rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix Table A.11 Returns to Education Among Migrants from RCRE Villages in 2003** Heckman Selection Models

||Model<br>|1|Mo<br>|del 2|
|---|---|---|---|---|
||ln(Daily||ln(Daily||
||Migrant<br>Wage)|Migrant?<br>(1= Yes)|<br>Migrant<br>Wage)|Migrant?<br>(1= Yes)|
|Years of Schooling|0.029<br>(0.012)|-0.007<br>(0.014)|--|--|
|0<=Years of Schooling <9|--|--|0.040<br>(0.019)|0.072<br>(0.022)|
|9<=Years of Schooling <12|--|--|0.003|-0.126|
||||(0.034)|(0.036)|
|Years of Schooling>=12|--|--|0.047|-0.029|
||||(0.059)|(0.068)|
|Age|0.146|0.285|0.149|0.286|
||(0.041)|(0.043)|(0.034)|(0.042)|
|Age Squared|-0.003|-0.005|-0.002|-0.005|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Male|0.213|0.311|0.217|0.307|
||(0.053)|(0.061)|(0.052)|(0.061)|
|Fathers Years of Education|-0.013|-0.0283|-0.012|-0.027|
||(0.010)|(0.011)|(0.009)|(0.011)|
|Mothers Years of Education|0.008|-0.005|0.008|-0.005|
||(0.010)|(0.012)|(0.010)|(0.011)|
|Household Size|--|-0.010|--|-0.020|
|||(0.044)||(0.044)|
|Number of Adult Laborers|--|0.054|--|0.057|
|||(0.046)||(0.046)|
|Household Land Per Capita|--|-0.115|--|-0.126|
|||(0.052)||(0.052)|
|Number of Elderly in Household|--|-0.054|--|-0.045|
|||(0.038)||(0.038)|
|Dependency Ratio|--|-0.079|--|-0.087|
|||(0.178)||(0.179)|
|Male/Female Ratio|--|-0.354|--|-0.317|
|||(0.202)||(0.202)|
|Number of Children Under 5|--|-0.197<br>(0.068)|--|-0.203<br>(0.069)|
|Number of Observations|3880||3|880|
|Censored Observations|3101||2|101|
|Uncensored Observations|779||7|79|

Notes: Individual information necessary to estimate daily returns to education from migrant employment are only available for the 2003 survey. We estimate returns to education in migrant employment for children of the household head and spouse who are under 50 and over 15 years of age. Standard errors, clustered at the village, are shown in parentheses.

75
The _Dow_ and _Mmusi_ cases showcase the tangible impacts of strong women’s movements. Women’s groups all over the world are effectively leveraging state commitments to international treaties such as CEDAW, constitutional reform processes and national court systems to ensure that the principle of gender equality is implemented in practice. Regional and international coalitions between networks have resulted in resources and knowledge becoming building blocks towards the next step of the process. International institutions have a part to play in supporting the reform of discriminatory laws through the mapping and highlighting of bad laws and linking reform to positive economic outcomes, capacity building of women’s lawyer’s associations and other networks, and through facilitating the flow of knowledge. The role of the judiciary in responding to these cues for social change is heartening and such landmark cases could be included in gender sensitivity training for legal personnel more broadly. The government through embarking on constitutional and legislative reform can cement the endorsement of equality principles in the national framework for generations to come. The lessons drawn from Botswana’s pathway to promoting legal equality contribute to a broader international dialogue on women’s economic empowerment.

# **List of Cases**

AG v Dow (2001) AHRLR 99 (BwCA 1992) Bhe v Khayelitcha (2005)1BCLR 1 CC 15 Oct. 2004 Kwebeg Land Board v Mathlo 2000 (2) BLR 430 (High Court) Moatswi and Another v Fencing Centre (Pty) Ltd 2004 AHRL 131 (BcIC 2002) Mofokate v Mofokate 2000 (2) BLR 430 (HC) Mmusi v Ramantele, Customary Court of Appeal, 22 November 2011. Civil Appeal case 121/2007 S v Ntwayame CLCLB-066-07 (2008) BwCA 37 (1 January 2008) Student Representative Council of Molepolole College of Education v AG 1995 BLR 178 (CA)

# **Databases**

_Human Development Index_ 2012 _,_ is a composite measure of health, education and income covering 187 countries which was developed as an alternative to purely economic assessments of national progress, such as GDP growth; _World Economic Forum Global Gender Index 2012_ benchmarks national gender gaps on economic, political, education and health criteria and provides country rankings that allow for effective comparisons across regions and income groups and over time.

**References**

13
the degree of devaluation and real income loss as a share of GDP to households falling below the national poverty line under different pass-through and wage indexation assumption. The higher the pass-through, the higher the real income loss (though indexation helps to lower losses for a given devaluation rate as can be seen from the dashed lines which traces the real income loss associated with a 30% public wage indexation). The real income loss also rises at an increasing rate, the greater the degree of devaluation.

Figure 10 Djibouti: Relationship between Magnitude of Nominal Devaluation and Real Income Loss to Poor Population (% of GDP) <!-- Start of picture text --> 30<br>25<br>100% Pass-Through<br>20<br>15<br>70% Pass-Through<br>10<br>60% Pass-Through<br>50% Pass-Through<br>5<br>0<br>30% 60% 100%<br>Degree of Devaluation<br>Dashed Lines indicate outcome w ith 30% public w age indexation.<br>Real Income Loss (% of GDP)<br><!-- End of picture text --> _Source: Authors’ calculation based on WEO, 2004_

# **6.2. Estimating Fiscal Savings Net of Social Transfers**

**_Can the Djibouti authorities compensate the poor for their real income loss in the form of social transfers and still be left with a positive level of fiscal savings?_** The previous discussion has shown that there will be real income losses to the population in response to a nominal exchange rate devaluation, with the poorer segments being particularly hurt. This section estimates the real fiscal savings that could be generated by a currency devaluation after having accounted for the amount of social transfers needed to compensate its poor population for its real income loss.

## **_6.2.1. Methodology and Main Assumptions_**

Two scenarios are considered. A first scenario assuming no wage indexation and a second scenario assuming a 30% wage indexation. The analysis is further disaggregated according to four possible exchange rate pass-through (50%, 60%, 70% and 100%). A summary of the results is shown below in Table 7 (see also Tables A.6 to A.8 of the appendix).

23
**Table 9: The Effect of Country Risk on PPI and FDI**

|**VARIABLES**|**Log FDI**<br>**(1)**|**Log PPI**<br>**(2)**|**Log PPI***<br>**(3)**|
|---|---|---|---|
|Country Risk|0.0136***|0.0300***|0.0342***|
||(0.00486)|(0.00574)|(0.0109)|
|Log GDP ppp|1.667***|1.419***|0.738***|
||(0.101)|(0.119)|(0.255)|
|Log Gdp Growth|0.0350|0.100*|0.0901|
||(0.0457)|(0.0538)|(0.105)|
|Log Inflation|-0.0287|-0.0808**|-0.183**|
||(0.0350)|(0.0409)|(0.0917)|
|Log Openness|0.648***|0.134|-0.378|
||(0.179)|(0.212)|(0.472)|
|Constant|-37.89***|-31.34***|-12.85**|
||(2.155)|(2.562)|(5.467)|
|R2 within|0.3523|0.2262|0.0571|
|R2 between|0.6451|0.7535|0.5291|
|R2 overall|0.6283|0.6475|0.4091|
|Observations|1,146|1,201|614|
|Number of _country|116|119|94|

*** p<0.01, ** p<0.05, * p<0.1. PPI* excluding Telecom Source: Authors’ calculations.

The country risk and the size of the economy are main variables that explain the level of the commitment in both types of investments, but the effect of country risk is much higher on PPI than on FDI. An improvement of one point in country risk is linked to an increase of 3.4 percent in infrastructure investment. In the case of FDI, such an improvement equals about 1.3 percent more. In other words, foreign investors overall—including those in extractive industries such as coal, gas, oil, forest products, metals or minerals—are more readily able to find returns from their investments commensurate with the country risks they are assuming than investors in infrastructure alone.

The evidence shows that openness is not a key variable for investment in infrastructure as it is for FDI. This is due to the tradable nature of FDI.

# **_5.5 Conflict-affected states analysis_**

Few investments can be considered higher risk than those that go into fragile and conflictaffected countries. This section reviews the basic features and timing of PPI in conflict-affected countries to see if they suffer from particular prejudice in trying to attract PPI.

16
48

|**COUNTRY**|PROJECT|**LEGAL**COMPONENT|FINANCIERS|
|---|---|---|---|
|Congo|Public Enterprise Sector Adjustment<br>Loan (proposed)|In depth study of existing legislation regulating public<br>enterprises and of impediments to liberalizationlprivatization<br>and reform proposals; review of legal framework relating to<br>commercial operations and proposals for reform; review of<br>legal framework relating to the judiciary: codes, tribunals,<br>expertise of magistrates and the legal profession, and<br>proposals for reforms and training; reform of hydrocarbon<br>law.|IBRD|
|Costa Rica|Second Water Supply and Sewerage<br>Project (Loan No. 3625 CR),<br>July 28, 1993|Legal technical assistance in the fomulation of a new legal<br>and regulatory framework for the provision of water and<br>sewerage services in Costa Rica.|IBRD $26 m.|
||Health and Nutrition Project,<br>Japanese Grant Agreement, May 22,<br>1992|Review of existing legislation related to the role and functions<br>of Costa Rica's Ministry of Health on environmental health.|Japanese Grant 1161.9 m.|
||IDF Grant for Institutional<br>Environmental Planning and<br>Strengthening (proposed)|Legal technical assistance in reviewing the proposed national<br>environmental law.<br>_<br>__l|IDF $360,000|
|Cote d'Ivoire<br>_____________|Economic Management Pect<br>(Cr No. 2503 IVC), June 14, 1993|Support to the Ministry of Justice to strengthen the legal<br>framework and judicial system in commercial areas,<br>including: (a) updating of economic legislation; (b)<br>establishment of an arbitration couirt for commercial disputes<br>and provision of logistical support to this Court; (c)<br>computerization of selected courts' registrar's offices;_(d)_<br>design and implementation of training program in commercial<br>and financial legislation for the legal community; (e)<br>assistance for publication of legal and judicial data and<br>acquisition of law books and periodicals for courts and<br>tribunals; and (f) institutional strengthening of the Supreme<br>Court through training of staff and acquisition of computers<br>and equipment.|IDA $17 m.<br>French Ministry of<br>Cooperation=<br>$1.3 m.|
Table 2b (continued)

||Share of e|mployees in<br>are women|state who|Share of<br>female-o|female emp<br>wned establ|loyees in<br>ishments|Share of<br>male-ow|male empl<br>ned establi|oyees in<br>shments|
|---|---|---|---|---|---|---|---|---|---|
||2001|2006|2010|2001|2006|2010|2001|2006|2010|
|A & N Islands|0.139|0.173|0.240|0.572|0.711|0.603|0.911|0.895|0.832|
|Andhra Pradesh|0.311|0.290|0.256|0.761|0.787|0.867|0.747|0.777|0.828|
|Assam|0.070|0.144|0.079|0.751|0.845|0.823|0.968|0.954|0.965|
|Bihar|0.078|0.125|0.085|0.739|0.734|0.895|0.943|0.912|0.954|
|Chandigarh|0.158|0.055|0.131|0.585|0.784|0.743|0.898|0.972|0.933|
|Dadra & Nagar Haveli|0.102|0.092|0.157|0.442|0.476|0.917|0.953|0.966|0.971|
|Daman & Diu|0.096|0.083|0.104|0.418|0.297|0.749|0.961|0.962|0.943|
|Delhi|0.115|0.127|0.144|0.641|0.741|0.853|0.948|0.938|0.920|
|Goa|0.251|0.276|0.199|0.759|0.688|0.902|0.812|0.840|0.883|
|Gujarat|0.087|0.107|0.116|0.477|0.816|0.884|0.945|0.949|0.940|
|Haryana|0.157|0.151|0.154|0.754|0.789|0.906|0.900|0.912|0.905|
|Himachal Pradesh|0.106|0.116|0.110|0.510|0.692|0.786|0.937|0.933|0.954|
|Jammu & Kashmir|0.124|0.081|0.111|0.846|0.790|0.746|0.926|0.947|0.925|
|Karnataka|0.163|0.186|0.195|0.772|0.834|0.843|0.892|0.881|0.875|
|Kerala|0.209|0.233|0.221|0.805|0.822|0.853|0.863|0.850|0.862|
|Madhya Pradesh|0.124|0.136|0.118|0.716|0.685|0.798|0.920|0.910|0.930|
|Maharashtra|0.160|0.165|0.153|0.659|0.750|0.883|0.913|0.909|0.927|
|Manipur|0.255|0.250|0.342|0.844|0.836|0.865|0.854|0.860|0.840|
|Meghalaya|0.396|0.365|0.351|0.774|0.789|0.816|0.839|0.901|0.958|
|Nagaland|0.202|0.315|0.229|0.854|0.678|0.779|0.879|0.800|0.849|
|Orissa|0.228|0.154|0.097|0.785|0.863|0.701|0.801|0.883|0.935|
|Pondicherry|0.232|0.316|0.293|0.495|0.617|0.839|0.824|0.750|0.865|
|Punjab|0.162|0.160|0.159|0.781|0.844|0.918|0.913|0.918|0.929|
|Rajasthan|0.093|0.098|0.108|0.583|0.696|0.818|0.930|0.939|0.939|
|Tamil Nadu|0.282|0.271|0.236|0.774|0.813|0.827|0.786|0.801|0.838|
|Tripura|0.068|0.063|0.064|0.777|0.833|0.932|0.963|0.975|0.948|
|Uttar Pradesh|0.136|0.145|0.133|0.798|0.723|0.795|0.911|0.896|0.907|
|West Bengal|0.090|0.090|0.097|0.774|0.801|0.902|0.966|0.967|0.965|
|Nationwide|0.162|0.168|0.156|0.738|0.778|0.850|0.890|0.892|0.907|

Notes: See Table 1.
**Table 2: A Comparison of Base Wage and Welfare Benefit (Non-Wage) Per Worker by Ownership (unit = Yuan at current Price)**

|||Wage inco|me||Welfare ben|efits|
|---|---|---|---|---|---|---|
||SOEs|Collective|Other private|SOEs|Collective|Other private|
|1997|6747|4512|8789|2335|1166|339|
|1996|6280|4320|8261|2042|1053|337|
|1995|5625|3931|7463|1758|935|293|
|1994|4797|3245|6303|1468|755|275|
|1992|2284|1681|2987|1006|549|133|

Notes: Welfare benefits do not include housing benefits in the above statistics. We expect that state workers' benefits could be even higher if housing benefits were added. The share of total benefit per worker will increase to 60-70 percent of total income per worker.

25
especially in countries with a sizable public pension system. Changing the statutory retirement age, encouraging beyond retirement work, and promoting further female participation in the labor force may all work to mitigate the adverse implications of aging to a certain extent. However, the degree of this impact depend on country specic characteristics.

This section introduces a number of alternative labor force participation scenarios to investigate the boundaries of policy impact in this area. The baseline labor force projections assume that the LFP rate for both men and women remain constant after 2020. This structure is in line with the current ILO projections. Here we introduce three alternative labor force trajectories, which reect the likely impact of dierent policy combinations.

First, we discuss a retirement age scenario, where the statutory retirement ages are increased from 63 to 65 for men, and from 60 to 63 for women by 2017. This measure changes the LFP rates, especially of those between the old and new statutory retirement ages. Second, we consider a high LFP scenario, where, LFP rates are assumed to increase substantially in order to provide an upper bound of the analysis. To this eect, we build on the rst scenario, and assume that in addition to the increasing retirement ages, the LFP rates converge to those of high LFP rate countries such as Iceland, Norway, Sweden, and Switzerland including all ages and both genders. Moreover, the average length of work life is assumes to be 10 years longer than the baseline case. Overall, this scenario implies LFP rates that exceed 80 percent even in the age group of 70 to 74. Finally, we consider the labor force projections of EuroStat along with the population projections for comparison.

Panel (a) in Figure 9 shows the labor force projections under each scenario. As expected, the high LFP scenario generates a labor force that is substantially larger than the baseline scenario, where the dierence is more pronounced towards the end of our projection horizon. By 2050, the former scenario assumes about 470,000 more participants than the latter one. In comparison, the retirement age scenario generates a more modest contribution to the labor force. The additional labor force generated by increasing the retirement age reaches to a peak of 50,000 in 2024, which gradually decreases to about 38,000 in 2050. Finally, the Eurostat labor projections also exhibit an increasing dierence over time, which reaches to about 171,000 by 2050.

The dierences between these scenarios in terms of the labor force dynamics also reect in the GDP growth trajectories in the medium and long term. Panel (b) in Figure 9 shows that, compared to other scenarios, the high labor force scenario brings about the greatest boost to GDP growth rate in the medium and long run. On average, the dierence between the growth rates under the baseline and high LFP scenarios is about 0.5 percentage points per year. This eventually increases the GDP per capita from about 22,200 under the baseline to 24
Table A.13: Hazard of birth: Cox proportional-hazards model

|Hazard Ratios|(1)|(2)|(3)|
|---|---|---|---|
|_Firstborn girl_|1.099***|1.093***|1.097***|
||(0.00874)|(0.0148)|(0.0148)|
|_Firstborn girl_ * _Post1_|0.975***|0.972***|0.968***|
||(0.00523)|(0.00569)|(0.00674)|
|_Firstborn girl_ * _Post2_|0.919***|0.917***|0.909***|
||(0.0107)|(0.0105)|(0.00984)|
|_Ideal fraction of sons_|||1.054***<br>(0.0119)|
|_Ideal no. of children_|||1.118***<br>(0.00887)|
|N|2,286,065|2,286,065|2,114,681|
|_Xi_|x|x|x|
|Year FE|x|x|x|
|State FE|x|x|x|
|Age FE|x|x|x|
|Parity FE|x|x|x|
|State x Year FE|x|x|x|
|Years since last birth FE|x|x|x|
|Firstborn girl x State FE||x|x|

NOTES: Hazard ratios from specification (2) estimated using a Cox proportional-hazards model on the mother-year sample. The dependent variable is an indicator for birth to a given mother in a given year. Sample includes all mothers who have ever given birth, for each year from their year of marriage to the year of interview. Standard errors in parentheses are clustered by state. State-year fixed effects of the model are included by stratifying the Cox PH model by state-year. Data: NFHS. *** 1%, ** 5%, * 10%.

60
This paper examines the evolution of foreign capital in Latin American economies by comparing them to China and India. In particular, we study total inward stocks into each country, inward stocks from major OECD countries, inward stocks from the U.S. and inward stocks from the U.S. in the manufacturing sector.

Although China appears as the developing economy with the largest FCS, its stocks from OECD sources and the U.S. in particular are smaller than those of the major Latin American countries. In fact; FCS in China are still smaller than in Latin America if we control for county size. However, Hong Kong (China) and Mainland China together accumulated larger stocks from OECD investments than any Latin American country. FCS in India, on the other hand, are still small compared to those in the major Latin American countries.

We then analyze the evolution of the relative stocks by looking at how they changed between 1990, 1997, and 2003, because the data suggest that time trends of Chinese FCS changed after 1997. We find that China accumulated larger stocks than Latin America since 1990, but not since 1997. This was not the case for U.S. capital in the manufacturing sectors of host countries, where stocks in China grew faster than in most Latin American countries between 1997 and 2003. This growth, however, is far from impressive, and it is mainly explained by faster GDP growth. In contrast, Indian FCS grew faster than in Latin American countries during the whole period 1990-2003, but this growth was slower than in China during the entire period according to both the U.S. and the OECD data.

Finally, we analyze the evolution of OECD FCS in Latin America relative to those in China and India after controlling for shocks affecting the source countries as well 3
However, the links between policy, the enabling environment, financial development, and welfare are complex. Moreover, although a legal framework, say creditor rights, may look adequate on paper, it may be ineffective in practice due to poor enforcement or because it does not reflect well some peculiar characteristics of the local environment or does not fit well with other idiosyncratic elements of the legal framework. Thus, identifying the key policy inputs (the weakest linkages in the enabling environment) is not a straightforward exercise. Assessing the possible final welfare impact of reforming them is even less trivial.

**Figure 1: The Process and Measurement of Financial Development**

<!-- Start of picture text --> EEIs FDIs EDIs<br>Enabling Financial Economic<br>Policy<br>environment development development<br><!-- End of picture text --> FDIs can thus help bridge the gap between policy and final objectives. Unlike in monetary policy where Taylor rules directly and unequivocally link policy (the interest rate) to welfare (inflation), there are no such rules in the case of financial development. Instead, given the rather complicated links between policy, the enabling environment, financial development, and welfare, FDIs can function as “intermediate” indicators and targets, much as in the case of monetary intermediate targets before the advent of modern inflation targeting. They can help detect what functions may be under-provided and sectors or instruments under-developed. This in turn can provide revealing clues as to where to look for in identifying possible gaps and key under-performing components of the enabling environment. At the same time, FDIs can help isolate and assess the impact of policy over time. As in the case of monetary policy, however, the intermediate (FDI) indicators are most effective when used in conjunction with the more operational (EEI) indicators.

# **b. Why compare across time and countries?**

Financial development is a time-bound process. While there are a few cases where shocks exogenous to the economy or disastrous government policies have resulted in a lowering of financial development, in most cases, financial sectors become more developed over time. However, the pace of development can vary substantially from country to country and from segment to segment of the financial market, depending in part of course on economic growth, but also on the specific progress made in any particular country in 5