inequality of Opportunity, Decomposition methods, Education mobility, Returns to Education, Family Networking, Cohort Analysis.

**JEL codes:** D31, D63, E24, I24, J62

>  This paper was started as background paper for the World Bank regional flagship report on “Towards a new social contract: Taking on distributional tensions in Europe and Central Asia”. We thank Jorg Neugschwender (Luxemburg Income project) and Teresa Randazzo (University of Bari) for technical assistance in building the dataset, and Tullio Jappelli (University of Naples, Italy) for extensive discussions. All errors are our own responsibility.
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Table 5: Secondary school completion and Completed years of education

||(1)|(2)|
|---|---|---|
|VARIABLES|Secondary Completion|Years of Education|
|year2014|0.0058|0.4973**|
||(0.0282)|(0.2398)|
|Household-poor (omitted = poorer)|0.0081|1.3286***|
||(0.0083)|(0.1570)|
|Household-middle|0.0469***|2.4016***|
||(0.0101)|(0.1597)|
|Household-rich|0.0808***|3.4853***|
||(0.0125)|(0.1700)|
|Household-richer|0.2359***|5.0930***|
||(0.0208)|(0.1992)|
|Household size|-0.0028**|-0.0305**|
||(0.0014)|(0.0122)|
|female|0.0379***|2.9445***|
||(0.0086)|(0.1397)|
|year2014_×_Household-poor|0.0046|-0.0849|
||(0.0123)|(0.1629)|
|year2014_×_Household-middle|0.0103|-0.3457**|
||(0.0140)|(0.1641)|
|year2014_×_Household-rich|0.0176|-0.6085***|
||(0.0211)|(0.2011)|
|year2014_×_Household-richer|0.0323|-0.5967**|
||(0.0325)|(0.2546)|
|female_×_Household-poor|0.0016|-0.7557***|
||(0.0108)|(0.1621)|
|female_×_Household-middle|0.0122|-1.0507***|
||(0.0126)|(0.1500)|
|female_×_Household-rich|0.0220|-1.4964***|
||(0.0144)|(0.1639)|
|female_×_Household-richer|-0.0035|-2.1604***|
||(0.0215)|(0.1866)|
|year2014_×_female|0.0291***|-0.1402|
||(0.0112)|(0.1038)|
|Constant|-0.0455|3.9527***|
||(0.0407)|(0.3609)|
|Observations|16,259|16,259|
|R-squared|0.193|0.327|

_Notes_ : Linearized (svy) standard errors in parentheses and significance levels are indicated as follows: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Other included controls are age dummies, location and its interaction with household wealth, gender of household head, marital status, and household relationship structure. The sample is those aged 16 to 24 years.

26
two surveys. There are 105 such switchers between 1993 and 1998 and 266 in the 2002– 2006 surveys.

The public and the private sector workers have very different characteristics, which reflect the differences in the job requirements in the two sectors (see table 3). As expected, the public sector workers spent more time in school (5 years more in 1993) and are more likely to have received vocational training (28 percent more in 1993). There are also proportionally more female workers in the public sector (12 percent more), suggesting that public employment is more “women friendly” than private employment (see Liu 2004). Considering the evolution between 1993 and 2006, it appears that the private and the public sector workers have become more similar in terms of their observed characteristics, with the exception of experience; the public sector workforce is aging.

|TA|BLE3. Descri|ptive Statistics<br>1993|on Public and|Private Sector|Workers<br>2006||
|---|---|---|---|---|---|---|
||Public|Private|Diff.|Public|Private|Diff.|
|Years of schooling|10.96<br>(3.43)|6.03<br>(3.65)|4.93<br>***|10.75<br>(2.26)|7.15<br>(3.46)|3.60<br>***|
|Years of experience|15.50<br>(8.72)|16.39<br>(11.33)|−0.89|22.14<br>(11.33)|19.28<br>(12.19)|2.85<br>***|
|Proportion of men|0.51<br>(0.50)|0.67<br>(0.49)|−0.16<br>***|0.58<br>(0.49)|0.66<br>(0.47)|-0.09<br>***|
|Proportion with<br>vocational training|0.36<br>(0.48)|0.05<br>(0.22)|0.31<br>***|0.34<br>(0.47)|0.11<br>(0.32)|0.23<br>***|
|Number of workers|564|824||1429|2664||

_Source:_ Author's calculations based on VLSS and VHLSS data.

# _<<B>>The “public sector dummy approach”_

We first consider the simplest method to compute the public-private wage gap, controlling for the differences in the workers' characteristics between the two sectors. This method implicitly assumes that the returns to the workers' characteristics are the 17
To control for observed and unobserved heterogeneity across countries and pairs, and to address endogeneity, I include a rich set of importeryear, exporter-year, and country-pair fixed effects. One of the biggest and oldest concerns in identifying the effect of trade policy on trade flows is endogeneity, since countries that trade more with each other are arguably more likely to conclude an RTA. Baier and Bergstrand (2007) argue that the most plausible estimates of the average effect of RTAs on bilateral trade flows can be obtained by the use of panel data with country-pair fixed effects. This eliminates an important source of endogeneity that is due to time-invariant unobserved heterogeneity between country pairs.<sup>16</sup> In estimating the gravity equation, I use the Poisson Pseudo Maximum Likelihood (PPML) estimation, as advocated by Santos Silva and Tenreyro (2006), to account for heteroskedasticity in the trade flow data which can lead to inconsistent estimation of log-linearized OLS.<sup>17</sup> A diagnosis of the error term, following Head and Mayer (2014), supports the use of PPML over OLS or Gamma PML.<sup>18</sup> The baseline estimating equation is given by where _Yijt_ is gross or GVC-exports in manufacturing from country _i_ to country _j_ in year _t_ . Three alternative measures of GVC-exports (described in detail below) will be used: gross exports in intermediate goods, backward GVC-exports, and forward GVC-exports. _αij_ captures all time-invariant country pair-specific effects such as distance, cultural and linguistic similarities, as well as any unobserved bilateral characteristics that may affect the trade flow between the two countries. _αit_ and _αjt_ are country-year fixed effects that capture all exporter and importer characteristics that vary over time such as output, price levels and multilateral resistance. _RTA_ is a

> 16The fixed effects, however, cannot account for country-pair-specific changes over time, other than trade agreements, that may affect trade flows (Bergstrand et al., 2015). The concern for potentially remaining endogeneity is partially addressed in the robustness checks by interacting distance with year dummies. This would control for the changing effects of distance, possibly an important element of changes in bilateral trade costs, over time.

> 17The PPML regressions were technically implemented using the STATA command _ppml_ _~~p~~ anel_ _~~s~~ g_ , developed by Zylkin (2017), which speeds up the estimation with many fixed effects (Larch et al., 2017).

18Head and Mayer (2014) compare the performance of OLS, PPML, and GPML under different data generating processes and show that PPML performs best when the meanvariance ratio is constant (CVMR). Using Monte Carlo simulations, the authors show that the Manning and Mullahy (2001) test statistic (“MaMu test”) can be used to distinguish between log-normal and CVMR. For my sample, the MaMu test delivers 1.64 (significantly less than 2), supporting CVMR error and hence a preference for PPML estimation. The authors also find no evidence of bias due to the incidental parameter problem with PPML using multi-way fixed effects.

11