rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1: Percentage of Female Firm Owners by Size Group**

<!-- Start of picture text --> 60<br>50<br>50<br>44<br>40 41<br>38 37 38<br>40<br>33<br>31<br>29<br>28<br>30 26 27 25<br>21 24 23 23<br>19 18 20 20<br>20<br>14<br>12<br>10<br>0<br>Argentina Bolivia Brazil Ecuador Honduras Mexico Peru Uruguay (*)<br>Micro Small Medium<br><!-- End of picture text --> _Note_ : Micro firms are defined as having less than 5 employees (including the owner), small firms have 5 to 10 employees, and medium size firms have more than 11 employees. For Uruguay only, small firms have between 5 and 9 employees and medium size firms have more than 10 employees. The year is 2008 or a year close to 2008. _Source_ : Author’s calculations based on household surveys from local statistical agencies.

Figure 2 shows the percentage of female firm owners in different industries. In all countries, the highest concentration of female firm owners is in trade. Up to 48 percent of firm owners in this industry are women. Manufacturing is the industry with the second largest percentage of female firm owners, followed closely by services. In most countries, few firm owners in agriculture are women, with the exception of Bolivia, Brazil, and Ecuador. In all countries, there is only a negligible fraction of female firm owners in the construction industry.

Overall, the household survey data indicates that there is substantial variation in the percentage of female firm owners across firm size categories and across industries. Female firm owners are concentrated in the smallest firms and are predominantly in the trade sector.

5
- Pico-Alfonso, M. A. 2005. Psychological intimate partner violence: The major predictor of posttraumatic stress disorder in abused women. Neuroscience & Biobehavioral Reviews, 29(1), 181-193.

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- Surkan P., C. Kennedy, K. Hurley, Black M. 2011. Maternal depression and early childhood growth in developing countries: systematic review and meta-analysis. Bull World Health Organ, 287:607–615D.

24
# 1. **Introduction**

# **1.1 Background**

In most countries, poverty is spatially concentrated. Extreme poverty in inaccessible areas with unfavorable terrain often coexists with relative affluence in more favorable locations close to major cities and markets. Information on the spatial distribution of poverty is of interest to policymakers and researchers for a number of reasons. First, it can be used to quantify suspected regional disparities in living standards and identify which areas are falling behind in the process of economic development. Second, it facilitates the targeting of programs whose purpose is, at least in part, to alleviate poverty such as education, health, credit, and food aid. Third, it may shed light on the geographic factors associated with poverty, such as mountainous terrain or distance from major cities.

Traditionally, information on poverty has come from household income and expenditure surveys. These surveys generally have sample sizes of 2000 to 8000 households, which only allow estimates of poverty for 3 to 12 regions within a country. Previous research has, however, shown that geographic targeting is most effective when the geographic units are quite small, such as a village or district (Baker and Grosh, 1994; Bigman and Fofack, 2000). The only household information usually available at this level of disaggregation is census data, but census questionnaires are generally limited to household characteristics and rarely include questions on income or expenditure.

In recent years, new techniques have been developed that combine household and census data to estimate poverty for more disaggregated geographic units. Although various approaches have been used, they all involve two steps. First, household survey data is used to estimate poverty or expenditure as a function of household characteristics such as household composition, education, occupation, housing characteristics, and asset ownership. Second, census data on those same household characteristics are inserted into the equation to generate estimates of poverty for small geographic areas.

2
|(8)|-0.0405_∗∗∗_<br>(0.0155)|0.00462<br>(0.00888)|0.0154<br>(0.0105)|Yes<br>Yes|Yes|Yes|Yes<br>Yes<br>55480|||
|---|---|---|---|---|---|---|---|---|---|
|(7)|-0.0255_∗∗∗_<br>(0.00844)|0.0120_∗∗∗_<br>(0.00361)|0.0249_∗∗∗_<br>(0.00564)|Yes<br>Yes|Yes|Yes|Yes<br>No<br>278909|||
|(6)|||0.0168<br>(0.0105)|Yes<br>Yes|Yes|Yes|Yes<br>Yes<br>55480|||
|(5)|||0.0267_∗∗∗_<br>(0.00581)|Yes<br>Yes|Yes|Yes|Yes<br>No<br>278909|||
|(4)||0.00374<br>(0.00883)||Yes<br>Yes|Yes|Yes|Yes<br>Yes<br>55480|||
|(2)<br>(3)|-0.0408_∗∗∗_<br>(0.0155)|0.0116_∗∗∗_<br>(0.00361)||Yes<br>Yes<br>Yes<br>Yes|Yes<br>Yes|Yes<br>Yes|Yes<br>Yes<br>Yes<br>No<br>55480<br>278909|status.<br>l _×_ Year of arrival.||
|(1)|-0.0278_∗∗∗_<br>(0.00859)|||Yes<br>Yes|Yes|Yes|Yes<br>No<br>278909|gender, marital <br>Canton of arriva||
||Unemployment rate|Network|Attitudes|Current year FE<br>Country of birth FE|Arrival year FE|Canton of arrival FE|Canton of arrival X Current year FE<br>Education FE<br>Observations|Dependent variable is Employment dummy.<br>All regressions also include age, age squared, <br>Standard errors are clustered at the level of|* p < 0.10, ** p < 0.05, *** p < 0.01|

50
**Table 2. R-squared Shapley–Owen Decomposition for Student Outcomes** **_Estimations Using Student-Level Data_**

||(1)<br>Reading|(2)<br>Quantitative<br>Reasoning|(3)<br>Graduation|(4)<br>Formal<br>Employment|(5)<br>Wages<br>(log)|
|---|---|---|---|---|---|
|_Percent of explained variation a_|_ttributable to:_|||||
|Individual characteristics|57.11|48.34|18.13|13.16|13.81|
|Peer characteristics|13.21|15.15|4.62|6.87|9.54|
|Program-level fixed effects|24.62|31.39|72.17|63.68|57.55|
|Local SCP supply measures|5.06|5.12|5.08|16.29|19.10|
|R-squared|0.36|0.32|0.14|0.15|0.16|
|Observations|13,461|13,461|120,712|64,108|46,068|

_Source_ : Authors’ calculations using administrative data. For the list of variables included in individual and peer characteristics, see Section 4.3. See Table A3 for the list of variables included in local SCP supply measures.

_Notes_ : This table presents results from the R-squared Shapley-Owen decomposition for the regressions (models I+Z+S) reported in Tables A4 (Columns [1] and [2] for learning outcomes), A5 (Column [3] for graduation) and A6 (Columns [5] and [6] for formal employment and wages) estimated using student-level data. For each regression, the table shows the R-squared and the percent of the explained variation attributable to each set of individual or peer characteristics, program-level fixed effects, and local SCP supply measures. “Observations” indicates number of students (equal to the number of observations in the underlying regression).

40
Table 2: Women’s Ideal Work Status, Attitudes, and Perceived Norms

||Attitudes|Pooled PN|Future PN|
|---|---|---|---|
||_Women_|||
|Ideal: I would work|0_._397***|0_._127***|0_._181***|
||(0_._034)|(0_._023)|(0_._032)|
|Num.Obs.|514|514|514|
|Covariates Included|Yes|Yes|Yes|
||_Men_|||
|Ideal: Wife would work|0_._084*|0_._095**|0_._160***|
||(0_._040)|(0_._029)|(0_._037)|
|Num.Obs.|524|524|524|
|Covariates included|Yes|Yes|Yes|

+ p _<_ 0.1, * p _<_ 0.05, ** p _<_ 0.01, *** p _<_ 0.001 _Note:_ The top section of the table shows the coefficient estimated for the variable indicating, for female respondents, that they would ideally work outside of the home, from weighted OLS regressions using models taking as the left-hand side variable, respectively, the personal attitudes score (1), the pooled perceived norms score (2), and the pooled future perceived norms score (3). The bottom section of the table presents the same, but for the variable indicating, for male respondents, that in their hypothetical ideal household, the wife would work outside the home. Covariates are included in all models; full results are in the Appendix.

19
stock of existing governance indicators, but rather as leading examples of major indicators in this taxonomy.<sup>2</sup> A striking feature of efforts to measure governance to date is the preponderance of indicators focused on measuring various _de facto_ governance outcomes, contrasting the relative few which measure _de jure_ rules. Almost by necessity, the latter type of rules-based indicators of governance reflects the views or judgments of _experts_ in the relevant areas. In contrast, the much larger body of _de facto_ indicators captures the views both of experts as well as survey respondents of various types.

We conclude in Section 6 with a discussion of the way forward with measuring governance in a manner that can be useful to policymakers. We emphasize the importance of consumers and producers of governance indicators clearly recognizing and disclosing the pervasive measurement error in _all_ types of governance indicators. We also note that to further a constructive discussion on governance indicators it is important to move away from oft-heard false dichotomies, such as ‘subjective’ vs. ‘objective’ indicators, or aggregate vs. disaggregated ones. As we discuss below virtually all measures of governance, for good reason, involve a degree of subjective judgment. And with respect to aggregation, different levels of aggregation are appropriate for different types of analysis, and in any case this is not an either-or distinction as most aggregate indicators can readily be unbundled into their constituent components.

We also emphasize the importance of both broad public scrutiny as well as more narrow and technical scholarly peer review of governance indicators. And finally, our overall conclusion is that while there has been considerable progress in the area of measuring governance over the past decade, the indicators that exist, and the ones that are likely to emerge in the near future, will remain imperfect. This in turn underscores the importance of relying on a diversity of the different types of indicators when monitoring governance and formulating policies to improve governance.

> 2  For access to a fuller compilation of governance datasets, visit www.worldbank.org/wbi/governance/data

4
been tried but not yet rigorously evaluated. In the context of strict budget constraints, having clear data on the best investments among those interventions that have been evaluated can be useful, and it can help governments and other education stakeholders to avoid investing in programs that have proven ineffective. However, it should not stop policy makers from continuing to innovate and test new programs that relax constraints on girls’ access and learning.

# 6. Conclusion

Previous reviews of what works to improve girls’ education tend to focus on girl‐targeted interventions. That approach omits key evidence of the impact of general education interventions on girls. This review innovatively brings together a large evidence base of general interventions that report effects for girls. Based on 177 studies from 54 countries, this review finds that girls’ access to school is more responsive to changes in costs, distance, and health conditions; while girls’ learning is more likely to be improved by structured pedagogy and interventions that help teachers to teach at the right level.

While this review focuses on girls’ education, the global learning crisis impoverishes both girls and boys (World Bank 2018b). Our findings demonstrate that gender neutral interventions hold great promise for girls’ learning as well as for boys. Considering the limited resources that education systems in most low‐ and middle‐income countries possess, the most practical approach to help girls learn may be to make schools better for all children. Such an approach may also be more politically palatable to voters than programs that restrict their benefits to girls.

Finally, attending school and acquiring learning are not the finish line for girls’ education. The ultimate objective is that girls can empower themselves through education and achieve their life aspirations. To this point, very few evaluations have included either long‐term follow‐ups or these broader measures of well‐being. But gaining literacy and numeracy are the foundation for positive longer term outcomes.

# References

- Adoho, Franck, Shubha Chakravarty, Jr. Korkoyah, Dala T., Mattias KA Lundberg, and Afia Tasneem. 2014. 'The Impact of an Adolescent Girls Employment Program. The EPAG Project in Liberia. ', _Policy Research Working Paper, The World Bank_ .

- Amarante, Verónica, Mery Ferrando, and Andrea Vigorito. 2013. 'Teenage School Attendance and Cash Transfers: An Impact Evaluation of PANES', _Economía_ , 14: 61‐96.

- Aturupane, Harsha, Paul Glewwe, Renato Ravina, Upul Sonnadara, and Suzanne Wisniewski. 2014. 'An assessment of the impacts of Sri Lanka’s programme for school improvement and school report card programme on students’ academic progress', _The Journal of Development Studies_ , 50: 1647‐69.

- Baird, Sarah J, Ephraim Chirwa, Jacobus De Hoop, and Berk Özler. 2016. 'Girl power: cash transfers and adolescent welfare. Evidence from a cluster‐randomized experiment in Malawi', _Chapter in NBER book African Successes, Volume II: Human Capital (2016), Sebastian Edwards, Simon Johnson, and David N. Weil, editors (p. 139 ‐ 164). University of Chicago Press._

15
- All South Asian countries with exception of Afghanistan, Bhutan and Nepal would have large share of coal in total electricity generation. As of 2020, the share of coal‐fired generation capacity in the total installed capacity would be 19%, 60%, 34% and 16%, respectively in Bangladesh, India, Pakistan and Sri Lanka. The corresponding shares in 2040 would be 67%, 58% 45% and 37% (see Table A1b).

- Total regional capacity would increase by 3.8 folds from 276 GW in 2013 to 1,067 GW in 2040. More than 70% of the total regional capacity would be installed in India.

# **_<u>Electricity Generation (TWh)</u>_**

- Coal based generation would increase from 66% in 2020 to 71% by 2040 in the baseline;

- The share of coal‐based generation would account for 92%, 75%, 64% and 57% in Bangladesh, India, Sri Lanka and Pakistan, respectively in 2040 under the baseline.

- Total regional generation would increase by 3.5 folds from 1,486 TWh in 2013 to 5,247 TWh in 2040.

## **Table A2a: Electricity Generation (TWh)**

||**2020**|**2030**|**2040**|
|---|---|---|---|
|**Afghanistan**|1.4|26.3|31.7|
|**Bangladesh**|90.1|191.2|312.2|
|**Bhutan**|34.2|59.2|59.6|
|**India**|1,740.8|2,828.1|3,954.2|
|**Nepal**|15.0|27.3|36.5|
|**Pakistan**|207.2|442.7|808.6|
|**Sri Lanka**|19.0|30.6|44.4|
|**Total**|2,107.8|3,605.4|5,247.1|

38
crisis is more likely if a shift to a flexible exchange rate regime occurred the year before the crisis. Other regression coefficients remain consistent with the baseline specification regardless of the exchange rate regime.

# 4. Selected country case studies

The preceding section quantified how shocks and vulnerabilities have affected the likelihood of crises. However, in addition to these factors, other structural and institutional weaknesses may make an economy more prone to crises once an adverse shock strikes. These weaknesses are explored in this section in a set of selected country case studies of financial crises, which complements and expands on the earlier analysis.

This section focuses on macroeconomic policies, and structural and institutional features that relate to shortcomings in financial sector supervision and corporate governance. It also identifies other factors, including political uncertainty, balance sheet mismatches, heavily managed exchange rates, state-led growth models, heavy presence of state-owned enterprises, less diversified economies, and implicit sovereign guarantees. Individual aspects of these have been widely discussed in the literature.<sup>8</sup>

# 4.1 Methodology

The case studies focus on 43 crisis episodes in 34 EMDEs that have witnessed rapid government or private debt accumulation since 1970. Most of these cases (65 percent) involved overlapping private and government debt accumulation episodes. Almost all cases (90 percent) involved two crises, and 40 percent involved three crises. While nonexhaustive, the case studies were selected by the following criteria. First, they are representative of debt accumulation episodes over the past fifty years. Second, they include a broad range of EMDEs, including both large EMDEs in major regional debt crises episodes and LICs. Third, they have been sufficiently examined in earlier studies for a general assessment about their causes and consequences to be reached with confidence. Appendix 5 summarize four of these examples in more detail.

For each of the cases examined, earlier work—IMF Article IV consultation reports, academic studies, and policy papers—provides a wealth of information on the structural features and institutional background. The main references for the country case studies that are specifically referred to in the section are listed in Appendix 6. In general, IMF Article IV reports were the primary sources of information.

> 8 For a discussion of some of these macroeconomic, structural and institutional shortcomings see Balassa (1982); Kaufmann (1989); and Sachs (1985, 1989) on growth strategies and uses of debt; Roubini and Wachtel (1999) on current account sustainability; Daumont, Le Gall, and Leroux (2004) and Kawai, Newfarmer, and Schmukler (2005) on inadequate banking regulation; Brownbridge and Kirkpatrick (2000) on balance sheet mismatch; and Capulong et. al. (2000) for poor corporate governance.

14
# **II. Data**

The Mexican National Institute of Statistics, Geography and Information (INEGI) tabulates official income data for Mexican 32 states GDP for 1970,1975,1980,1985,1988, and then annually for the period 1993-2002. We follow Esquivel (1999), in making several corrections to this data. First, most oil is pumped from the states of Tabasco and Campeche, but the attribution of oil revenues has changed without obvious cause over time. Though the revenues are in fact allocated to all states via a federal sharing formula, in some years they were entirely attributed to Tabasco, and in others to Campeche. We tried to correct for this, excluding the oil production as captured in the mineral production category of the state accounts, but still found the resulting growth series to be too erratic and exaggerated to be credible. We attribute this behavior to unresolved petroleum accounting issues since the remaining 30 non oil producing states behave more reasonably.<sup>21</sup> Though dropping these states clearly implies losing some of the spatial story, we find it preferable than contaminating the analysis with clearly unreliable series.

Second, we also corrected populations figures for Chiapas and Oaxaca for years 1975, 1980, 1985, 1988 as the 1980 census, when compared to other household census appears to have understated the state’s population induced distortions in the GDP per capita.<sup>22</sup> .

Finally we have merged the state of Mexico with Mexico DF (Federal District), the capital. The rationale for this aggregation stems from the fact that the two states have long been part of a common industrial belt and there exist strong labor market linkages between them which may lead to an overstatement of the capital city’s per capita income. This has led to reported population in the Mexico DF has remaining stable over the last 20 years while the population in the state of Mexico doubled. We run the analysis both with and without this aggregation (results available on request) and, while the fundamental story does not change, the more moderate growth behavior of the aggregated capital city we find more plausible and we report those results.

> 21 In fact, the state of Chiapas also produces some very modest amounts of oil and we subtracted this off from the state product series in  1975 and 1980.

> 22 Population figures for years 1975, 1980, 1985, 1988 for Chiapas and Oaxaca were extrapolated using yearly population growth rates between 1970 and 1990. According to official figures, the mining production over GDP for Chiapas went from 7.5% in 1970 to 18% in 1975 up to 45% in 1980  and back to 7% in 1985. Clearly, years 1975 and 1980 saw arbitrary assignments of oil production to Chiapas. We have corrected for this as to allow the ratio mining production over GDP to be 7.5% for the outlier years .

8
# The following persons contributed to this publication:

Shawki Barg'nouti Guy Le Hoigne Brian Berman Herve L. Plusquellec Fateh H. Chaudhri Richard Reidinger William Cuddihy G. Edward Schuh Hamdy H. Eisa Paul F. Siegel John Hayward Carol F. Timmer Peter Hazell C. Peter Timmer William E. James Vijay S. Vyas The overall research project and this publication were coordinated by Shawki Barghouti. Paul B. Siegel, Carol F. Timmer and William E. James assisted in the preparation and editing.

This publication draws heavily from reports prepared for the Agriculture and Rural Development Department by G. Edward Schuh and Shawki Barghouti, Vijay S. Vyas and William E. James, C. eeter Timmer, and Brian Berman.

Other sources of information include presentations at the World Bank's Seventh Agriculture Sector Symposium by John A. Hayward, Herve L. Plusquellec and Guy Le Moigne, Peter Hazell, Hamdy H. Eisa and Richard Reidinger.

Details on rural diversification in Indonesia, Malaysia and Thailand were provided by Fateh M. Chaudhri, William L. Cuddihy and Richard Reidinger from the World Bank's Asia Region.