rafmacalaba/datause-encoder-data huggingface dataset:
controversial. As with the broader literature on international differences in saving rates, explanations for high saving in East Asia have focused on other factors.

One clue to explaining high saving rates in East Asia is that saving rates in the region have not always been high. Private saving rates in East Asia trended up continuously during the region<sup>’</sup> s high growth period from the mid-70s to the mid-1990s that preceded the Asian crisis, starting at about 15 percent and rising to about 30 percent of GDP. Though China<sup>’</sup> s (national) saving rate has been high for some time, averaging 29 percent of GDP during 1970-77, before the country<sup>’</sup> s change in economic policy launched it on its high-growth path, it also increased over time, reaching 41 percent of GDP in 1993-94, just before the exchange rate system was unified. There is little variation within the region in the pattern of saving rates over time. Overall, saving rates increased sharply in all countries except the Philippines during the two decades from the mid-1970s to the mid-1990s. Since public saving increased as well over the same period, the increase in national saving was even more dramatic than that in private saving alone.<sup>10</sup> Whereas national saving rates in Southeast Asian and Latin America were similar during the mid-70s, by 1995 the regional gap was about 20 percent. The implication of this change in saving rates over time is that high saving rates in the region do not simply reflect factors that could be captured by a regional fixed effect. Thus, although cultural factors are often cited to explain Asian saving rates, the increase in saving rates in the region over time does not support the view that high saving was driven by a cultural predisposition, but rather was induced by phenomena that varied over time. The interpretation of high saving rates in the region has indeed been linked to a variety of such factors.

An orthodox approach to explaining high saving rates in the region is typified by the World Bank<sup>’</sup> s 1993 "miracle<sup>”</sup> study. This study cites an early demographic transition, macro stability, and policies toward the financial sector as the key policy contributions to the encouragement of higher private saving. An early demographic transition tended to increase the relative size of the high-saving working age cohort and decrease dependency ratios (see also Bloom and Williamson 1997). Macro stability, in turn, was judged to contribute to higher saving rates both directly and indirectly. The direct effect operates through higher public saving rates (see also Edwards 1995), while the key indirect contribution is through the

> 10 The surge in saving in Thailand in the 1980s, for example, was largely driven by the public sector.

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# Contracting

4.12 Two acts are then required of the municipal government before its formal inclusion in the project. First, the municipal eotnscil must enact--and the mayor execute--a revised municipal tax code based upon a model provided under the program. Second, the council must approve--and the mayor sign--a project agreement with its respective state government and SEF. The project agreement specifies the municipality's obligations with respect to the provision of financial and in-kind contributions to the project, the administrative relationships between the project and the various agencies of the municipal government, and any specific procedural reforms that might be required as a condition of inclusion in the project.

# Mobilization

4.13 Mobilization begins with the arrival of the SERPRO supervisor on site. The supervisor then recrtits temporary staff, organizes office space and procures materials and equipment. The temporary staff used in the project are normally hired locally and given a 5-day training course. 121 CIATA generally prefers to hire staff with less-than-university-level educations on the grounds that university graduates are more adept at outwitting verification procedures.

# Preparation of Reference MaR

4.14 Prior to beginning field work, an initial reference map is prepared. The map, showing only street boundaries and other major geographical features, is used to organize subsequent field work and to provide the base for more detailed block maps. The initial map is assembled from existing parcellary maps, subdivision plans, and road and utility maps. Once the temporary staff has been recruited, survey teams then go to the field to verify this information, and sketch and measure streets not identified from existing sources. These are then incorporated on the reference map. The reference map, prepared at a scale of 1:5,000 is then divided into sections, which are expanded to a scale of 1:1,000 and printed on separate sheets.

# Field Work

4.15 Property Identification: This phase of the field work has two objectives. The first is to identify each property and assign it an assessment number. For this purpose, the survey team determines only the boundaries of the front footage of each lot. Boundary points are sketched on the section maps, and an assessment number is assigned to each segment of frontage.

4.16 ProRerty Characteristics: The second objective is to gather data on property characteristics, to be subsequently used in calculating individual property valuations. The characteristics of each property are coded on a standard record (see Annex 1). The record lists: (i) the assessment number _IV Their fLeldwork Ls verified by having a sample of blocks rechecked. CIATA also uses a template to check property records for Implausibilities (for example, a thatched roof on a building reported to have floors of cera4ic tile)._
Table 2: Pre-FPE Compositional Differences between Control and Treatment groups

|Vibl<br>|Treatment|Control|p-value|Dif (_X_1_−X_0)|Norm-dif|
|---|---|---|---|---|---|
|arae|(1)|(2)|(3)|(4)|(5)|
|OWM|58.5060<br>|61.0930<br>|0.0000|-2.5869|-0.1866|
||(10.9261)|(8.5287)||||
|APS|19.2063<br>|11.2710<br>|0.0000|7.9353|1.5799|
||(3.9627)|(3.0859)||||
|Child Age|21.3088<br>|22.1000<br>|0.0000|-0.7912|-0.1239|
||(4.7751)|(4.2376)||||
|Gender (female)|0.5920<br>|0.5651<br>|0.0270|0.0269|0.0384|
||(0.4915)|(0.4958)||||
|_School or Home District_||||||
|Berea|0.0699<br>|0.0817<br>|0.0694|-0.0118|-0.0315|
||(0.2550)|(0.2740)||||
|Botha-Bothe|0.0717<br>|0.0573<br>|0.0177|0.0144|0.0413|
||(0.2580)|(0.2324)||||
|Leribe|0.1863<br>|0.1963<br>|0.3017|-0.0100|-0.0180|
||(0.3894)|(0.3973)||||
|Mafeteng|0.0991<br>(0.2989)|0.1005<br>(0.3007)|0.8549|-0.0013|-0.0031|
|Maseru|0.3729<br>|0.3579<br>|0.2040|0.0151|0.0221|
||(0.4837)|(0.4794)||||
|Mohale’s Hoek|0.0801<br>|0.0801<br>|0.9988|-0.000|-0.0000|
||(0.2716)|(0.2715)||||
|Mokhotlong|0.0304<br>(0.1718)|0.0288<br>(0.1673)|0.6990|0.0016|0.0067|
|Qacha’s Nek|0.0158<br>(0.1247)|0.0169<br>(0.1289)|0.7214|-0.0011|-0.0062|
|Quthing|0.0298<br>(0.1701)|0.0316<br>(0.1750)|0.6736|-0.0018|-0.0073|
|Thaba-Tseka|0.0088<br>(0.0933)|0.0066<br>(0.0808)|0.3069|0.0022|0.0178|
|_Faculty_||||||
|Agriculture|0.0374|0.0632|0.0000|-0.0258|-0.0836|
||(0.1899)|(0.2434)||||
|Education|0.1778<br>|0.2696<br>|0.0000|-0.0917|-0.1566|
||(0.3824)|(0.4438)||||
|Health Sciences|0.0541<br>(0.2262)|0.0692<br>(0.2538)|0.0107|-0.0151|-0.0444|
|Humanities|0.0758<br>(0.2646)|0.1265<br>(0.3324)|0.0000|-0.0507|-0.1194|
|Law|0.0430|0.0356|0.1275|0.0073|0.0266|
||(0.2029)|(0.1856)||||
|Science & Technology|0.1272<br>(0.3333)|0.1234<br>(0.3289)|0.6346|0.0039|0.0083|
|Social Sciences|0.2401|0.3125|0.0000|-0.0723|-0.1147|
|Observations|(0.4272<br>3419|(0.4636)<br>3194||||

_Source_ : Own calculations using NUL students’ records data for the academic years 2010, 2011, 2012, 2013, and 2014. _Notes_ : OWM is the overall weighted mean. The treatment group is all students in their first year of university studies. Standard deviations are in parentheses. The reported p-values are for tests of equality of means between the treatment and control groups (independent samples). _Diff_ stands for difference in means by treatment status. ̄Norm-diff means normalized differences between treatment and control means computed as _△_ **X** = _~~√~~_ _<u>XS</u>_ <u>10</u> _−_<sup>2+</sup> _<u>X</u>_<sup> ̄</sup><sup>_S_</sup> <u>01</u><sup>2,where</sup><sup>_S_</sup> _G_<sup>2isthesamplevarianceof</sup><sup>_Xi_in</sup> subsample with treatment _Gi_ = _{_ 0 _,_ 1 _}_ . _G_ = 1 if a student is in second year.

18
24 Evaluation studies need to be independent of those who have a direct stake in the success of the endeavor, with evaluation teams in which there are no preconceived positions on the innovation being reviewed. While most sponsors of evaluative studies are aware of the need for evaluators’ independence, one encounters too often evaluation teams purposefully selected by the concerned stakeholders so as to include scholars who have already expressed themselves favorably on the idea being studied, and this can compromise objectivity. In situations where attribution of impact is analytically difficult, a full evaluation will require quantitative work by qualified professionals to complement qualitative assessments. In many cases such econometric skills will be drawn from academia and research institutes, where they more commonly reside.
_2_

# **I. Introduction**

Sub-Saharan Africa has the highest rate of female entrepreneurship in the world, with more women starting businesses in Africa than anywhere else (World Bank, 2012). Although women now make up approximately half of the entrepreneurs in Sub-Saharan Africa, there is still a large gender gap in business performance (see for example, Klapper and Parker 2011 or Bardasi et al. 2011 for a review of the literature). The latest data from the World Bank Enterprise Surveys (2015) indicates that male-owned businesses in Ethiopia are 2.7 times larger than femaleowned enterprises in terms of number of employees and have average monthly sales that are 4 times higher.<sup>1</sup> The existing evidence that explains the gender gap in business performance highlights a number of potential factors, including capital and time constraints. Campos and Gassier (2015) emphasize that the systematically different choices that men and women make, which are in turn driven by gender-specific constraints, can explain gender differences in business performance. One key difference they highlight is the sectors in which men and women choose to operate their enterprise. The sector of operation has been identified as an important determinant of returns for female entrepreneurs in many developing countries. De Mel, McKenzie, and Woodruff (2008) find that women are more likely to be present in sectors characterized by low returns, and Singh, Reynolds, and Muhammad (2001) find a high concentration of female-owned firms in low-income informal sectors. These findings raise the question of whether there are certain sectors that women could choose where potential returns are higher. If so, are there any defining characteristics of the women who are more likely to be operating in these sectors that could help direct programming and targeting?

In this paper, we use enterprise data collected from Ethiopia to explore what could be driving sector selection in the context of growth-oriented female entrepreneurs. Specifically, we study female-owned enterprises that operate in sectors dominated by men and show that, on average, they are able to make higher returns than female-owned enterprises operating in female-concentrated sectors. Our findings are consistent with the results of Campos and colleagues (2011) in Uganda, who found that women-owned firms in male-dominated sectors, on average, were more profitable than firms in female-concentrated sectors. In this paper, we use data from Ethiopia to corroborate the results obtained in Uganda by testing the hypothesis of higher returns for women in male-dominated sectors and then seek to uncover factors correlated with crossing over into these higher-return male-dominated sectors.

Comparing the average outcomes of crossovers and noncrossovers suggests that entering and operating in a male-dominated sector leads to higher profits, indicating that encouraging female entrepreneurs to enter sectors that are dominated

> 1A “female-owned” business was defined as one in which the top manager was a woman. In the 2015 Ethiopian enterprise survey, 9 percent of the 848 businesses indicated that their top manager was a woman.
# Research Working Paper No. 726, The World Bank.

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45
**Table** **<u>6.  The Empty Model Public and Private Schools</u>**

||**Pu**<br>|**blic Schools**<br>||**Public and Private Schools**<br>|
|---|---|---|---|---|
||**National**|**Urban**|**Rural**|**National**|
|Total students' scores variance|48.35|56.26|46.67|48.99|
|Variance within the schools|23.82|24.67|23.15|24.08|
|Variance between the schools|24.54|31.59|23.52|24.90|
|Variance component ratio of school effect|0.51|0.56|0.50|0.51|
|Number of students|19,419|11,256|8,163|23,955|
|Number of schools|1,586|744|842|1,909|

Source: Authors' estimates using the Primary Education Assessment, second round 1997, SEP.

# _(2) Model 2 with school dummies and students' characteristics:_

In order to have greater precision in the estimation of the students' effects on the learning achievement, several variables were introduced at the student level, including student's gender, age, pre-school education, repetition of 5th grade, blurred vision, teacher performance, student attitude towards learning, household size, household income, household utilities, number of books in house, number of rooms in house, parent schooling level, parent expectations of student educational achievement and parent opinion of educational services in the school. The variables were entered individually to test whether the coefficients remained robust. The model is described by the following equation: where,

- _Yij_ Vector of individual student test scores, Mathematics.

γ00 Overall mean of achievement.

- _Bh_ Vector of parameters to estimate ; 1, .., H .

- _X ij_ Matrix of student's socioeconomic background variables.

- _dk_ The dummy variables that indicate schools in the sample.

- γk The deviations of achievement of the "k" school around the overall average conditioned on students’ characteristics.

- ε ij The deviations of students scores around the overall average.

14
# **Figure 2.1 Evolution of uncertainty in EMDEs**

_While financial market uncertainty, defined in terms of stock market volatility, has declined in most EMDEs, policy and political uncertainty increased from the pre‐crisis to the post‐crisis period for most EMDEs. Generally low post‐crisis financial market volatility was interrupted by several bouts of global financial market volatility._ **A. Share of EMDEs with higher uncertainty in post‐ B. Evolution of uncertainty crisis than pre‐crisis.** <!-- Start of picture text --> Percent Index, 2002Q1=0<br>100 Financial market uncertainty<br>6 Political Risk<br>80 EPU<br>VIX<br>60 3<br>40<br>0<br>20<br>-3<br>0<br>Financial market Political risk EPU<br>uncertainty 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> Sources: Baker, Bloom, and Davis (2016); Bloomberg; Haver Analytics; International Country Risk Guide; World Bank estimates.

Notes: 33 countries for measure based on standard deviation of daily stock market changes; 102 countries for ICRG political risk score; and 4 countries (Brazil, China, India, and Russia) for the EPU measure. Financial market uncertainty refers to realized standard deviation of daily changes in stock price changes. Political risk refers to the ICRG political risk index (adjusted such that higher index denotes higher risk).

A. Pre‐crisis and post‐crisis refer to 2003‐08 and 2010‐15, respectively. To exclude data for the spike in global financial market uncertainty in the wake of the bankruptcy of Lehman Brothers, pre‐crisis average for financial market uncertainty excludes 2008. Last observation is for Q1 2016.

B. All series are normalized to standard deviation of 1.

# **Figure 2.2 Financial market uncertainty and investment in EMDEs**

_Rising global financial market uncertainty, as captured by the VIX, reduces EMDE investment. Accommodative monetary policy by major central banks has reduced financial market uncertainty._

**A. Investment response to a 10 percent increase in the VIX B. Contribution of the VIX to EMDE investment growth**

<!-- Start of picture text --> Percentage points Percent<br>0.0 16 VIX Others Actual<br>12<br>-0.5<br>8<br>-1.0 4<br>0<br>-1.5 -4<br>-8<br>-2.0<br>1 2 3 4 5 6 7 8<br>Quarter 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: Bloomberg, Haver Analytics, World Bank estimates.

Note: Vector autoregressions are estimated with sample for 1998Q1‐2016Q2. The model includes, in this order, the VIX, MSCI Emerging Markets Index (MXEM), J.P.Morgan Emerging Markets Bond Index (EMBIG), aggregate real output and investment growth in 18 EMDEs with G7 real GDP growth, U.S. 10‐year bond yields, and MSCI World Index as exogenous regressors and estimated with two lags. Estimates for 2016 are based on the first half in 2016 (annualized).

A. Shows cumulative responses of EMDE investment to a 10 percent increase in the VIX. Solid lines indicate the median responses and the dotted lines indicate 16‐84 percent confidence intervals. B. Indicates historical decomposition to EMDE investment. “Others” indicates other EMDE and global factors, including stock and bond price index.

47
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