rafmacalaba/datause-encoder-data huggingface dataset:
forgone income to PW wages is 0.35 (st.dev.=0.344; N=930) in R1, rising to 0.39 (st.dev.=0.392; N=774) in R2.<sup>25</sup> The corresponding medians are 0.30 and 0.31. Density functions of the ratio of forgone income to public works wages (right hand panel in Figure 7) also have three distinct modes. As with days, one is around zero and is the overall mode. This represents the BREGS participants who stated that they would not have been earning income had they not worked on the scheme. A second mode is around 0.5 where participants would have earned about half of the earnings on public works and the third and smallest mode is about 0.9. The latter beneficiaries would have earned close to the equivalent amount but possibly have had to migrate and bear costs to do so. There may also be unobserved non-pecuniary benefits to work on BREGS that make it more desirable than alternative equally remunerated casual work.

In summary, these observations suggest that forgone income is significant, though falling well short of that implied by assuming that the opportunity cost of labor on the scheme is the casual market wage rate. There are three distinct groups of workfare participants: those for whom there is no likely income loss from joining the program, those for whom there is only a small net income gain from joining the program, and an intermediate group for whom around half of the BREGS wage represents a net income gain.

# **6. Impacts on poverty**

In estimating the actual impacts on poverty, we will use the household-specific reports of forgone earnings for men and women from the last section. The post-BREGS distribution of consumption is that actually observed in the data. The pre-BREGS distribution is derived from the post-BREGS distribution by subtracting the net earnings gains from public works employment, as given by gross wages less the estimated forgone income. Table 7 summarizes the various simulations of the impacts on poverty as discussed in detail below.

We estimate that the poverty rates (proportion of the population of Bihar living below the poverty line) among BREGS participants would have been 62.2% and 52.6% in R1 and R2, respectively, without the program. By contrast, what we observe in the data (including, of course, net earnings from the scheme) are corresponding poverty rates of 56.8% and 50.2%. Thus we

> 25 The corresponding means without truncation are 0.63 (st.dev.=2.31) and 0.63 (st.dev.=2.73). However, these means are distorted by some very large outliers (reaching a forgone income of 68 times actual wage receipts from PW), that are clearly measurement errors.

20
# **Table 6: Results with Productive and Non-productive Expenditures (Dynamic Panel - GMM)**

## **_Dependent variable: Growth rate of GDP per capita_**

||Fast growing<br>countries<br>**(1)**|Comparison<br>group<br>**(2)**|Whole sample<br>pooled<br>**(3)**|Whole sample<br>with dummies<br>**(4)**|
|---|---|---|---|---|
|Openness|0.058|-0.348|0.047|-0.014|
|(% of GDP)|(1.834)*|(-1.335)|~~(~~0.936)|(-0.17)|
|Tax revenue|-0.153|1.481|0.154|0.377|
|(% of GDP)|(-0.355)|~~(~~1.037)|~~(~~0.22)|~~(~~0.445)|
|Other revenue|-0.400|-0.639|-0.374|-0.081|
|(% of GDP)|(-0.905)|(-0.26)|(-0.504)|(-0.089)|
|Productive expenditure (-1)|0.777|0.397|0.866|...|
|(% of GDP)|(1.673)*|~~(~~0.393)|~~(~~1.513)||
|DFAST*Productive expenditure (-1)|...|...|...|3.217|
|(% of GDP)||||~~(~~1.465)|
|DCOMP*Productive expenditure (-1)|...|...|...|-0.331|
|(% of GDP)||||(-0.263)|
|Non-productive expenditure (-1)|-0.890|-1.187|-2.003|-2.181|
|(% of GDP)|(-1.301)|(-0.975)|(-2.641)***|(-2.402)**|
|Budget surplus|0.126|0.115|-0.058|-0.017|
|(% of GDP)|~~(~~0.316)|~~(~~0.13)|(-0.108)|(-0.027)|
|Inflation - consumer price index|-0.375<br>(-3.12)***|-0.105<br>(-2.743)***|-0.103<br>(-2.972)***|-0.116<br>(-2.735)***|
|Growth rate of GDP per capita (-1)|-0.03|-0.378|-0.382|-0.331|
||(-0.151)|(-1.436)|(-1.749)*|(-1.27)|
|No. of observations|98|171|269|269|
|Overidentification statistics|1.993|2.07|6.97|3.75|

Note: The estimation method is a dynamic GMM. Annual data are used. (-1) indicates variables lagged one period. DFAST is the dummy variable which is 1 for fast growing countries and 0 otherwise. DCOMP is the dummy variable which is 1 for the comparison group and 0 otherwise. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients. Overidentification statistics is for instrumental variables where H0: there is no overidentification problem. We fail to reject in each case.

37
used in the remainder of this section.<sup>17</sup> Here we show the share of population that (i) uses formal bank services, (ii) uses other formal financial but not bank services, (iii) uses only informal financial services, and (iv) does not use any financial services. Kenya has a higher share of population using formal financial services (21.5%) than Tanzania and Uganda, but also Zambia, where this proportion is below 20%, but a lower share than in Botswana, Namibia and South Africa, where this share is above 40%. The share of population that uses non-bank formal but not bank services is relatively high (with 15%) – mostly driven by M-Pesa -- and higher than in the other African countries for which we have such data. The share of population that is completely excluded from any formal or informal financial service is lower in Kenya (34%) than any other country except for South Africa, suggestive of the strong role that informal and other formal arrangements play in Kenya.

**Table 11.  Use of Financial Services across Africa**

||Formal|Formal,<br>other|Informal|Excluded|
|---|---|---|---|---|
|Kenya|21.5|15|29.5|34|
|Tanzania|15|2|7|75|
|Uganda|18|0|29|52|
|Zambia|14|12|11|62|
|Botswana|44|5|5|48|
|South|||||
|Africa|54|6|9|31|
|Namibia|53|3|1|42|

Source: Porteous (2007) and Beck (2009)

**Table 12.  Use of Financial Services in Kenya, over Time and across Different Groups**

||2006|2009|||||2009||||
|---|---|---|---|---|---|---|---|---|---|---|
||overall|Overall|female|male|Urban|rural|none|primary|secondary|tertiary|
|Bank|18.5|22.6|17.8|27.8|40.9|17.6|4.9|13.4|34.7|70.2|
|Formal-|||||||||||
|other|8.1|17.9|15.9|20.2|21.5|17|7.2|16.6|25|19.5|
|Informal|35|26.8|33.3|19.5|16.4|29.6|32|35.8|15.2|2.2|
|Excluded|38.3|32.7|33|32.4|21.1|35.8|55.9|34.2|25.1|8|

Source: Beck (2009). Definitions: see text.

> 17 Porteous (2007) classifies banks and Postbank as formal and insurance companies and formal money transfer operators, such as M-Pesa, as non-bank formal.  SACCOs and MFIs, on the other hand, are classified as informal financial institutions. In the FinAccess definitions for Kenya, as reported in Table 12, on the other hand, SACCOs and MFIs are classified as other formal providers.

25
|Federal Water Resources Management Project|Manuel Contijoch|
|---|---|
|Federal Water Resources Management Project|Manuel Contijoch|
|Federal Water Resources Management Project|Manuel Contijoch|
|Federal Water Resources Management Project|Manuel Contijoch|
|Federal Water Resources Management Project|Manuel Contijoch|
|Federal Water Resources Management Project|Manuel Contijoch|
|Federal Water Resources Management Project|Manuel Contijoch|
|Federal Water Resources Management Project|Manuel Contijoch|
|Rural PovertyAlleviation - Rio Grande do Norte|Luis O. Coirolo|
|Rural PovertyAlleviation - Rio Grande do Norte|Luis O. Coirolo|
|Rural PovertyAlleviation - Rio Grande do Norte|Luis O. Coirolo|
|Rural PovertyAlleviation - Rio Grande do Norte|Luis O. Coirolo|
|Rural PovertyAlleviation - Rio Grande do Norte|Luis O. Coirolo|
|Rural PovertyAlleviation - Rio Grande do Norte|Luis O. Coirolo|
|Social Investment Fund Project(02)|Rafael E. Flores|
|Social Investment Fund Project(02)|Rafael E. Flores|
|Social Investment Fund Project(02)|Rafael E. Flores|
|Social Investment Fund Project(02)|Rafael E. Flores|
|Generation Recoveryand Improvement Project|Joerg-Uwe Richter|
|Generation Recoveryand Improvement Project|Joerg-Uwe Richter|
|Generation Recoveryand Improvement Project|Joerg-Uwe Richter|
|Science and TechnologyReform Support Project|Lauritz B. Holm-Nielsen|
|Science and TechnologyReform Support Project|Lauritz B. Holm-Nielsen|
|Science and TechnologyReform Support Project|Lauritz B. Holm-Nielsen|
|Science and TechnologyReform Support Project|Lauritz B. Holm-Nielsen|
|Science and TechnologyReform Support Project|Lauritz B. Holm-Nielsen|
|Science and TechnologyReform Support Project|Lauritz B. Holm-Nielsen|
|Science and TechnologyReform Support Project|Lauritz B. Holm-Nielsen|
|Private Sector Development Project(03)- In-Service Training|Setareh Razmara|
|Private Sector Development Project(03)- In-Service Training|Setareh Razmara|
|Private Sector Development Project(03)- In-Service Training|Setareh Razmara|
|Private Sector Development Project(03)- In-Service Training|Setareh Razmara|
|Private Sector Development Project(03)- In-Service Training|Setareh Razmara|
|Private Sector Development Project(03)- In-Service Training|Setareh Razmara|
|Private Sector Development Project(03)- In-Service Training|Setareh Razmara|
|HeilongjiangAgricultural Development|Zhengxuan Zhu|
|HeilongjiangAgricultural Development|Zhengxuan Zhu|
|HeilongjiangAgricultural Development|Zhengxuan Zhu|
|HeilongjiangAgricultural Development|Zhengxuan Zhu|
|HeilongjiangAgricultural Development|Zhengxuan Zhu|
|HeilongjiangAgricultural Development|Zhengxuan Zhu|
|HeilongjiangAgricultural Development|Zhengxuan Zhu|
|HeilongjiangAgricultural Development|Zhengxuan Zhu|
|IND.ENERGY EFFIC. I|Richard E. Hamilton|
|IND.ENERGY EFFIC. I|Richard E. Hamilton|
|IND.ENERGY EFFIC. I|Richard E. Hamilton|
|POWER TRANS.III|WeigongCao|
# **Table 5: Latin America & Caribbean**

||**P**<br>**I**|**rojected**<br>**mpact as**||**Projected**<br>**Impact as**|**Incremental**|**Projected**<br>**Impact as**|<br> <br>|**Projected**<br>**Impact as**|**Incremental**<br> <br>|**Projected**<br>**Impact as**|<br>**Incremental**|**Projected**<br>**Impact as**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**Incremental**<br>**Impact: Land**<br>**Area (sq. km)**<br>**a**<br>**C**<br>**T**|**% of**<br>**oastal**<br>**otal**|**Incremental**<br>**Impact:**<br>**Population**|**a % of**<br>**Coastal**<br>**Total**|**Impact:**<br>**GDP (mil.**<br>**USD)**|**a % of**<br>**Coastal**<br>**Total**|**Incremental**<br>**Impact: Agr.**<br>**Area (sq. km)**<br> <br> <br>|**a % of**<br>**Coastal**<br>**Total**|**Impact:**<br>**Urban Extent**<br>**(sq. km)**<br> <br> <br>|**a % of**<br>**Coastal**<br>**Total**|**Impact:**<br>**Wetlands (sq.**<br>**km)**|**a % of**<br>**Coastal**<br>**Total**|
|**Mexico**|**9,136**|29.04|**463,833**|20.56|2571.55|21.22|310|10.89|701|18.35|1,765|**52.25**|
|**Brazil**|**6,281**|15.08|**1,151,493**|30.37|4889.48|28.48|275|16.47|960|33.67|2,597|11.48|
|**Cuba**|2,876|37.22|131,272|34.89|463.78|34.47|8|25.97|81|29.55|1,301|49.46|
|**Argentina**|2,407|18.03|278,155|19.52|2242.71|16.42|157|9.93|313|27.47|459|11.30|
|**R.B. de Venezuela**|2,142|14.19|119,215|33.83|619.73|33.92|28|9.30|202|29.90|763|19.99|
|**Bahamas, The**|1,517|**54.67**|3,711|**73.02**|48.92|**65.69**|24|47.39|1|**94.12**|553|**71.40**|
|**Colombia**|1,473|17.88|124,875|19.07|263.18|18.14|16|5.62|199|17.68|483|23.21|
|**Chile**|1,180|**54.67**|31,309|38.49|152.64|37.50|16|27.60|46|38.44|20|18.71|
|**Honduras**|1,055|36.07|25,592|31.03|20.84|28.56|8|22.33|8|25.97|701|39.19|
|**Nicaragua**|1,048|15.11|12,912|32.12|10.26|31.84|4|9.88|7|46.46|353|36.86|
|**Peru**|727|36.69|61,009|46.90|177.12|46.18|5|26.92|54|42.72|20|37.91|
|**Guyana**|640|20.35|29,491|37.49|43.22|46.38|0|**100.00**|93|**66.41**|234|13.75|
|**Suriname**|637|15.43|51,427|36.63|136.49|37.44|0||53|23.70|343|12.52|
|**Panama**|501|40.71|39,998|45.17|184.87|43.26|12|20.79|54|44.33|78|**54.26**|
|**Ecuador**|476|28.73|36,905|16.69|54.68|15.42|12|13.53|31|15.03|93|**67.34**|
|**Belize**|419|26.93|22,274|**56.15**|113.29|**61.14**|1|5.56|50|52.61|1|100.00|
|**Dominican Republic**|349|28.19|71,861|17.98|189.59|16.94|23|19.71|42|25.87|9|41.24|
|**Costa Rica**|343|35.85|12,939|28.95|48.29|28.16|15|33.06|15|34.39|164|41.16|
|**Uruguay**|273|10.03|17,572|27.56|118.88|27.83|12|10.03|29|22.45|0|0.00|
|**Guatemala**|213|20.97|16,365|29.51|22.26|28.08|1|26.67|9|39.68|24|29.17|
|**Haiti**|190|38.49|89,906|40.40|35.38|38.78|10|26.27|14|48.63|73|40.55|
|**Puerto Rico (US)**|173|**51.84**|104,692|**53.81**|1783.45|**52.71**|3|36.00|151|**51.23**|0||
|**Jamaica**|137|37.54|31,029|28.49|100.95|26.62|2|26.32|56|32.60|37|36.55|
|**French Guiana (Fr.)**|130|20.98|2,491|27.93|28.55|28.02|0|0.00|6|27.22|85|20.67|
|**El Salvador**|102|**55.32**|17,654|**53.00**|28.32|**49.77**|0|**66.67**|10|**49.64**|0|**100.00**|
**Table A.3. Restricted irrigation: labor-intensive agriculture with exposure for 300 days per year − median infection risks from involuntary ingestion of wastewater-contaminated soil estimated by 10,000 Karavarsamis-Hamilton Monte Carlo simulations**<sup>_a_</sup>

|**Soil quality**|**Infe**|**ction risk per perso**|**n per year**|
|---|---|---|---|
|**(****_E. coli_per 100 g)**<sup>**_b_**</sup>||||
||**Rotavirus**|**_Campylobacter_**|**_Cryptosporidium_**|
|10<sup>7</sup>−10<sup>8</sup>|1|0.70|2.1 × 10<sup>−2</sup>|
|10<sup>6</sup>−10<sup>7</sup>|0.96|0.12|2.1 × 10<sup>−3</sup>|
|10<sup>5</sup>−10<sup>6</sup>|0.28|1.3 × 10<sup>−2</sup>|2.1 × 10<sup>−4</sup>|
|10<sup>4</sup>−10<sup>5</sup>|3.3 × 10<sup>−2</sup>|1.3 × 10<sup>−3</sup>|2.1 × 10<sup>−5</sup>|
|10<sup>3</sup>−10<sup>4</sup>|3.3 × 10<sup>−3</sup>|1.3 × 10<sup>−4</sup>|2.1 × 10<sup>−6</sup>|

> _a_ Assumptions: 10−100 mg soil ingested per person per day for 300 days per year; 0.1−1 rotavirus and _Campylobacter_ , and 0.01−0.1 _Cryptosporidium_ oocyst, per 10<sup>5</sup> _E. coli_ ; _N_ 50 = 6.7 ± 25% and α = 0.253 ± 25% for rotavirus; _N_ 50 = 896 ± 25% and α = 0.145 ± 25% for _Campylobacter_ ; _r_ = 0.0042 ± 25% for _Cryptosporidium_ . No pathogen die-off.

> _b_ Soil quality taken, as a worst-case scenario, as the wastewater quality.

**Table A.4. Unrestricted irrigation: median infection risks from the consumption of wastewater-irrigated lettuce estimated by 10,000 Karavarsamis-Hamilton Monte Carlo simulations**<sup>_a_</sup>

|**Wastewater quality**|**Infe**|**ction risk per perso**|**n per year**|
|---|---|---|---|
|**(****_E. coli_per 100 mL)**||||
||**Rotavirus**|**_Campylobacter_**|**_Cryptosporidium_**|
|10<sup>7</sup>−10<sup>8</sup>|1|1|0.94|
|**...**||||
|10<sup>3</sup>−10<sup>4</sup>|0.37|1.7 × 10<sup>−2</sup>|2.9 × 10<sup>−4</sup>|
|100−1000|4.4 × 10<sup>−2</sup>|1.7 × 10<sup>−3</sup>|2.9 × 10<sup>−5</sup>|
|10−100|4.6 × 10<sup>−3</sup>|1.8 × 10<sup>−4</sup>|2.9 × 10<sup>−6</sup>|
|1−10|4.6 × 10<sup>−4</sup>|1.7 × 10<sup>−5</sup>|2.9 × 10<sup>−7</sup>|

> _a_ Assumptions: 100 g lettuce eaten per person per 2 days; 10−15 mL wastewater remaining on 100 g lettuce after irrigation; 0.1−1 rotavirus and _Campylobacter_ , and 0.01−0.1 _Cryptosporidium_ oocyst, per 10<sup>5</sup> _E. coli_ ; no pathogen die-off; _N_ 50 = 6.7 ± 25% and α = 0.253 ± 25% for rotavirus; _N_ 50 = 896 ± 25% and α = 0.145 ± 25% for _Campylobacter_ ; _r_ = 0.0042 ± 25% for _Cryptosporidium_ .

82
sector of employment overseas back home experience a significant wage premium compared to returnees who find employment in other sectors. Those, however, are a minority in our sample: only 6%, 28% and 15% of return migrants to Bangladesh, Nepal and Pakistan, respectively, are employed in the construction sector.

# **6 Conclusion**

This paper reported systematic differences in the labor market outcomes of return migrants compared and nonmigrants in South Asian countries, which are major senders of temporary migrants globally. While some of these differences are partly driven by the selection of return migrants among the working age population, they subsist when controlling for workers’ observable characteristics. In particular, we find that return migrants who come back at an older age are less likely to participate to the labor force compared to nonmigrants with similar observable characteristics. Return migrants are also more likely to be self-employed compared to nonmigrants, and to hire paid employees among those that become entrepreneurs. Those who are wage employees upon return are more likely to be employed in the construction sector, the main sector of employment at destination, compared to nonmigrants. Return migrants who are paid employees earn a significant wage premium compared to nonmigrants in Pakistan, but not in Bangladesh and Nepal. The returnee wage premium, however, is larger and statistically significant in the construction sector, suggesting that return migrants who find employment in this sector benefit from their experience abroad.

This paper however also illustrates the limitations of standard nationally-representative data sets when looking at the relationship between past migration overseas and labor market outcomes back home. Given the parsimonious information on past migration overseas included in those surveys, the evidence reported in this paper is descriptive rather than causal. To investigate in detail the causal link between temporary migration overseas and labor market outcomes back home, much more comprehensive information on past migration overseas needs to be collected in nationally representative surveys of South Asian countries, in the spirit of the Egyptian Labor Market Survey (Wahba 2015). In addition, dedicated migrant surveys that collect detailed retrospective information on employment and migration history, such as the World Bank Bangladesh Return Migrant Survey (BRMS), are well-suited for that purpose, but remain quite scarce globally (Bossavie, Gorlach, et al. 2021; Bossavie and Ozden 2022).

19
# **1 Introduction**

Policy makers are looking for a combination of policies to improve the livelihoods, micro and small businesses that prevail in high-poverty settings (Filmer and Fox (2014), Jayachandran (2020)). Integrated interventions such as “graduation” or “economic inclusion” programs that provide capital, skill training, consumption support, coaching, and savings facilitation have had robust results.<sup>2</sup> Yet there are ongoing debates on how to optimize multi-faceted policies (e.g. Andrews et al. (2021)). In-kind assets, cash grants or microcredit are commonly used to inject capital, but evidence is lacking on which instrument is most effective to address capital constraints. Many multi-faceted programs also facilitate savings accumulation, through various modalities such as savings groups or formal savings accounts. Savings mobilization provides an alternative mechanism to facilitate productive investments, possibly reducing the need for capital injections in the first place. This multiplicity of instruments raises questions about the optimal mechanism to address financial constraints, which in turn depends on the nature of the capital or savings constraints that are binding for the poor.<sup>3</sup> In this paper, we report results from an RCT testing how three alternative instruments to alleviate financial constraints, combined with training, increase poor households’ investments, improve income-generating activities and influence social outcomes. Specifically, we document the relative effects of relaxing savings constraints through enhanced savings groups (Village Savings and Loans Associations, “VSLAs”) designed to facilitate savings accumulation and provide small credit opportunities without any capital injection, or relaxing capital constraints through a cash grant of 95,000 CFA (USD 384 PPP) (“cash grants”) or the same grant with a 50 percent repayment condition (akin to subsidized credit, “cash grants with repayment”).<sup>4</sup> These three financial support modalities are combined with a micro-entrepreneurship training on how to develop simple business plans, assess business opportunities and define capital needs. Of note, participants do not

> 2E.g. Banerjee et al. (2015b), Bandiera et al. (2017), Bedoya et al. (2019), Bossuroy et al. (2022). Some micro-entrepreneurship interventions have also successfully combined cash or in-kind grants with technical or business training (e.g. Cho and Honorati (2014) and Blattman and Ralston (2017)).

> 3Micro-credit has resulted in limited impacts on micro-enterprise growth (Banerjee et al. (2015a), Cr ́epon et al. (2015)), especially for households at the bottom of the distribution (Meager (2019)), which has also raised questions on how to optimize financial support for the poorest.

> 4Official IMF average exchange rate for 2017: USD 1=CFA 580.657 (IMF)). World Bank PPP conversion factor for Cˆote d’Ivoire in 2017, GDP (LCU per international $): 247.134.

1
# **Introduction**

The recently published Europe and Central Asia (ECA) Regional Report on Gender (World Bank 2011) provides summary data on unconditional gender wage gaps across countries, with values ranging from a low 10 percent (Slovenia) to 50 percent (Georgia). Azerbaijan’s gender wage gap (36 percent) is at the higher end of this distribution.

It is not clear whether there are large disparities between the wages of Azerbaijan’s men and women throughout their careers. One possibility, which the empirical literature has started exploring systematically only in the last few years using data for a handful of industrial countries, is that gender wage gaps _evolve_ over time and that the first few years in the labor market, as young people transition from school to work, are an important determinant of the emergence of overall gender wage gaps (Manning and Swaffield, 2008; Bertrand, Goldin and Katz, 2010; Del Bono and Vuri, 2011). One possibility is that young men and women earn equal wages at the beginning of their career, but the childbearing years—in the absence of suitable leave policies, child care, and other supporting mechanisms—gradually place women at a disadvantage. Because this is a fledgling literature (see, for instance, Pastore, 2010; and Pastore, Sattar and Tiongson, 2013), little is known about this possibility, particularly in developing countries and economies in transition such as Azerbaijan.

This paper aims to contribute to the literature on gender wage gaps in early career by studying wage differences among young people in Azerbaijan.

The analysis is based on an _ad hoc_ nationally representative School-to-Work Transition survey carried out in the country in August 2005 on a large sample of young people aged 15 to 29. The age of individuals in the sample allows us to observe the entire process of transition from school to work and to adulthood, including the phase when the young person is going to establish a family and have children. According to official statistics, the average age of first marriage for women is 23 years, while a remarkable share of about 13% of the entire female population experience early marriage, namely when they are between 15 and 19 years of age. This allows us to study the asymmetric impact of marrying and maternal decisions by gender on labor supply and other labor market outcomes, such as wages.

The outline of the paper is as follows. Section one discusses a survey of the literature on the evolution of the gender wage gap in the early entrance in the labor market. Section two describes the data used in the econometric analysis and the methods used. Section three presents the results. Section four discusses the results while drawing some implications for national and international policy makers. Section five provides concluding remarks.

2