rafmacalaba/datause-encoder-data huggingface dataset:
unemployment record. Fixed-term internships typically last nine months. Thus, in order to assure strict comparability, in pre-1992 data but not in post-1992 data pre-internship unemployment spells experienced by young people who re-register as unemployed following completion of their internship generally should have been erased. Although this means that the 1992 unemployment inflow data are not wholly comparable to those for earlier years, this is not a serious problem.

The Slovenian data compare favorably with data used in other studies of the effects of unemployment insurance. First, both recipients and non-recipients of unemployment insurance are included. Second, it covers a complete duration of a spell, not only the portion in insured unemployment, and it distinguishes between exits to employment and non-employment. Third, it covers the period both before and after the change of the UI law. The change provides exogenous variation in benefits and thus helps identify the effects of potential duration on job exit rate.

# **4.** EMPIRICAL ANALYSIS

# **Non-parametric analysis**

One way to explore a possible association between unemployment insurance and duration of unemployment is by comparing survival function for different groups of unemployed, that is, using non-parametric analysis. I use Kaplan-Meier survival functions, showing the probability of still being unemployed at a given duration of unemployment spell (see, for example, Kalbfleisch and Prentice, 1980). I focus on escapes from unemploymen **t** o employment, and thus treat escapes to out of labor force as censored observations (together with true censored observations).

Let us first examine possible differences in surviving in unemployment among recipients and non-recipients of unemployment compensation (Figure 2). The two survival functions differ from each other with statistical significance. The function for non-recipients is below the one for the recipients, indicating higher cumulative exit to employment. But for durations of more than 24 12
# FIGURE 2 Pass-through during significant currency appreciations

A. Unconditional pass-through from different appreciation episodes, 1998-2017 B. EMDEs: Unconditional pass-through from different appreciation episodes, 19982017 <!-- Start of picture text --> Ratio 5-10 percent<br>0.3 10-20 percent<br>0.2<br>0.1<br>0.0<br>-0.1<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br><!-- End of picture text --> C. Frequency of significant exchange rate appreciations: Advanced economies <!-- Start of picture text --> Percent of countries<br>40<br>5-10 percent 10-20 percent<br>30 >20 percent<br>20<br>10<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018<br><!-- End of picture text --> D. Frequency of significant exchange rate appreciations: EMDEs <!-- Start of picture text --> Percent of countries<br>40<br>5-10 percent 10-20 percent<br>30 >20 percent<br>20<br>10<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018<br><!-- End of picture text --> Note: Appreciations are defined as positive quarterly changes in the nominal effective exchange rate. The sample comprises 34 advanced economies and 138 EMDEs. EMDEs = emerging market and developing economies; GDP = gross domestic product; IMF = International Monetary Fund; IT = inflation targeting. A.B. Pass-throughs are defined as the change in consumer prices after one quarter divided by the cumulative depreciation of the nominal effective exchange rate following significant depreciation episodes. The markers refer to the median pass-through associated with different appreciation episodes.

B. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF. Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

25
owned businesses, and their relatively higher (compared to male-owned firms) returns from producing or selling under contract and from security payments have narrowed the productivity gap. It is important to note that a sizeable portion of the productivity gap is explained by the location fixed effects or is unexplained by the covariates in the data.

The study builds on the few others that have explored gender gaps in labor productivity among informal firms. Many of these studies have documented gender gaps in informal firms in rural areas (Nagler and Naude, 2017; Rijkers and Costa, 2012). In contrast, this study examines informal firms in largely urban areas. Nagler and Naude (2017) use household enterprise data in rural areas for four Sub-Saharan African economies - Ethiopia, Malawi, Nigeria, and Uganda and find that enterprises owned by women are less productive than those owned by men. Rijkers and Costa (2012) similarly find gender gaps for rural informal enterprises in Ethiopia, Bangladesh, Indonesia, and Sri Lanka. However, with the addition of control variables, half the gender gap disappears in Ethiopia, and no gender gap is observed in Bangladesh and Sri Lanka. Some studies are also region-specific. Bruhn (2009) explores the gender gap in productivity in 9 economies in Latin America and the Caribbean, with 3 economies - Mexico, Peru and Bolivia including data on informal firms. The study finds that female-owned micro and small enterprises are less productive than male-owned enterprises in Mexico (9 percent) and Peru (16 percent) but not in Bolivia. Hallward-Driemeier (2013) explores the gender gap in 19 Sub-Saharan African economies using enterprise modules of various labor force surveys as well as surveys of micro-enterprises that are assumed to be all informal. The study finds that female-owned enterprises are 50 percent less productive than male-owned enterprises. The corresponding gap is 6 percent for the formal sector, implying that the gender gap may be much larger in the informal than formal sector. However, the gender gap in the informal sector narrows considerably after including controls. Finally, there are 6
Lewis and Lockheed (Hanushek and Kimko 2000; Klasen 1999; World Bank 2001; Hill and King 1995; Dollar and Gatti 1999). More recently, as enrollments in primary education have become universal, attention has switched from looking at the development effects of school enrollments to the effects arising from completed primary or secondary school or from actual measures of what has been learned in school (Pritchett 1977; Hanushek 2006; Hanusheck and Woessmann 2007). Schooling has a powerful effect on growth; Hanushek and Woessmann estimates that each year of schooling boosts long-run growth by .58 percentage points. But what is learned is more important; countries with test scores that are one standard deviation higher than the average also have a 2 percent higher growth rate in GDP per capita (Hanushek and Woessmann 2007).

Returns to girls’ education in developing countries are largely positive and in some cases exceed those observed in developed countries. Educated women are more likely to enter the formal labor market, where earnings exceed those of informal or home-based work (Malhotra, Grown, and Pande 2003). And women with secondary schooling see significant returns (Psacharopoulos and Patrinos 2002; Schultz 1993, 2002; Lloyd and others 2005). In countries with a tradition of dowries or bride prices the perceived value of a potential bride grows with education (Schuler 2006; Behrman and others 1999). Even in the agricultural sector, where most farmers in developing countries are women, education raises productivity, and the returns to women’s education exceeds those of men (Lockheed, Jamison, and Lau 1980; Quisumbing 1996). Moreover, improvements in women’s farming methods have reduced infant malnutrition (Smith and Haddad 1999).

The social impact of female education is profound. Most prominent is the role of mothers’ education in lowering fertility, reducing infant and child mortality, and promoting children’s education. Educated girls tend to marry later, and they are more likely to plan their families, improving reproductive health and lowering fertility. On average, infant mortality declines 5–10 percent for each year of girls’ education (Schultz 1993). One study in Africa found a 40 percent boost in child survival for mothers with five years of primary education (Summers 1994). Crosscountry studies by Klasen (1999) and Hill and King (1995) show similar results.

Hannum and Bachmann’s recent review of this literature observes that “countries with higher rates of female schooling and lower child mortality experienced substantial reductions in fertility and desired family size” (Hannum and Bachmann 2006:514). Klasen (1999) and Lloyd, Kaufman, and Hewett (2000) show that surges in education almost always precede the transition 3
Within subgroups, the participation rates in rural areas were highest for married women in the age range of 25 to 65 years, irrespective of their income class. Moreover, trends in the FLFP rate of richer and poorer households are very similar. The participation rates of the youngest age group (15 to 24 years) were low in comparison to older groups, however, their decline was sharper during the period of 1993-94 to 2011-12 than that of the older groups. The participation rate of unmarried young women declined the sharpest during this period, for those in both the lower and upper income categories. In conclusion, we observe that the locational difference, or specifically, the rural-urban difference, accounts for the largest drop. More interestingly, the same socio-economic and demographic factors play differential roles across location.

**<u>Table 13: Group Specific FLFP Rate</u>**

|Location|Economic<br>Status|Age Group|Marital Status|1993-94|1999-00|2004-05|2009-10|2011-12|
|---|---|---|---|---|---|---|---|---|
|||Between 15-24|Not married|43.5|38.3|40.3|24.7|20.8|
||Bottom 40%||Married|45.1|43.3|42.6|30.5|29.3|
|Rl||Between 25-65|Not married|43.1|38.9|42.7|40.8|35.5|
|ura|||Married|55.9|55.8|59.6|42.7|41.4|
|||Between 15-24|Not married|34.4|26.9|30.4|19.7|17.1|
||Top 60%||Married|44.3|36.2|39.6|28.0|26.0|
|||Between 25-65|Not married|43.2|40.3|43.7|43.2|40.5|
||||Married|52.2|47.2|52.2|41.8|40.3|
|||Between 15-24|Not married|25.6|19.1|27.5|19.7|20.9|
||Bottom 40%||Married|21.9|18.3|20.8|13.7|10.4|
|Urbn||Between 25-65|Not married|36.9|33.6|38.9|33.3|34.4|
|a|||Married|32.0|30.1|34.2|22.9|23.9|
|||Between 15-24|Not married|16.1|15.4|19.3|13.6|14.1|
||Top 60%||Married|13.3|13.0|16.7|12.0|12.7|
|||Between 25-65|Not married|31.1|29.9|31.9|28.9|31.3|
||||Married|19.8|19.3|22.6|18.5|19.5|

Source: Authors’ own estimate based on NSSO data. Note: ‘Married’ refers to currently married females and ‘not married’ refers to females who are currently not married (e.g., never married, widowed, etc.).

This multidimensional dissection of the data on women’s labor force participation in India leads us to several important conclusions. First, there has been a national decline in FLFP, and the bulk of this drop occurred in rural areas. Second, an increase in educational enrollment can successfully 25
**Figure 2: Deviation Exports/GDP, Latin American countries**

(actual versus predicted value) <!-- Start of picture text --> 27.6<br>Argentina<br>15.4<br>26.3<br>Uruguay<br>15.9<br>22.7<br>Colombia<br>23.0<br>20.2<br>Bolivia<br>-1.9<br>19.5<br>Peru<br>9.7<br>18.5<br>Venezuela, RB<br>19.6<br>7.6<br>Chile<br>-4.1<br>6.5 Difference 2000<br>Mexico<br>10.8 Difference 2008<br>6.5<br>Brazil<br>4.3<br>5.1<br>Ecuador<br>6.3<br>1.0<br>Paraguay<br>-11.0<br>-20 -10 0 10 20 30<br><!-- End of picture text -->

# **Export growth by categories**

Official statistics in Peru break exports down among traditional and non-traditional products.<sup>9</sup> Traditional products are essentially commodities which Peru has produced and exported historically, reflecting its comparative advantages associated to the relative abundance of resources. Examples include concentrates of and refined copper, lead, zinc, iron, tin, gold, silver, fishmeal and oil, among others.<sup>10</sup> 9 The distinction between traditional and non-traditional exports does not respond to a formal analytical criterion, but in practice it is used frequently because traditional exports are an approximation of the basic raw materials that were responsible for almost all exports since the end of the 1970s (especially minerals and agricultural commodities) while non-traditional exports are the remaining exports, which though using traditional raw material as inputs have a greater value added than traditional exports.

10 Including agricultural products whose relative relevance has been declining over time, such as sugar, cotton and coffee.

8
|**Financialyear**|Total|Tonga|Samoa|Vanuatu|Kiribati|Tuvalu|Solomons|Other Countries|
|---|---|---|---|---|---|---|---|---|
|2007/08|4486|805|647|1698|69|99|238|930|
|2008/09|6821|1355|1228|2342|38|49|311|1498|
|2009/10|6216|1142|1021|2137|48|54|256|1558|
|2010/11|7091|1411|1219|2352|149|51|252|1657|
|2011/12|7009|1398|1162|2412|142|88|407|1400|
|2012/13|7456|1573|1137|2829|138|56|423|1300|

<mark>Source: New Zealand Department of Labour, http://www.dol.govt.nz/initiatives/strategy/rse/information.asp</mark> Vanuatu has supplied the most workers to the RSE, which is somewhat unexpected given that Tonga and Samoa both had initially much stronger ties to New Zealand through existing migrant networks, and through permanent migration programs. The positive experience of Seasonal Solutions, a large co-operative, in hiring ni-Vanuatu during the initial pilot of 45 workers seems to have been important here. A common theme in discussions with officials from several countries is that an employer-driven scheme like the RSE can see a supplying country quickly gain momentum if workers from that country establish a good reputation. For example, when exploring how the Solomon Islands became the fourth largest supplier, despite the initial absence of any facilitation measures and a limited diaspora in New Zealand, officials in Honiara repeatedly stressed that the good performance of the initial group caused employers to recruit more from this country. It also may have helped that the largest single RSE, with a labor demand of approximately 1,000 workers, was one of the first employers of Solomon Islanders (as part of a mixed workforce of other Pacific Islanders) and is one of the few RSEs with sufficient scale to have large increases in demand for labor from a supplying country.

Another lesson from the data in Table 2 concerns the importance of geography and costs. Despite the kick-start efforts, the numbers of workers coming from the more remote countries of Kiribati and Tuvalu have been relatively small. Bedford et al. (2010) discuss the case of Tuvalu, and note a number of factors for low recruitment, including the larger geographic distance and small population size, and a lack of pro-active actions by the government of this country. For Kiribati, the constraining distances are not only for the international travel (which is recognized by allowing a longer worker period in New Zealand), but also for domestic travel in order to be recruited, given that the Gilbert Islands region of Kiribati is several thousand kilometers from the capital city. The increase in workers from Kiribati since 2010 reflects deliberate efforts by the New Zealand authorities, under the Strengthening Pacific Partnerships program, to target 12
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# **6. Concluding Remarks**

This paper reviewed the extensive literature on the cyclical properties and determinants of skill premiums and returns to schooling, which are highly focused on high-income countries. The few studies that look at LAC economies specifically appear to present contradictory evidence and rely exclusively on analyses of the evolution over time of standard Mincerian estimates of returns to schooling.

The research presented here is somewhat unique in its use of a pseudo-panel approach to the estimation of the permanent, trend and cyclical components of RTS. While not totally bullet-proof, this approach is appealing insofar as it gets rid of ability biases in the estimates of RTS, and can be implemented for any developing country with repeated cross-sections of household employment surveys.

In addition, the analyses attempted to link four types of transitory economic fluctuations with the cyclical components of RTS in models that controlled for time invariant country characteristics and timeperiod effects common to all observations from a given year. The evidence suggests that temporary fluctuations in foreign demand for LAC exports are perhaps the most important driver of cyclical movements of RTS within countries.

It is worth recalling that changes in the average relative wage of skilled labor relative to unskilled labor can be due to changes in the number of jobs and the composition of workers that remain employed. Indeed, the results presented in this paper are consistent with labor and trade models driven by compositional changes rather than changes in the wages of workers that remain employed throughout the cycle. Furthermore, trade—and to a lesser extent inflation—appear to be the main drivers of transitory fluctuations in returns to schooling.

The evidence also suggests that flexibility of the labor market plays a role in the transmission of foreign real shocks. More flexible labor markets seem to be a factor that amplified the response of RTS when there are variations in foreign demand. Thus, countries that have flexible labor markets and experience a considerable volatility of their external sector might also experience a greater volatility of RTS, which might be reflected in greater volatility of income distribution and a higher risk of disinvestments in human capital.

16
Table 11 Testing for Sensitivity of Results to Country Size, Using Six Year Averages and Trade Policy 1/

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|TRI|0.03<br>(.11)|-|-|-|-|-|-|
|TRI*Slze|5.00**<br>(2.35)|<br>-|-|-|-|-|-|
|TR II|-|0.0<br>(0.0)|-|-|-|-|-|
|TRII*Size|-|0.05<br>(.16)|-|-|-|-||
|Black|-|-|-0.02**<br>(0.0)|-|-|-||
|Black*Size|-|-|0.04<br>(.05)|-|-|-|-|
|Dollar|-|-|-|-0.06**<br>(.02)|-|-||
|Dollar*Size|-|-|-|.61<br>(.42)|-|-||
|Trade Shares|-|-|-|-|0.25*<br>(.10)|--||
|Trade Share*Size|-|-|-|-|-3.60<br>(3.36)|-||
|Indlr|-|-|-|-||29.44**<br>(7.97)||
|Indir*Size|-|-|-|-|-|-766.09<br>(367.80)||
|Movement towards<br>international prices||-|-|-|||0.04<br>(.03)|
|Movement*Size|-|-|-|-|-|-|0.71<br>(.82)|
|Land|-0.24<br>(.17)|0.07<br>(.47)|-0.01<br>(.11)|-0.25<br>(.12)|0.16<br>(.11)|-0.21<br>(.17)|-0.13<br>(.22)|
|Labor force|-0.07<br>(.58)|0.92<br>(.47)|0.82<br>(.27)|0.61<br>(.31)|0.59<br>(.27)|0.38<br>(.49)|-0.02<br>(.52)|
|Education<br>(0-3 years)|0.18<br>(.16)|-.22<br>(.09)|0.17<br>(.06)|0.15<br>(.10)|0.10<br>(.06)|0.16<br>(.11)|0.06<br>(.14)|
|Education|05|-0.10|0.04|0.03|0.00|0.09|0.02|
|(3-9 years)|(.09)|(.06)|(.04)|(.06)|(.04)|(.08)|(.08).|
|Capital Stock|0.50<br>(.08)|0.34<br>(.11)|0.43<br>(.04)|0.51<br>(.05)|0.44<br>(.04)|0.37<br>(.07)|0.53<br>(.07)|
|SLze|-0.42|-0.04|0.17|0.32|0.22|0.28|0.22|
||(.46)|(.22)|(.06)|(.13)|(.08)|(.32)|(.12)|
|_N_|67|32|231|125|263|67|102|
|<br>R-Square|<br>.96|<br>.62|<br>.90|<br>.94|<br>.89|<br>.96|.91|

}/ Levels of policy variables used for all variables except trade **shares,** indir, and movement towards international prices -- which urs changes in policies.

32
second is the relative higher weight of processed foods consumption, as was observed in Table _2_ , albeit the lower ultra-processed foods consumption.

_Table 8_ - Changes in medical expenditure

||**Processed and ultra-process**<br> <br>|**ed foods**<br>|
|---|---|---|
||Low<br>Medium|Upper|
|**1**|6.55%<br>6.56%|6.58%|
|**2**|3.17%<br>3.17%|3.18%|
|**3**|2.01%<br>2.02%|2.02%|
|**4**|1.45%<br>1.45%|1.46%|
|**5**|0.99%<br>1.00%|1.00%|
|**6**|0.70%<br>0.71%|0.71%|
|**7**|0.48%<br>0.48%|0.48%|
|**8**|0.31%<br>0.31%|0.31%|
|**9**|0.19%<br>0.19%|0.19%|
|**10**|0.06%<br>0.06%|0.06%|

Source: Estimates based on POF 2017/2018; Global Burden of Disease 2019; and SIH/SUS. Estimates assume a price shock of 20%.

Figure 5 depicts the progressive pattern of the long-term estimated medical expenditure changes after implementing a taxation on processed and ultra-processed foods. The lowest consumption decile presents the highest gains, with estimated changes in medical expenditure being almost twice as large as the second decile, three times as large as the third, and 113 times as large as the top consumption decile. While our analysis does not allow us to estimate fiscal impacts on SUS, the public health care system, it suggests that a taxation on processed and ultra-processed foods would have a positive impact in terms of reduced demands and medical expenditure.

_Figure 5_ - Changes in medical expenditure <!-- Start of picture text --> 7.00% 6.56%<br>6.00%<br>5.00%<br>4.00%<br>3.17%<br>3.00%<br>2.02%<br>2.00% 1.45%<br>1.00%<br>0.71%<br>1.00% 0.48%<br>0.31% 0.19%<br>0.06%<br>0.00%<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>Processed and ultra-processed foods<br>Change in medical costs<br><!-- End of picture text --> Source: Estimates based on POF 2017/2018; Global Burden of Disease 2019; and SIH/SUS. Estimates assume a price shock of 20%.

20