rafmacalaba/datause-encoder-data huggingface dataset:
.17

**Table 2:** Initial Conditions, Policy **and** Long-run Growth, 1965-89

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|**Dep**Var.|GR6589|GR6589|GR6589|GR6589|GR6589|GR7089|
|GDP65<br>HKS65<br>M3Y65<br>CPY65|-1.038<br>(0.44)*<br>0.396<br>(0.159)*<br>0.037<br>(0.019)*<br>-----|-1.308<br>(0.646)*<br>0.420<br>(0.165)*<br>-----<br>0.054<br>(0.030)*|-1.288<br>(0.426)*<br>0.365<br>(0.171)*<br>0.038<br>(0.021)*<br>------|-1.295<br> <br>(0.419)*<br>0.436<br> <br>(0.159)*<br>0.037<br>(0.019)*<br>-----|-1.2?<br>(0.414)*<br>0.397<br>(0.165)*<br>0.038<br>(0.018)*<br>------|-1.327<br>(0.488)*<br>0.479<br>(0.179)*<br>0.040<br>(0.021)*<br>------|
|CGY65|-----|-0.003<br>|------|-----|------|------|
||_____|(0.024)|||||
|IY6589|------|------|0.077<br>(0.033)*|0.061<br>(0.022)*|0.06<br>(0.023)*|0.049<br>(0.027)*|
|GP6589|------|------|-0.467<br>(0.466)|------|------||
|XY6589|------|------|0.001<br>(0.022)|-----|------|-----|
|INFL6589|------|------|0.0004<br>(0.011)|------|------|-----|
|SEGRY6589<br>BMP6589|------<br>------|------<br>------|-0.286<br>(0.131)*<br>-0.0004<br>|-0.258<br>(0.088)*<br>------|-0.217<br>(0.096)*<br>------|-0.328<br>(0.097)*<br>-----|
||||(0.002)||||
|DA<br>DLA<br>DR|-1.202<br>(0.576)*<br>-0.152<br>(0.70)<br>-----|-1.48<br>(0.584)*<br>-0.303<br>(0.584)<br>-----|-1.017<br>(0.528)*<br>0.567<br>(0.814)<br>-----|-1.092<br>(0.481)*<br>0.652<br>(0.743)<br>------|-0.982<br>(0.485)*<br>0.581<br>(0.798)<br>0.641<br>(0.623)|-1.137<br>(0.607)*<br>0.629<br>(0.843)|
|BSRCB6589|-----|-----|-----|-----|0.003||
||||||(0.035)||
|c|0.206<br>(0.662)|0.195<br>(0.747)|0.008<br>(1.237)|-1.050<br>(0.822)|-0.538<br>(0.982)|-0.983<br>(0.990)|
|Adj. R2<br>S.E.R<br>Obser.|0.292<br>1.469<br>52|0.287<br>1.474<br>52|0.391<br>1.363<br>52|0.426<br>1.323<br>52|0.417<br>1.333<br>52|0.378<br>1.535<br>52|

Notes: Coefficient. in parentheses are the standard errors of the coefficients; * shows the significance of the coefficient at the 5% level; SER is the standard error of regression.

Definition of variables: GR6589 (GR7089) is the annual average real GDP per capita growth rate for the period 1965-89 (1970-89); ODP65 is the log of real GDP per capita in 1965; U565 is the estimated years of schooling in 1965; 33Y65 is the ratio of total assets of **the** financial system to GDP in 1965; CPY65 is the ratio of private sector credit to GDP in 1965; CGY65 is the average ratio of government credit to GDP in 1965; 1Y6589 is the average ratio of real investment to GDP for the period 1965-89; 1Y6589 **is** the average ratio of export to GDP for 1965-89; **lNFL6589 is** the annual average inflation rate for 1965-89; 5E0RY6589 is the average ratio of seignorage revenue to GDP (defined as the change in claims of the central bank on government to GDP in the same year) for 1965-89; B3P6589 is the average black market exchange rate premium over the official rate for 1965-89; DA and DLA are dummy variables for Africa & Latin America; DR is dummy variable for real interest rate (1 for positive and 0 for negative); BRC36589 **is** the average ratio of commercial banks' reserves with the central bank to total financial assets for 1965-89.
# **Are minimum wages and payroll taxes a constraint to the creation of formal jobs in Morocco?**

Diego F. Angel-Urdinola, Abdoul Gadiry Barry and Jamal Guennouni The World Bank and _Le Haut Commissariat au Plan du Maroc_<sup>1</sup> JEL Classification: J21, J23, J31, J32, J38, J62, J64, J65

**Keywords** : Minimum wages, payroll taxes, informal sector, job search, youth employment

**Corresponding Author:** Diego F. Angel-Urdinola (dangelurdinola@worldbank.org)

> 1 This study was prepared as a background paper for the World Bank’s Morocco Country Economic Memorandum: “ _Le Maroc à L’horizon 2040: Les Ingrédients d’un Rattrapage Economique Accéléré_ ”. The authors acknowledge the technical support of David Robalino and Michael Weber who provided the authors with access to the structural microsimulation model, SimPLE (Simulation of Policies in Labor Economics). The authors benefited from valuable comments, strategic guidance, and support from Marie Francoise Marie-Nelly, Shantayanan Devarajan, Setareh Razmara, and Jean-Pierre Chauffour. M. Jamal Guennouni is with the _Haut Commissariat au Plan_ (Morocco).
<!-- Start of picture text --> 3,000 14<br>c_<br>ra)22,500 ae 12<br>2,000 me Ueee eet 10<br>i) sa eee .<br>S 1,500 wooo esr 8 ==<br>=)“6€Cc<br>#§ 1,000 4<br>z 500 5<br>wu<br>0 0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>—— GDP (left axis) ——— GDP growth (right axis)<br><!-- End of picture text -->
**Table 17** : The Gini coefficient in rural areas

|State|1983|1993-94|1999-00|2004-05|2009-10|
|---|---|---|---|---|---|
|Uttar Pradesh|0.29|0.278|0.246|0.286|0.356|
|Maharashtra|0.283|0.302|0.258|0.308|0.268|
|Bihar|0.255|0.222|0.207|0.205|0.226|
|Andhra Pradesh|0.292|0.285|0.235|0.289|0.278|
|West Bengal|0.284|0.251|0.224|0.27|0.239|
|Tamil Nadu|0.324|0.307|0.279|0.316|0.264|
|Madhya Pradesh|0.292|0.277|0.242|0.265|0.292|
|Rajasthan|0.34|0.26|0.209|0.246|0.225|
|Gujarat|0.252|0.236|0.234|0.269|0.253|
|Karnataka|0.299|0.266|0.241|0.263|0.235|
|Orissa|0.266|0.243|0.244|0.281|0.262|
|Kerala|0.33|0.288|0.27|0.341|0.417|
|Assam|0.192|0.176|0.201|0.195|0.244|
|Jharkhand||||0.225|0.24|
|Haryana|0.271|0.301|0.239|0.322|0.301|
|Punjab|0.279|0.265|0.239|0.279|0.288|
|Chhattisgarh||||0.295|0.276|
|India|0.297|0.282|0.26|0.3|0.291|

Source: Planning Commission website (accessed on February 4, 2013)

**Table 18** : The Gini coefficient in urban areas

|State|1983|1993-94|1999-00|2004-05|2009-10|
|---|---|---|---|---|---|
|Uttar Pradesh|0.312|0.323|0.328|0.366|0.329|
|Maharashtra|0.329|0.351|0.348|0.372|0.41|
|Bihar|0.297|0.307|0.319|0.33|0.332|
|Andhra Pradesh|0.306|0.32|0.313|0.37|0.382|
|West Bengal|0.328|0.334|0.341|0.378|0.384|
|Tamil Nadu|0.347|0.344|0.381|0.356|0.332|
|Madhya Pradesh|0.29|0.327|0.315|0.393|0.364|
|Rajasthan|0.301|0.29|0.282|0.367|0.378|
|Gujarat|0.264|0.287|0.286|0.305|0.328|
|Karnataka|0.33|0.315|0.323|0.364|0.334|
|Orissa|0.294|0.304|0.292|0.35|0.389|
|Kerala|0.371|0.338|0.321|0.4|0.498|
|Assam|0.248|0.286|0.309|0.316|0.324|
|Jharkhand||||0.351|0.358|
|Haryana|0.304|0.28|0.287|0.36|0.36|
|Punjab|0.321|0.276|0.29|0.393|0.371|
|Chhattisgarh||||0.434|0.326|
|India|0.325|0.34|0.342|0.371|0.382|

Source: Planning Commission website (accessed on February 4, 2013) 31
# **Islamic Inheritance Law, Son Preference and Fertility Behavior of Muslim Couples in Indonesia**

Eliana Carranza<sup>*</sup> World Bank JEL codes: J12 J13 J16 K1 Keywords: Islamic inheritance, Fertility, Son Preference, Indonesia. Sector boards: GEN, HNP

> * E-mail: carranza@fas.harvard.edu. I am grateful to Sebastian Bauhoff, Claudia Goldin, Lawrence Katz, Sendhil Mullainathan and Rohini Pande for their advice on this project. I also gratefully acknowledge financial support from the Radcliffe Institute for Advanced Study and the Project on Justice, Welfare and Economics. All errors are my own.
# **Appendix B. Tables.**

**Table 1. Violent Events in 2009: Colombia** **<u>and the Four CLS-WILL Regions</u>**

||**Homicide**<br>**Rate x**<br>**100,000**<br>**Inhabitants**|**Forced Displacement**|**Forced Displacement x**<br>**100,000 Inhabitants**|
|---|---|---|---|
|Atlantic|17.15|144|45.73|
|Central|16.37|20|40.92|
|Coffee-Growing|54.87|175|252.67|
|South|8.80|671|656.08|
|Colombia|35.52|154,040|346.00|

Source: Vice-President ́s Observatory for Human Rights.

**Table 2.  Presence of Armed Groups and Aggressions against the Population**

||**Atlantic**|**Central**|**Coffee-**<br>**Growing**|**South**|
|---|---|---|---|---|
|Presence of armed groups in the<br>community between 2001-2010|15.79%|25.00%|36.21%|44.07%|
|Threats|7.02%|12.50%|20.69%|20.34%|
|Assaults|3.51%|6.25%|12.07%|10.17%|
|Impose rules|7.02%|2.08%|15.52%|23.73%|
|Extortions|3.51%|10.42%|10.34%|8.47%|
|Expropriate land|1.75%|2.08%|0.00%|5.08%|
|Seize livestock illegally|0.00%|2.08%|0.00%|8.47%|
|Seize crops illegally|0.00%|2.08%|3.45%|3.39%|
|Total number of communities|57|48|58|59|

Source: CLS-WILL: first wave – community questionnaire.

**Table 3. The Economic Consequences of Conflict during the Last Two**

**Years**

|Have member of the community|**Atlantic**|**Central**|**Coffee-**<br>**Growing**|**South**|
|---|---|---|---|---|
|Abandon crops|0.00%|0.00%|6.90%|8.47%|
|Stopped investing in the farm|1.75%|2.08%|13.79%|6.78%|
|Stoppedproducingtraditional crops|1.75%|6.25%|12.07%|5.08%|
|Total number of communities|57|48|58|59|

Source: CLS-WILL: first wave – community questionnaire.

37
Shingal, Anirudh. 2011. “Services Procurement under the WTO’s Agreement on Government Procurement: Whither Market Access?” _World Trade Review_ 10 (4): 527–49.

- ———. 2015. “Econometric Analyses of Home Bias in Government Procurement.” _Review of International Economics_ 23 (1): 188–219.

- Silva, J. M. C. Santos, and Silvana Tenreyro. 2006. “The Log of Gravity.” _The Review of Economics and Statistics_ 88 (4): 641–58.

- Taş, Bedri Kamil Onur, Kamala Dawar, Peter Holmes, and Sübidey Togan. 2019. “Does the WTO Government Procurement Agreement Deliver What It Promises?” _World Trade Review_ 18 (4): 609–34.

23
The locations correspond to the survey clusters as they are defined in a typical two-stage sampling scheme. The observable characteristics must be found as variables in both the survey and the census or in a tertiary data source that can be linked to both data sets.<sup>4</sup> Using a linear approximation to the conditional expectation, the household’s logarithmic per capita expenditure is modeled as The vector of disturbances, **_u_ ,** is distributed _F_ (0 **,Σ** ). The model in (2) is estimated by Generalized Least Squares using the household survey data. In order to estimate the GLS model, **Σ** , the associated error variance-covariance matrix, is estimated. Individual disturbances are modeled as where η _c_ is a location component and ε _ch_ is a household component. This error structure allows for both spatial autocorrelation, i.e. a “location effect” for households in the same area to the extent that it is not already covered by location-level explanatory variables, and heteroskedasticity in the household component of the disturbance. The two components are uncorrelated and (by construction) uncorrelated with observable characteristics in the regression equation.

The model in (2) is first estimated by simple OLS. The residuals from this ˆ regression serve as estimates of overall disturbances, given by _uch_ . These residuals are decomposed into uncorrelated household and location components:

> 3 Early variants of the methodology were presented in Hentschel et al (2000) and Elbers, Lanjouw and Lanjouw (2000).  These earlier versions differ in important ways with the approach outlined in ELL(2002).

4
Ekberg et al. (ibid) outline mechanisms through which parental leave reform in Sweden may improve women’s labor market outcomes in the long run. The first mechanism comes from the demand side. Adopting policies to encourage fathers to take time out after the birth of a child, and thus reducing the gap in leave between both parents, could change employers’ beliefs that women are significantly more likely to drop out of the labor market (either permanently, or for an extended period) after the birth of a child. When women are more likely than men to drop out of the labor market, an otherwise unbiased employer may prefer to hire a man due to the investment costs of training workers (Lazear and Rosen 1990). This may be a particularly important consideration in the hiring of high-skilled workers. Beyond the immediate period following the birth of a child, the mother is more likely to experience work interruptions because of a need to take care of sick children, or due to other household responsibilities. This further increases the relative costs of employing a woman and makes women less attractive as employees due to statistical discrimination (Phelps, 1972).

The second mechanism outlined by Ekberg et al. (ibid) relates to behavior within the household. It is proposed that if men spend more leave days taking care of a child, the allocation of time within a household will be altered so that the comparative advantage that women have in childcare and household responsibilities will be reduced. If more balanced leave days are available to parents and there is a less gender-specific accumulation of human capital, this would also alter employers’ views in such a way that should benefit women.

Motivated by the argument that encouraging men to take more time relative to women may reduce the comparative advantage that mothers have in childcare-specific human capital, we investigate how the gap in the allocation of leave to both parents, that is, the difference between the number of days allocated to mothers versus fathers, relates to women’s labor market outcomes. We look at this primarily in terms of aggregate participation in the labor force, but also consider the gender wage gap, women’s propensity to be employed in managerial positions and in the formal sector, labor force participation rates for women of different education levels, and attitudes to women’s work.

# 3. Data and descriptive findings

# _3.1 Leave data_

The leave data we use in our analysis have been collected and compiled by the World Bank’s _Women, Business and the Law_ (WBL) project. The WBL index measures the legal inequalities that a woman faces as she navigates her career, from the time she enters the workforce through to her retirement. Eight
# **Introduction**

Paid work without legal contract is a phenomenon closely related to such fields of economic and social studies as shadow economy, tax evasion, trust in and efficiency of institutions, labor demand and labor supply, self-employment, worker mobility, labor market flexibility, social exclusion, social security, and many others. Understanding determinants of the size of informal workforce is thus important both for policy making and for design of institutional reforms. Yet research in this field, especially in European context, has been limited due to lack of data.

In this paper we compare the prevalence of informal employment in 30 European countries using data from the European Social Survey (2004-2009), further referred to as ESS. Our analysis excludes under-declared work (envelope wages) and does not distinguish declared and undeclared output. In other words, we focus on dependent workers without contracts, as well as on self-employed (a further classification of self-employed into ̳formal‘ and ̳informal‘ will be suggested below).

We address the following questions:

- How strongly do European countries, as well as Northern, Western, Eastern and Southern Europe<sup>1</sup> differ from each other in terms of levels of informal employment observed in the first decade of the 21<sup>st</sup> century? Does a stable ranking emerge?

- How does prevalence of works without contract among wage earners depend on their human capital and other characteristics? In particular, how do minorities, first and second generation immigrants compare to native workers?

- Is informal wage employment found only in small establishments in selected sectors, or is it more common?

- How are the levels of informal dependent employment and  informal self-employment related to the economic cycle (and, in particular, how did they respond to the current crisis)?

- In what ways is the profile of an  ̳informal worker‘ different from that of a person employed under a contract, on one hand, and of informal self-employed, on the other? Are these differences country-specific (or country group-specific)?

- Does a typical informal worker come from a poorer household than his/her counterpart who has an employment contract? What about informal self-employed?

- What are the main determinants of informal employment at the individual level?

> 1 We will sometimes refer to these geographical areas as to „European regions‖.  Otherwise (i.e. when „European‖ is not added) „regions‖ stand for within-country regions.

2
- _64

# Table 2 (Bolivia)

# Week-to-Week Percent Changes in Prices and Free Market Exchange Rates

||CPI|Exchange Rate|
|---|---|---|
|August 5- August 11|18.37|4.91|
|August 11- August 18|8.57|2.42|
|August 19- August 25|6.15|9.26|
|August 26-<br>September 1|19.87|12.31|
|September 2- September 8|36.82|-12.96|
|September 9- September 15|-4.60|-9.78|
|September 16- September 22|-0.84|-3.15|
|September 23- September 29|-2.51|8.09|
|September 30- October 6|0.74|1.87|

Source: Morales (1987a)
# Table of Contents

|Page|
|---|
|Chapter I:<br>Introduction .... . . . . . . . . . . . . . . . . . .<br>.<br>_1_|
|Chapter II:<br>Health .... . . . . . . . . . . . . . . . . . . . . .<br>.<br>_3_|
|The data .... . . . . . . . . . . . . . . . . . . . . . . . . .<br>.<br>_3_|
|The trends . . . .10|
|The causes of decline in child mortality . . . . . . . .15|
|Chapter III: Education . . . . . . . . . . . . . . . . . . . . . . . . . 24|
|The data .............................<br>. 26|
|The trends ............................<br>. 28|
|Chapter IV: Nutrition ........................<br>. 33|
|The data .............................<br>. 33|
|The trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35|
|Chapter V:<br>Per Capita Private Consumption .... . . . . . . . . . .<br>. 39|
|The data ........<br>......................<br>. 40|
|The trends ............................<br>. _40_|
|Chapter VI: Social Sector Expenditurei . . . . . . . .43|
|The data .|
|The trends<br>.|
|Chapter<br>VII: Concluding Remarks . . . . . . . . . .49|
|<br> <br>List of Tables|
|Table 1: Infant mortality rates: DHS, the<sup>World Bank,</sup><br> <br>|
|and the UN_........................._<br>_._<br>_6_|
|Health 1: Under 5 Child Mortality Rates (Hill and<br>Pebley data) . . . . . . . . . . . . . . . . . . <sup>. . . . . . . 51</sup>|
|Health 2: Under 5 Child Mortality Rates<sup>(interpolated</sup><br>UN data) . . . . . 55<br>Health 3: Immunization for BCG .59|
- tion? Evidence from Coagglomeration Patterns,” _American Economic Review_ , June 2010, _100_ (3), 1195–1213.

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34
Table 5. Random effects probit of use of Mirt stove at least twice per week with three cooking event thresholds

||40 Degrees C|45 Degrees C|60 Degrees C|
|---|---|---|---|
||threshold|threshold|threshold|
|Incentive Period 1|-0.445|-0.260|-0.708|
||(0.342)|(0.303)|(0.300)**|
|Incentive Period 2|<br>-0.189|<br>-0.083|<br>-0.282|
||(0.347)|(0.308)|(0.298)|
|Incentive Period 3|-0.177|-0.120|-0.239|
||(0.337)|(0.298)|(0.287)|
|Incentive Period 4|<br>-0.253|<br>-0.239|<br>-0.430|
||(0.343)|(0.303)|(0.293)|
|Incentive Period 5|-0.452|0.225|-0.221|
||(0.373)|(0.327)|(0.310)|
|Pay Treatment Period 1|<br>-1.317|<br>-1.163|<br>-1.197|
||(0.350)***|(0.312)***|(0.307)***|
|Pay Treatment Period 2|<br>-1.039|<br>-0.914|<br>-1.227|
||(0.343)***|(0.306)***|(0.307)***|
|Pay Treatment Period 3|<br>-0.208|<br>-0.131|<br>-0.551|
||(0.334)|(0.294)|(0.286)*|
|Pay Treatment Period 4|-0.251|-0.318|-0.461|
||(0.337)|(0.297)|(0.286)|
|Pay Treatment Period 5|<br>-0.141|<br>-0.023|<br>-0.503|
||(0.374)|(0.323)|(0.317)|
|Free Treatment Period 1|-0.660|-0.606|-0.637|
||(0.275)**|(0.257)**|(0.252)**|
|Free Treatment Period 2|<br>0.029|<br>-0.042|<br>-0.007|
||(0.267)|(0.252)|(0.243)|
|Free Treatment Period 3|0.268|0.245|0.068|
||(0.264)|(0.246)|(0.237)|
|Free Treatment Period 5|<br>-0.054|<br>0.239|<br>-0.266|
||(0.335)|(0.296)|(0.290)|
|Group training dummy|<br>Yes|<br>Yes|<br>Yes|
|<br>Socio-economic variables|Yes|Yes|Yes|
|Cooking characteristics|Yes|Yes|Yes|
|<br>Constant|-0.161|-0.358|-0.480|
||(0.651)|(0.552)|(0.529)|
|N|1,341|1,338|1338|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01.  Robust standard errors in parentheses. Free treatment period 4 is the reference group.

21
# **Simple Model Frameworks for Explaining Inefficiency of the Clean Development Mechanism**

# **By**

**Knut Einar Rosendahl and Jon Strand**<sup>**1**</sup>

> **1** K. E. Rosendahl is a senior research fellow in the Research Department of Statistics Norway, and a visiting scholar at Resources For the Future. Jon Strand is a senior economist in the World Bank (DECRG, Environment and Energy Team). He also holds a chair in the Department of Economics, University of Oslo.