rafmacalaba/datause-encoder-data huggingface dataset:
domestic work, etc.). Others find difficulties entering the labor force and become discouraged. Several young people in the labor force are also found unemployed, searching for work.

The standard labor market indicators include the unemployment rate (UR), the employment rate (ER), and the labor force participation rate (LFPR). Box 1 provides the definitions of these states. To be considered unemployed, an individual must not be employed but actively seeking work. The LFPR is the share of the population that is either employed or unemployed, and the unemployment rate is the share of the labor force that is unemployed. The employment rate is the share of the population that is employed. Because the unemployment rate is measured with respect to the labor force while the employment rate is measured with respect to the population, the two will not be perfectly inversely correlated. In practice, it is can be difficult to distinguish between being unemployed and not being in the labor force because verifying job search for the unemployed is costly. It can even be difficult to distinguish between being employed and being out of the labor force because individuals employed without formal contracts such as in day labor situations or who work for household enterprises may not be counted in official labor market statistics.

# **Box 1 Definitions**

- Population (P): The civilian, non institutionalized population aged 15-24 that is potentially available for work

- Employed (E): During the reference period*, the respondent was either selfemployed, working for others for pay, or working for a household enterprise without pay.

- Unemployed (U): Not employed but actively seeking work during the reference period.

- Labor Force (L = U + E):  The sum of employed and unemployed.

- Not in the labor force (N = P-L):  The rest of the population not in the labor force.

- Unemployment Rate: UR = U/L

- Employment Rate: ER = E/P

- Labor Force Participation Rate: LFP = L/P

- Home Status Rate: HR = {N – [S&N]}/P

- Jobless Rate: JR = HR + [U/P] = [P – E – S&N]/P

- School Enrollment Rate: SR = S/P

- School and Work Rate: S&LR = (S&L)/P

* Reference period: In labor market surveys, questions regarding respondent time allocation are typically framed with respect to activities in the past week, the past month, or the past year.

Two other measures are used in developing country settings, the home status rate (HR) and the jobless rate (JR). The home status rate is the proportion of the population that is neither in the labor force nor in school. It measures the group that has left school 4
**Table** **<u>8. Determinants of Mathematics Achievement Scores in 5</u>**<sup>**th**</sup> **<u>grade at National Level</u>**

||**Public**|**and Privat**<br>**Ll f**|**e Schools**||**Public Sch**<br>**Ll f**|**ools**|
|---|---|---|---|---|---|---|
||**Coeff.**|**eve o**<br>**Sig.**|**Elasticity **|**Coeff.**|**eve o**<br>**Sig.**|**Elasticity**|
|Student Socioeconomic Index|0.485|0.000||0.485|0.000||
|Teacher's gender (Male)|-0.675|0.023|-0.0072|-0.916|0.015|-0.0103|
|Teacher's age|0.190|0.095|0.0183|0.280|0.070|0.0270|
|Attendance to updating courses (Yes)|-0.931|0.074|-0.0171|0.416|0.476|0.0077|
|Teacher's residence within the community (Yes)|-0.052|0.890|-0.0004|-0.102|0.801|-0.0008|
|Teacher's years of residence in the community|0.240|0.027|0.0261|0.135|0.261|0.0148|
|Teacher's schooling level|0.139|0.294|0.0103|0.219|0.183|0.0163|
|Teacher's pedagogical behavior|0.053|0.034|0.0052|0.194|0.015|0.0041|
|Teacher's interest in students' learning|0.288|0.023|0.0098|0.092|0.003|0.0031|
|Number of updating courses|0.028|0.584|0.0030|0.021|0.709|0.0023|
|Type of post. Short term (Yes)|-1.210|0.030|-0.0013|-1.177|0.014|-0.0013|
|More than one post (Yes)|-0.004|0.990|0.0000|0.304|0.395|0.0014|
|Teacher's income|0.135|0.225|0.0097|0.094|0.475|0.0069|
|Didactic material available to the teacher|0.011|0.608|0.0033|-0.004|0.878|-0.0011|
|Number of supervisor visits (as Director's answer)|5.523|0.000|0.0754|5.484|0.000|0.0780|
|Teacher's enrollment in Carrera Magisterial (Yes)||||1.436|0.003|0.0187|
|Carrera Magisterial level||||-0.413|0.056|-0.0072|
|Correction of self-selection bias at stage 2|6.886|0.222||8.146|0.154||
|Correction of self-selection bias at stage 3|-9.769|0.054||-12.472|0.014||
|Correction of self-selection bias in Carrera Magisterial||||1.674|0.182||
|Constant|45.854|0.000||44.873|0.000||
|R^2|||0.388|||0.377|
|Number of Students|||14847|||13,767|
|Number of Schools|||1718|||1602|

Source: Authors' estimates using the Primary Education Assessment, second round 1997, SEP. n.a. Not applicable.

# **_General Results_**

In general, students with teachers that have more years of experience (using age as a proxy) achieve higher scores in Mathematics. It is clear that teacher experience and seniority improve student achievement growth rates, suggesting that teacher proficiency is enhanced by practical experience and training. The marginal productivity of time spent in formal education of teachers on teacher effectiveness is statistically insignificant. But the potential of training to contribute to the improvement of teaching effectiveness appears high. The following findings show: the importance of teachers’ experience and practice; teacher ability to deal with children’s questions and doubts intelligently (implying the importance of teachers’ subject matter knowledge), and teacher effectiveness in monitoring students' performance or difficulties and talking to students.

18
**Table 12: Descriptive statistics on ever-married women by current marital status, Senegal 2005 DHS**

||% of ever-<br>married<br>women 15-49|Age|H’hold<br>head|BMI|% Under-<br>weight (at<br>mean age)|DHS ass<br>Urban|et index<br>Rural|Received most<br>of husband’s<br>property|Born rural,<br>lives urban|Born urban,<br>lives rural|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Once-married|75.7|29.7|5.8|22.75|11.6|1.06|-0.54|--|8.3|4.8|
|Remarried widow|4.4|38.8|19.9|24.35***|8.50|0.67***|-0.57|16.0|6.1|5.6|
|Widow|1.5|38.5|32.2|25.35***|12.0|1.10|-0.41|30.9|14.1***|2.2*|
|Remarried divorcee|13.1|34.3|8.9|24.99***|10.3|0.93***|-0.43***|--|13.1***|6.0|
|Divorced|5.4|32.4|14.6|23.20*|10.3|1.35***|-0.30***|--|12.7***|3.1**|

Note: All characteristics are expressed as percentages of the marital status group except for age (years), BMI and the wealth index. All significance tests are relative to married once women, where *** p<0.01, ** p<0.05, * p<0.1. Underweight is defined as having BMI less than 18.5. Column (6) presents mean underweight conditional on age and aged squared and evaluated at mean age for the sample as a whole. Differences relative to once-married women are no longer statistically significant. Pregnant women are omitted from the BMI and underweight means. Divorced includes separated women. The Wealth index is generated by DHS using principal components analysis on assets; housing construction materials; and type of water access and sanitation facilities. The index places households on a continuous scale of relative wealth and refers to the household to which the woman belongs. Sample: ever-married women, aged 15-49 Source: Authors’ calculations using Senegal’s 2005 DHS.

**Table 13: Measures of women’s decision-making and access to resources by marital status, Senegal 2005 DHS (%)**

||<br>Own health<br>care|Has no say on:<br>Large hh<br>purchases|Visits to<br>family|Constraints<br>health<br>Permission|on seeking<br>care:<br>Cost|Own earni<br>on hous<br>None|ngs spent<br>ehold:<br>> half|Beatin<br>If argue|g justified<br>Refuses<br>sex|Never watches<br>TV|
|---|---|---|---|---|---|---|---|---|---|---|
|Once-married|81.5|83.8|66.8|6.0|55.0|33.2|16.4|51.6|49.8|32.3|
|Remarried widow|66.7***|67.9***|50.3***|4.5**|70.7***|16.8***|34.0***|56.9|58.8***|43.1***|
|Widow|32.7***|44.4***|31.3***|2.1**|68.1***|33.8|44.3***|40.6|48.1|21.0**|
|Remarried divorcee|73.2***|76.7***|62.4***|4.8**|58.9***|31.1|20.7***|50.7**|46.9***|26.7***|
|Divorced|46.5***|70.3***|50.9***|4.9***|59.6***|38.1***|21.4*|38.3***|36.4***|15.5***|

Note: The table shows the % of women in each marital status answering positively to each question. “Has no say” is defined as answering that each decision is taken by either the husband/partner alone or by someone else. Divorced includes separated women. Significance tests are relative to once-married women, where *** p<0.01, ** p<0.05, * p<0.1.

Sample: ever-married women, aged 15-49 Source: Authors’ calculations using Senegal’s 2005 DHS.

43
mortality has dropped faster than in any other country in South Asia and gender differences in infant mortality have disappeared, unlike patterns in its neighboring countries. The total fertility rate nearly halved from 6.1 between 1971 and 2001 and the microcredit revolution sweeping the countryside has given women visibility and greater status. Better water and sanitation facilities have reduced the drudgery of mothers who now have time for other activities. An information and communication boom has accompanied use of radio, television and mobile phones. A better roads network has allowed women to move out of their villages to jobs in town through more secure modes of transport and given them greater mobility (World Bank, 2007).

The progress recounted above is not meant to undermine the serious problems that remain and new ones that are surfacing. So, while women’s status has improved dramatically form the last few decades, gender inequalities in many other areas are persistent such as access to markets, political spaces and high tech services. Moreover, there are serious differences by area of residence, wealth quintiles and ethnicity. The practice of dowry is on the rise, and one of the reasons why girls are married off on average by the time they are 15 years old.

Previously we described the extent to which education has expanded in Bangladesh and the manner in which it has trumped the conservative marriage market with increasing numbers of girls compared to their mothers’ generation marrying men less educated than them. Clearly then, demand for education is not only contingent on cultural reasons and has some important structural correlates. Bangladesh for the last two decades followed a concerted policy to enhance girls’ education through innovative incentive schemes that provided stipends to secondary school girls for remaining in 6
This note is concerned with the effect of new PPP estimates on the evolution of international and global inequality. In the first section, I will briefly explain some key characteristics and results of the 2005 round of ICP. Section 2 will present the new estimates of international and global inequality based on these results.<sup>4</sup> Section 3 concludes the note.

# **1. International Comparison Program, 2005 Round**

The international comparison program is a joint UN-OECD-World Bank-regional development project that, at approximately decennial intervals (the last survey was conducted in 1993), covers the entire world with the objective of determining, from direct price comparisons of about 1000 goods and services, price levels within nations.<sup>5</sup> The latest ICP was the largest such project in the history of ICP, and probably the largest worldwide single empirical economic project ever undertaken.

The 2005 estimates of purchasing power parity (PPP) exchange rates for 146 countries are not only the most recent and best estimates that we have, and the survey, “the most extensive and thorough effort ever to measure PPPs across economies” (World Bank, 2008, p. 9), but for a number of countries they are the first such estimates obtained from direct price comparisons. The number of participating countries has increased from 118 in the previous round (1993) to 146 now. China has for the first time participated in the ICP. Previous estimates of Chinese PPP exchange rates were based on 1983 and 1995 research papers (Ahmed 1983, and Rouen and Chen, 1995). Similarly, India has participated for the first time since 1985. Up to now, the PPP estimates for India were based on the extrapolations of the 1985 results. The price comparisons now include 48 African countries, more than ever before.

> 4 An initial estimate of global inequality with new PPPs for the year 2002 only was published by Milanovic (2008).

> 5 One can consider  the first global ICP to have been the one conducted in 1980.  The earlier,  much smaller rounds, limited to developed economies, were done in 1970, 1973 and 1975.

3
Policy Research Working Paper 6682

# **Abstract**

Recent economic and political developments have highlighted a challenge shared across the Arab region of generating employment, promoting inclusive growth, and improving competitiveness. In the short run, weakened macroeconomic fundamentals in the developing economies of the Middle East and North Africa are a key challenge. The region’s main challenge is to achieve sustainable growth that delivers the quantity and quality of jobs needed. An inclusive and competitive private sector has proven to be one of the most effective and long-term solutions for this challenge. This paper provides an analytical framework to diagnose and identify key challenges to the growth of small and medium enterprises that is supported by a quantitative model based on the World Bank’s Enterprise Surveys database. The findings reconfirm that the route to a sustained role for small and medium enterprises in job creation requires improving the credibility of reforms, the effectiveness of policies, and equitable enforcement. Although one size fits all is infeasible for Arab countries, it is important to design policies across sectors to create productive employment and promote economic growth. Supporting innovation and enhancing access to finance are central to the development agenda for small and medium enterprises. And creating an enabling environment and setting up accountable institutions are key to ensure equal opportunity and inclusive growth.

This paper is a product of the Financial and Private Sector Development Unit, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at snasr@worldbank.org and arostom@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
implementation, sampling and quality control in most client-countries of the World Bank. The Enterprise Surveys currently cover over 130,000 firms in 125 countries, of which 116 have been surveyed following the standard methodology. This allows for better comparisons across countries and across time. Hundreds of academic research papers as well as policy documents produced by the World Bank Group and other organizations use these data. Of the 116 countries surveyed under the global methodology, 40 are in Sub-Saharan Africa, 29 are in Eastern Europe and Central Asia, 31 are in Latin America and the Caribbean, 10 are in East Asia and Pacific, 4 are in South Asia, and only two in the Middle East and North Africa (Table 1).<sup>1</sup> ES has included some high-income countries as comparators mostly as an exception since the mandate of the World Bank Group focuses on the developing world.

ES study a representative sample of the non-agricultural, formal, private economy with a strong emphasis on building panel data to make it possible to track changes in the business environment over time. In this paper, however, the panel dimension is not explored yet. The ES facilitate linking firm performance and other firm characteristics with the business environment while assessing the constraints to private sector growth and job creation faced in a particular country. The questionnaire covers the following topics:

1. Firm characteristics – covering variables such as firm age, firm legal status, gender of the owner.

2. Quality and availability of infrastructure and related services – covering variables such as number of power outages, the time to get an electricity connection or water connection.

> 1 In MENA only Yemen and Iraq has been implemented using the global methodology up to now and therefore the results for this region should not be interpreted as representative of the whole region.

5
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In order to address the potential endogeneity in the model, we instrument the EBA Regulation index with the legal origin of a country’s commercial code or company law.<sup>3</sup> This reflects the overall substantive and procedural aspects of a legal system, and therefore is fundamentally related to the complexity of legal aspects measured under the EBA Regulation index. Meanwhile, it is arguable that legal origin, usually established centuries ago, does not have a direct impact on individuals’ recent financial behavior. Column (3) in table 4 shows that the effect of more a comprehensive and supportive regulatory framework on financial inclusion remains positive and significant under the instrumental variable method.

**Table 4.** Regression results

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|FINC|FINC|FINC|
|REGULATION|0.00926<br>||0.590***<br>|
||(0.00676)||(0.0235)|
|REGULATION||||
|_--II quartile_<br>||0.358<br>(0.267)<br>||
|_--III quartile_||0.654**||
|||(0.285)||
|_--IV quartile_||0.757**||
|||(0.309)||
|FEMALE|-0.266***|-0.267***|-0.141***|
||(0.0559)|(0.0567)|(0.0136)|
|AGE|0.101***|0.101***|0.0515***|
||(0.0101)|(0.0102)|(0.00215)|
|AGESQUARE|-0.000975***|-0.000980***|-0.000499***|
||(0.000127)|(0.000128)|(2.33e-05)|
|EDUCATION|0.898***|0.900***|0.453***|
||(0.0742)|(0.0689)|(0.0172)|
|INCOME||||
|_--II quintile_|0.109**|0.110**|0.0601***|
||(0.0428)|(0.0427)|(0.0226)|
|_--III quintile_|0.390***|0.389***|0.203***|
||(0.0506)|(0.0505)|(0.0224)|
|_--IV quintile_|0.644***|0.649***|0.344***|
||(0.0634)|(0.0646)|(0.0223)|
|_--V quintile_|1.120***|1.128***|0.600***|
||(0.0744)|(0.0747)|(0.0228)|
|LOGGDPCAP|1.079***|1.079***|0.567***|
||(0.105)|(0.112)|(0.0117)|
|LOGPOP|-0.0966|-0.144*|-0.266***|
||(0.0805)|(0.0801)|(0.0117)|
|Constant|-10.31***|-9.469***|-2.621***|
||(1.275)|(1.251)|(0.238)|
|Regional FE|Yes|Yes|Yes|
|Observations|60,490|60,490|60,490|
|No. of Countries|58|58|58|
|R-squared|0.266|0.271||
|Robust standard erro|rs in parentheses|||
|*** p<0.01, ** p<0.|05, * p<0.1|||

> 3 Our legal origin variable categorizes countries into three groups: common law, civil law and socialist.

11
- 2CN -

into the objective function immediately. After the cost-minimizing combination of inputs has been determined, the dominant firm is assumed to choose its optimal quantity of output in such a way as to make its after-tax profit equal to its profit prior to the tax change, as stated in equation (21):

- _-_

- (21) _(.Pyt) (Yc) dv'(fs) (-) - (i,)(bt) (At)_ (u,,) (I&) - _ap(Pppd (Kpt) - '5k(,Pt) (Kkt;) - CZTP, =_ - -

- _(Pyt) (Yto) - Gt(-) isv)(bt) (At)_ (upt) _(ffc) -_ 8P(Ppd) _(Kpt) - 6 k(Pk)_ **_(Kke)_**

where Y* and Go are the firm's optimal output and minimum variable cost function prior to the imposition of the CIT. Augmenting the objective function (1) by borrowing constraint (4), entry constraint (18) and shifting assumption (21), we obtain the following Lagrangean:

- co

- (22) _L =_ e E _Da,,:(Py,t) (Yt) - Gt(fs)_ **_()_** _-_ (it, _t..) (At)_ - _(upt) (le) (P,:t) (Ipt)_ - **_(ikc) (Ikt)_** + _DAt,:_ - _CITPt_ - _,_ - _(Ppt,,) ' (Kpt,1_<sup>_)_+</sup><sup>_(Ppt)(Kpt)_-</sup> _(Pkt,1 (Kkt,l + (Pkt)_ **_(K)J_**

- - [DRt,: - **_c,_** (Y+ - Yc) I

- _-k.5 [(Pyt) ( (Yt) - G't (fs) ( -(it, t,.) (be) (At) - (upt) (Kt")_

- - _8V(PPd) (Kpt) - 8 k(Pkd) (Kkt) CXTPt: (Pyt:) (Yt,:)_ + _G"(-)_ + _(it, t4.) (be) (At:)_ + _(u..) (14)_

- + _8p(Pd)_ (Kpc) _+_ **_ak(Pk)_** _(Kk,)])_

Differentiating (22) with respect to Y, and setting the derivative equal to zero, we obtain the following first-order condition: _t23) Tst_ **_Y_** _' t(fS_ + _Py -_ T t ]1- )-kt° _dYt" d,(.fs) Y(fs)t_ = 1 _by_ (17) In the absence of the CIT kS = 0 and Tct = 1, and if the dominant firm does not use limit pricing to deter entry, k4 = 0 as well. In that case equation (23)