rafmacalaba/datause-encoder-data huggingface dataset:
the total number of students at primary level hardly rose at all, increasing from 14.4 million in 1990-91 to just 14.6 million in 1998-99. Yet terminal efficiency, the percentage of students finishing sixth grade with the group they started school with, increased from 70 percent in 199091 to 86 percent in 1998-99. Basic secondary education has expanded very rapidly in the past 20 years, increasing from three million students in 1980-81 to more than five million in 1998-99. In 1980, only 58 percent of 13-15 year-olds were in basic secondary school; in 1998-99, 80 percent of that age group were enrolled. Even so, dropout rates continue to be high (and they are still rising) at the basic secondary level, so that despite basic secondary being compulsory, at the end of the 1990s only 65 percent of 18 year-olds had completed basic secondary (SEP 1999a). These data include both rural and urban areas. In urban areas, the dropout rates are higher than in rural areas. Besides the rapid expansion of basic secondary in the 1980s and 1990s, the key change in Mexican education in the past two decades has been the rapid increase in enrolment in post-basic education, and the rise in the percentage of basic secondary graduates who go on to uppersecondary.

In 1990-91, only 75 percent of those who finished basic education continued on to uppersecondary; in 1998-99, the proportion rose to 95 percent (SEP 1999a). Table 1 shows that of all the students who attended upper-secondary in 1999, 7.96 percent went to CONALEP, 0.76 percent attended schools offering the _bachillerato general,_ and 21.19 percent attended schools offering the _bachillerato t&cnico._ Tables Al.2 and Al.3 show the main differences between these educational systems.

12
which suggest the ln PD term dominates ln AD, ln RPA and ln PPD.<sup>13</sup> In short the measures of the differential degree of clustering within cities do not seem to add to the analysis.

There are two issues with drawing the conclusion that we should focus on PD rather than PPD or RPA measures for cities. First concerns measurement error. Use of Landscan data measures within-city clustering and inequality with error. While Landscan may do a better job than other currently available data sets, the assignment of people to work and residential locations is surely done with considerable error, which would bias the coefficients downward. The second issue concerns our measure of clustering. While our measure of clustering fits into a decomposition and is a standard measure in looking at spatial inequality, there are other standard measures. We look at one of these. In a context with so many grid cells in each urban area but a varying number, we preferred the spatial Gini to use incomparison over HHI based ones or a Theil index. We calculated the spatial Gini of cell concentration of economic activity within each city.<sup>14</sup> In column 7 of Table 7, the Gini has a completely insignificant coefficient. Of course, as for the coefficient of variation and covariance terms, the small size of the coefficient relative to the standard error could be explained by measurement error.

The conclusion for this sample and data is that a simple population density measure works as well as more nuanced measures, in attempting to capture economic density of cities in explaining income differences.

# **5.2.2 Does neighborhood density matter?**

A key issue as noted in the introduction is that studies suggest that within cities there are local scale externalities which decay sharply with distance so that from that perspective firms in one neighborhood do not interact with firms in another (Arzaghi and Henderson,

> 13In terms of horseraces for ln PD vs ln AD the ln PD term is positive at 0.74 (but with an s.e. of 0.44), while the ln AD term is small (-0.145) and insignificant. For ln PD vs ln PPD, as column 3 already tells us, the ln PD coefficient is large (0.51) and significant, while that for ln PPD is small (0.090) and insignificant. Finally for ln PD, against ln RPA, ln PD has a coefficient of 0.489 (but with an s.e. of 0.283) while ln RPA has a small (0.077) insignificant coefficient.

> 14We do this by ordering each cell by its density and noting the cumulative share of the population in each cell. The cumulative distribution of the population ordered by density represents the Lorenz curve. We then sum up the area under the Lorenz curve (by adding up the “height” and the “width” of each bar of the cumulative population histogram, where the “height” is the cumulative population and the “width” is 1/(number of total cells in the city)). We call this integral I, and, according to the Gini formula, calculate Gini=1-2I for each city.

24
**Graphs 6 - 9**

# **Replacement Rates by Cohort**

# **Observed :Labor Market Outcomes**

(In percent of individual gross wage) <!-- Start of picture text --> Grcph 6. Kazakhstan<br>replacement rates Graph 7. Kyrgyz RepUic<br>75,0 75.0-- 7 7 replacement wr-fe<br>65.0 6- .W. .. -_*<br>5-<br>45.0 -_35 ------<br>35.0 -_ _- - 25<br>25.0 0 5 10 15 20 25 30 35<br>0 5 10 15 20 25 30 35<br>years since reform implemented<br>years since reform irplemented<br>- Wnmn - - - - IA@ -nWome * Me n -<br>Grqh 8. Moldova Graph 9. Latvia<br>replacement rates replacement rates<br>75- 75 --<br>65<br>55 65<br>45 *-5<br>45<br>25- 35-35<br>0 5 10 15 20 25 30 35<br>25<br>years since reform irmplemented 0 5 10 15 20 25 30 35<br>years since reform imDlemented<br>- Wcn- -- - rsh - Wor9n - - - -Men<br><!-- End of picture text --> _Source:_ For graphs **6-9,** author's calculations.

4.18 _Incentives to work longer._ Except for Kazakhstan, the adjustment in the pension system introduced **by** the reform have been swift for today's retiring women and will also be tough for future retiring women. Unless the begin to work longer than the minimum retirement age, their benefits will be cut **by 30-50** percent. The minimum pension provides a floor on benefits, but this is quite low.

4.19 On of the main arguments in favor of **DC** systems is that they can be structured to encourage longer working years. Will women respond to these incentives, change their

**27**
_Potential problems associated with free_ **_entry_** _Section 4_ objectives. In such circumstances, restrictions on entry should usually be removed; and

- where universal service supported by exclusivity is working, it will generally be better to switch to a competitively neutral mechanisms to support universal service. Once this has been done, restrictions on entry will no longer be justified.

In some cases, Governments will be committed to universal service, but will not have the capacity to administer competitively neutral mechanisms. In such cases, restrictions on entry may be justified, if there are good prospects that this approach will achieve the objective.

Where Government decides to move away from a policy of cross-subsidisation, consideration could be given to continuing to limit entry for a transition period. Otherwise, higher cost suppliers may be able to enter the market and take advantage of price distortions before the incumbent is able to rebalance tariffs. If the entrant has sunk costs, and can lock in demand through long term contracts, then this could result in a higher overall cost structure for the industry.

# 4.3 Exclusivity and involving the private sector

Exclusivity is often granted to encourage private sector participation in an infrastructure sector. Exclusivity clauses have been a very common component of moves to involve the private sector (e.g. the granting of concessions, leases, or assets sales) in telecommunications, water and electricity. For example:

- in Jamaica, when the telecommunication utility was privatised in the 1980's a 25 year exclusivity clause was included;

- in South Africa in 1997 an exclusivity period of five years was extended to Telkom for local long distance and international services. Under the agreement Telkom was obliged to meet a series of service extension and quality improvement targets, and is subject to financial penalties if it fails to meet those targets;51 and

- in Buenos Aires, as in most places which have involved the private sector in water supply, the water concession gives the concessionaire the exclusive right to supply water within the concession zone. In other words, competitive entry is prohibited.

- 51 Financial Times Business Report: Technology (Q3:35) 13 February 1997.

_47_
# **Endogeneity concerns**

Extending the baseline model by including additional control variables controls for any omitted variable bias and accounts for potential endogeneity concerns. These additional control variables are formal and informal labor productivity, elevation and night lights emission. First, informal labor productivity denotes the productivity of informal businesses and strives to control for a potential endogeneity stemming from more productive businesses implementing more digital technologies (see top rows in Table 4). The effects of distance on different types of digital technologies are in line with the baseline results. In particular, the results indicate statistically significant negative effects of distance on the adoption of various digital technologies, suggesting that the baseline results are not driven by the potential common factor that more productive businesses are more likely to adopt digital technologies. The middle rows show results when formal labor productivity, which is the productivity of the closest formal firm, is included. Formal labor productivity controls for the fact that spillovers might be driven by informal businesses being close to very productive formal firms. The results remain robust to the baseline.

**Table 4: Baseline Model with Labor Productivity**

||(1)<br>PC|(2)<br>Tablet|(3)<br>Phone|(4)<br>MM|(5)<br>Website|
|---|---|---|---|---|---|
|Distance of Informal to Closest Formal Firm|-0.567*|-0.749***|-0.187***|-0.196***|-0.196|
||(0.314)|(0.154)|(0.009)|(0.036)|(0.405)|
|Informal Labor Productivity (log)|0.056|0.093|0.174***|0.184***|0.044|
||(0.064)|(0.117)|(0.017)|(0.038)|(0.130)|
|Number of Observations|814|814|814|806|600|
|Distance of Informal to Closest Formal Firm|-0.451**|-0.734***|-0.137***|-0.182**|-0.206|
||(0.214)|(0.084)|(0.036)|(0.077)|(0.422)|
|Formal Labor Productivity (log)|0.031|0.011|0.025|-0.009|-0.041|
||(0.044)|(0.125)|(0.054)|(0.047)|(0.036)|
|Number of Observations|730|730|730|722|556|
|Distance of Informal to Closest Formal Firm|-0.408**|-0.764***|-0.119***|-0.206***|-0.166|
||(0.194)|(0.111)|(0.034)|(0.050)|(0.497)|
|Formal Labor Productivity (log)|0.003<br>(0.033)|-0.005<br>(0.110)|0.047<br>(0.045)|0.039<br>(0.049)|-0.080***<br>(0.019)|
|Informal Labor Productivity (log)|0.079|0.113|0.160***|0.201***|0.081|
||(0.066)|(0.152)|(0.039)|(0.019)|(0.113)|
|Number of Observations|664|664|664|657|499|

12
# **Information Only**

Many schemes seek to change the balance of political interests by providing taxpayers and consumers with better information about the possibilities to improve SOE performance. Schemes to clarify SOE responsibilities have the potential to act this way, although that is not usually their main intent. Proponents of such schemes typically assume, implicitly or explicitly, that reasonable people can agree on the goals of the enterprise, the only problem is to make that consensus explicit. To the extent that clarifying responsibilities alters politics, however, it does so by alerting and arming taxpayers and customers with better information. Performance contracts, for example, offer taxpayers and customers a yardstick against which to measure the behavior of managers and politicians. Similarly, corporatization clarifies the accounting while special supervisory agencies promise opinions on firm performance that are expert and less compromised by non-commercial concerns.

Other schemes focus more directly on the possibility of empowering taxpayers and consumers by providing better information on the performance they should expect. Often this information is obtained through some kind of benchmarking exercise. For example, the Ministry of Power in India recently decided to publish statistics comparing the performance of 29 state electricity companies. Other possibilities involve publicizing the services that SOEs have promised to provide. For example, international aid agencies usually insist that large signs be placed next to public works projects describing, briefly, the works promised, the date on which they are to be completed, the funding supplied and the responsible agency. The sign tells the community what to expect and who to complain to if those expectations are not meet. Yet another approach is to ask the public to rate the quality of the service the enterprise supplies and to publicize the results. In 1993, for example, a citizens group in Bangalore began to issue periodic “report cards” on municipal utilities and services. The report cards are based on careful household surveys of service use, general satisfaction, frequency of problems, how many visits it took to resolve them, the frequency of bribes and the amounts paid. This scheme has inspired similar efforts in other cities in India and elsewhere.

Efforts to use better information to mobilize taxpayers and customers are seldom evaluated systematically. One careful quantitative study showed that corruption in 34
We include firms that were active, and thus included in the business registry, in at least two years over the sample period (between 2006 and 2015). Further, we include firms that remained located in the same district for every entry in the business registry. We then combine this firm panel with transaction-level customs data provided by the Serbian Customs Administration. The higher frequency customs data is aggregated at the firm-level to reflect the imports of each firm by origin country on an annual basis. The firms are identified by a unique tax identification number that allows us to merge the balance sheet data with the customs data. In the main results, we restrict the set of import partners to the top 150 countries that Serbian firms source from (aggregating all firms) over the sample period.<sup>2</sup> We define a firm’s neighbors as firms in the same industry and district (importing firms are observed in 484 NACE 4-digit industries and all 25 districts).<sup>3</sup> We group firms by district in order to capture the geographic variation in sourcing choices observed in Serbia (see Figure 4). Similarly, since the combination of inputs a firm sources are dependent on its production process, grouping firms by NACE 4-digit industry to define neighboring firms ensures that only the neighbors with relevant experiences for firm learning are being included in the analysis.

While we define the signals a firm receives from its neighbors in an analogous manner to Fernandes and Tang (2014), it is important to note that, in contrast, we do not trim our panel to exclude firms that receive exceptionally strong or weak signals because we do not want to artificially censor the learning signal.<sup>4</sup>

> 2The set of import partners is restricted to the top 150 countries so that it was feasible to calculate the neighbor learning variables. The top 150 countries make up 99.6% of Serbia’s import values.

> 3The district is equivalent to the smallest geographical unit, the Nomenclature of Territorial Units for Statistics (NUTS) level 3, used by the Eurostat. The average size of a Serbian district is 3100 _km_<sup>2</sup> . Excluding the two largest cities – Belgrade, the capital city, and Novi Sad – each district has about 100,000 to 200,000 population.

> 4The results are robust to this decision and can be provided upon request.

14
- 41 -

30 Anne Booth has pointed out to us in correspondence that the consumption component of the "Farmers' Terms of Trade Index* (FTT) for Java does suggest a higher inflation rate for rural areas than the CPI or NEC. However, we have found that this is largely due to the very high price increases recorded for vegetables in the FTT for Central and East Java over 1985-86 (see, for example, CBS, 1987 Table 9.4.9). Substitution possibilities in consumption are thought to be relatively high for vegetables (Deaton's, 1989, estimates for Java in 1981 indicate a compensated own price elasticity of about unity), so fixed weight price indices can considerably overstate the welfare effect of such a change in relative prices. It should also be noted that most farmers in Java are (or car. readily become) net producers of vegetables, and so they are unlikely to be worse off after such a change in relative prices.
Based on this literature, alongside the intuition that the economic shock from the Great Recession would affect migrant more than non-migrant households, we define migrant households for this analysis based on child age, highlighting whether a child lived in a household with a migrant to the United States during their early, most vulnerable years. Specifically, a child under three years old measured in 2008 is defined to be in a migrant household if the household had a migrant to the United States (including the head, spouse of head or children of either as described above)<sup>vii</sup> in at least three of the four calendar years 2005– 08, and not in a migrant household otherwise.<sup>viii</sup> This definition includes migrants who left prior to 2005 and remained away for at least three of the four years 2005–08.<sup>ix</sup> Since the baseline household survey was implemented from April to July of 2008, the four-year window covers the entire lifespan of any child under three years of age at the time of the baseline, including part or all of the in-utero period. Similarly, a child under three years old in 2012 is defined as living in a migrant household if the household had a migrant to the United States in at least three of the four calendar years 2009–12. For older children, living in a migrant household is defined in parallel fashion, i.e., based on having a migrant in the United States during the years corresponding to when the child was under three years old, adjusting the window based on exact age at measurement (see Appendix Figure 1).<sup>x</sup> _3.2 Cross-sectional cohort comparison model_ Categorizing cohorts of children under three years old in 2008 or 2012 by migration status as defined in Section 3.1 results in four groups. These are used to set up a difference-indifference (DD) analysis in which outcomes for children in migrant versus (comparison) nonmigrant households in 2012, i.e., children for whom the economic shock occurred during early childhood, are contrasted with children in migrant versus non-migrant households in 2008, for whom the crisis occurred after they were measured.<sup>xi</sup> We posit that children in non-migrant households in 2012 were less exposed to, and therefore would have been less affected by, the economic crisis, as their households did not have migrants living in the United States and hence
# **Multidimensional Poverty, Gender, and Forced Displacement : A Multi-Country, Intrahousehold Analysis in Sub-Saharan Africa**

Yeshwas Admasu,<sup>[†]</sup> Sabina Alkire,<sup>[‡]</sup> and Sophie Scharlin-Pettee<sup>[‡]</sup>

**Keywords** : multidimensional poverty, MPI, forced displacement, refugee, IDP, gender inequality, intrahousehold, Sub-Saharan Africa

**JEL:** I32, J16, D63, D13, D74, O55

The authors of this paper conducted their research under Gender Dimensions of Forced Displacement project. The project is co-led by Lucia Hanmer and Diana Arango under the guidance of Hana Brixi, Global Director, Gender Unit, The World Bank Group.

Acknowledgments: The authors are grateful for the guidance and comments received by Amalia Hadas Rubin, Diana Jimena Arango, Jeni Klugman, and Lucia Hanmer at the World Bank, as well as Monica Pinilla-Roncancio and Corinne Mitchell at OPHI. The authors would also like to acknowledge the help of Utz Pape with the data used in this study. All errors remain our own. Funding: This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

Data availability: The data that support the findings of this study are openly available at the World Bank Microdata Library at https://microdata.worldbank.org/index.php/home.
39 Table 5 (con'd) <!-- Start of picture text --> Structural Equations |<br>Addition Proportion of Proportion of Probahilty a- | Birihs in Last<br>Variables Children Last 5 Years Last 5 Years Stenlized in 5 Years<br>Wanted 5 Years Protected by Protected by Last 5 Years<br>Ago Temporary Traditional<br>Modem Methods<br>Methods<br>Probability sterilized . -0.23 (0.22)<br>Constant -0.76 (5.21)--* 0.19 (0.26) -1.64 (2.12)- 0.54 (1.40) 0.95 (0.66)<br>Thresholds 1 0.64 (25.09)... 0.99 (2.65)..<br>2 1.33 (32.54)'' 2.28 (3.31)"<br>3 2.07 (35.08)_* _<br>N 2438 2438 2438 2627 2627<br><!-- End of picture text -->

+ Reduced Form r Rural * Signiricant at _10o_ level Significant at 5% level Significant at 1% level
35

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