rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 5 Employment Patterns and Access to Pensions Across Rural and Urban Areas of China, Indonesia and Korea**

<!-- Start of picture text --> Panel A China<br><!-- End of picture text --> <!-- Start of picture text --> China (CHARLS)<br>Urban Male Urban Fmale<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45-46 49-50 53/54 57/58 61/62 65/66 69/70 45-46 49-50 53/54 57/58 61/62 65/66 69/70<br>age age<br>Pension Eligible Working/Total Pension Eligible Working/Total<br>Rural Male Rural Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45-46 49-50 53/54 57/58 61/62 65/66 69/70 45-46 49-50 53/54 57/58 61/62 65/66 69/70<br>age age<br>Pension Eligible Working/Total Pension Eligible Working/Total<br>rate rate<br>rate rate<br><!-- End of picture text --> <!-- Start of picture text --> Panel B Indonesia<br>Indonesia<br>Urban Male Urban Fmale<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45-46 49-50 53/54 57/58 61/62 65/66 69/70 45-46 49-50 53/54 57/58 61/62 65/66 69/70<br>age age<br>Pension Eligible Working/Total Pension Eligible Working/Total<br>Rural Male Rural Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45-46 49-50 53/54 57/58 61/62 65/66 69/70 45-46 49-50 53/54 57/58 61/62 65/66 69/70<br>age age<br>Pension Eligible Working/Total Pension Eligible Working/Total<br>(Only Include Adults Who Directly Reported To The Employment Survey)<br>Panel C Korea<br>Korea<br>Urban Male Urban Fmale<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45-46 49-50 53/54 57/58 61/62 65/66 69/70 45-46 49-50 53/54 57/58 61/62 65/66 69/70<br>age age<br>Pension Eligible Working/Total Pension Eligible Working/Total<br>Rural Male Rural Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45-46 49-50 53/54 57/58 61/62 65/66 69/70 45-46 49-50 53/54 57/58 61/62 65/66 69/70<br>age age<br>Pension Eligible Working/Total Pension Eligible Working/Total<br>rate rate<br>rate rate<br>rate rate<br>rate rate<br><!-- End of picture text --> Notes: Calculated using the 2008 CHARLS pilot (China), 2007 IFLS (Indonesia) and the 2006 KLoSA (Korea).

33
<!-- Start of picture text --> SSA's Exporting Countries: Real Non-Oil GDP Growth<br>(Percent)<br>12<br>= Li<br>Se ae,<br>= — ==<br>;<br>1997-2002 2003 2004 2005 2006 2007 2008<br>—@ Angola —TM—Cameroon —te—Chad —— Congo, Rep<br>——EQG —®— Gabon —— Nigeria<br><!-- End of picture text -->
Unlike equation (2), equation (3) includes the interaction term between informal competition and country characteristics of interest captured by Z. It also includes as controls the interaction terms between informal competition and country and firm characteristics. These controls ensure that our main interaction term in the equation (involving informal competition and Z variables) is not spuriously picking up the differential impact of informal competition depending on factors such as income (GDP per capita) and firm size. Equation (3) is estimated using maximum likelihood estimation method. All regressions use Huber-White robust standard errors clustered on the country-year.

In the online Appendix, a formal definition of all the variables is provided in Table A2 and summary statistics are provided in Table A3.

# 4.3 _Dependent variable_

The dependent variable is a dummy variable equal to 1 if the firm had an internationally recognized quality certificate at the time the WBES was administered and 0 otherwise ( _Quality Certificate_ ). The data source for the variable is WBES. About 10.4 percent of the firms in the baseline sample have a quality certificate.

19
Borrowers were then sorted for each model specification according to two different criteria: (A) to determine the number of borrowers who would default at given acceptance rates; and (B) to estimate the number of firms that would receive credit at a given default rate.

Table 4A presents information on the percent of borrowers who would be expected to default at given approval rates for Argentina and Brazil. The exercise shows that for any target approval rate, a loan portfolio selected using the complete information model has a lower default rate than the one selected using only negative information. For example, in the case of Argentina, if one wanted to extend credit to 60% of the sample population, then having access to positive information would reduce the default rate from 3.81% to 2.98%, a reduction of approximately 20% on portfolio losses.

In Table 4B the gains from using positive information in terms of increased access to credit is demonstrated, controlling for the riskiness of borrowers. In this ex-post exercise, borrowers again are sorted in order of increasing predicted default probability and the percentage of approved loan applications is computed once the portfolio default rate reaches the set target. In both cases, more borrowers gain access to credit for a given default rate when using the complete information model than in the case of only negative information. Taking the case of Brazil, if a target default rate of 3% is desired, then the full model permits more than 82% of the sample population to obtain credit whereas this figure is only 55.8% for the negative only model – a difference of more than 30% in terms of providing access to credit.

# **_Individual bank versus  shared information_**

Having established the importance of including both positive and negative information, we now turn to the more general issue: the value of sharing information. Using data from the Argentine PCR we estimate the following equation for individual financial institutions:

# Pr. ( Deafult ) _i_ =  αINDIVIDUAL _i_ + βSHARED _i_ + e _i_

information could not be used for defining dependent variable. It was used for designing explanatory variables.

10
costs of higher education vary so widely across countries and institutions, and this information would be crucial to identify a threshold that financial aid needs to cover to increase access and graduation rates of disadvantaged students.

Nevertheless, most of the evidence discussed here is quite recent and this literature is growing quickly. We therefore hope that more precise conclusions and policy recommendations could be drawn in the coming years. Overall, the available evidence from the (quasi‐)experimental literature is encouraging for the institutional and political leverage to reduce inequality in higher education. Although some of the inequalities discussed here may arise very early in the life course, our results highlight the possibility, and perhaps the necessity, to also tackle education inequalities later. Well‐designed interventions in high school and higher education can thus bring about substantial improvements in the difficult educational careers of disadvantaged students.

# **References**

- Abbiati, G., Argentin, G., Barone, C., & Schizzerotto, A. (2017). Information barriers and social stratification in higher education: evidence from a field experiment. _The British Journal of Sociology_ , (0).

- Abbiati, G., & Barone, C. (2017). Is university education worth the investment? The expectations of upper secondary school seniors and the role of family background. _Rationality and Society_ , _29_ (2), 113–159.

- Alon, S. (2007). The influence of financial aid in leveling group differences in graduating from elite institutions. _Economics of Education Review_ , _26_ (3), 296–311.

- Alon, S. (2011). Who Benefits Most from Financial Aid? The Heterogeneous Effect of Need‐ Based Grants on Students’ College Persistence. _Social Science Quarterly_ , _92_ (3), 807– 829.

- Anderson, D. M., & Goldrick‐Rab, S. (2016). _Aid After Enrollment: Impacts of a Statewide Grant Program at Public Two‐year Colleges_ . Retrieved from http://www.wihopelab.com/publications/Anderson‐Goldrick‐Rab‐2016‐Impacts‐of‐ Statewide‐Grant‐Program.pdf

- Andrews, R. J., Imberman, S. A., & Lovenheim, M. F. (2016). _Recruiting and Supporting Low‐ Income, High‐Achieving Students at Flagship Universities_ (Working Paper No. 22260). National Bureau of Economic Research.

- Attewell, P., Lavin, D., Domina, T., & Levey, T. (2006). New Evidence on College Remediation. _The Journal of Higher Education_ , _77_ (5), 886–924.

- Avery, C. (2010). _The Effects of College Counseling on High‐Achieving, Low‐Income Students_ (Working Paper No. 16359). National Bureau of Economic Research.

34
2 in Section IV along with a brief discussion of some descriptive statistics. Section V contains the estimation results and we conclude in Section VI.

# **II. The Background**

## **A. Tunisia's Demographic Transition**

Tunisia has progressed quite far in the demographic transition since Independence in 1956. The crude lirth rate has fallen from 46 to 30 and the crude death rate from 23 to 7. Although this represents an unchanging rate of natural increase of 16 ptr 1000, the crude death rate is unlikely to fall much more, while the fertility rate will probably continue to decline. The determinants of contraceptive use and fertility are the major foci of this paper. The role of the family planning program and of access to family planning are of major policy importance for the future decline of fertility in Tunisia and for generalizing from this experience to other countries. It is, therefore, important to put the Tunisia case into context.

Fertility in Tunisia has declined at a pace similar to that of the average lower middle income countries. The Total Fertility Rate (TFR) of 4.1 is almost identical to that of the average of similar countries. Yet one might have expected that fertility would be even lower for several reasons: 1) Tunisia has a strong family planning program and was ranked 14th out of 99 countries on family planning program effort in 1982 ('.lauldin and Lapham 1987). 2) It has almost 60 percent of its population in urban areas and has infant mortality below that of the average country at its level of development. 3) It has also had a program of social reform that seems to be exactly what would be recommended by those who stress the importance of creating the demand for family planning through social change. These legislative and programmatic changes, outlined in Table 1, have occurred much earlier than ir other countries of the region.

Despite this legislative change, female schooling has expanded slowly until recently. In 1965, Tunisia had a female enrollment rate at the primary level 75 percent that of other lower middle income countries. For secondary schooling, female enrollment rates were less than half of those of comparable countries. By 1988, almost universal primary enrollment had been achieved and female enrollment rates at the secondary level had reached almost 80 percent of those of comparable countries. These changes have begun to have an impact on adult women. Whereas 77 percent of the women in the World Fertility Survey (WFS) of 1978 had had no schooling, by 1988 the DHS sample had only 57 percent without schooling. The proportions with primary and some secondary schooling had doubled over the period from 16 percent to 31 percent and 7 percent to 12 percent, respectively. This is substantial progress, but these are still very low levels of education for a country that has achieved a TFR of 4. This is graphically illustrated in Figure 1, which shows that the TFR is below what would be expected on the basis of the level of femiale illiteracy. Thus, it appears that Tunisia has in some sense
in comparison to almost a doubled male attainment of 6.03 years on average.<sup>19</sup> Increasing women’s mean education by 2.8 years to eliminate the gender education gap would increase women’s output by an estimated USD 338 million, making women’s total contribution to GDP equivalent to USD 1.25 billion or 28.6 percent of GDP (up from 26.8 percent under the status quo).

The estimated growth due to the closure of the education gender gap is driven by a variety of changes. Wages increased by 0.3 percent across the gender-disaggregated sub-sectors. However, the variation in the sectoral composition of the economy by gender contributed most to the growth in women’s output. The modern-sector output of women increased by 62.4 percent and reached USD 708.8 million as the entire share of the workforce in the modern sector would have increased from 29.5 percent to 41.7 percent. At the same time, women’s participation in the traditional sector would have declined, with their contribution to traditional output declining by 15.9 percent from the status quo scenario.

The dividend would be less, but still considerable, if the gender gap in education were equal to the average found in Sub-Saharan Africa (SSA). Whereas women in SSA have 77 percent of the years of education achieved by men<sup>20</sup> (Barro & Lee, 2013), such share stands at 41 percent for Liberia. By reaching the level found in SSA, the Liberian GDP would increase to USD 4.25 billion and the aggregate, per-capita and per-worker output would experience a growth of 5 percent.

Even larger gains would be achieved if Liberia’s gender education gap were to mirror that of OECD countries. The gender hierarchy in educational achievement in the OECD has recently reversed, where more (young) women than ever are attaining formal education (OECD, 2015). If the women-tomen divergence found in the OECD would be achieved in Liberia, GDP would be 8.65 percent higher.

**Figure 1: Selected macro-economic changes due to complete convergences of the education gender gap (in USD, growth in %)**

<!-- Start of picture text --> $5,000<br>4,388<br> $4,500 85%<br>62.4%<br> $4,000<br>65%<br> $3,500<br>45%<br>31.8%<br> $3,000<br> $2,500 25%<br>8.4%<br>1.8%<br> $2,000 5%<br> $1,500 1,254 -15.9% -18.4% 1,262<br>-15%<br> $1,000 709<br>545 600<br>-35%<br> $500<br> $- -55%<br>GDP Female Trad. Mod. Trad. Mod.<br>Female Male<br>Source: author’s calculations<br>Millions<br>Growth (in %)<br><!-- End of picture text -->

> 19 To allows for the 5-year cohort projections, the Barro & Lee 2013 data were used. The value is slightly lower than the more recently measured (2016 HEIS) estimates presented in the introduction section.

> 20 The sample adopted is the one for which countries were available in the latest estimates of the dataset and excluded: Comoros, Djibouti, Eritrea, Ethiopia, Madagascar, Mayotte, Réunion, Somalia, Angola, Chad, São Tomé and Príncipe, Western Sahara, Burkina Faso, Cabo Verde, Côte d'Ivoire, Guinea, Nigeria.

16
<!-- Start of picture text --> 04 |<br>\<br>Sc 02 i!<br>seigos !i|<br>Fefol<br>a= ° wnby ar Sa Se Sn<br>uv 8 ]<br>i=}5 1<br>% |<br>-02 |<br>|<br>8 TFT 6 5 4 #3 2 A 0 1 2 3 4 5<br>Years relative to MNREGA<br>-® Pre-trend coefficients -- Treatment effects<br><!-- End of picture text -->

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|<br>A |<br>fo}c |\<br>$8oO 05 i\<br>£8c=gaoF !i|<br>Fe=<br>235 0 ee:<br>us ]<br>oFog 1<br>x2 |<br>= 05 | :<br>8<br>|<br>|<br>8 TFT 6 5 4 #3 2 A 0 1 2 3 4 5<br>Years relative to MNREGA<br>-® Pre-trend coefficients -- Treatment effects<br><!-- End of picture text -->
**Figure 18. Simulated percentage change in earning as a result of the minimum wage increase**

<!-- Start of picture text --> 0.24<br>0.12<br>0.06<br>0.03 0.02 0.01 0.00 0.02 0.00<br>0.00<br>250 230 212 180 (137.6) for 35+(34-) 50% of the Median Wage<br>(193.6)<br>Minum wage (Gross, 2018 Euros)<br>Short-run Long-run<br><!-- End of picture text --> _Source: Own estimates using the 2018 Kosovo LFS._

**Figure 19. Simulated percentage point change in unemployment rate as a result of the minimum wage increase**

<!-- Start of picture text --> 0.56<br>0.38<br>0.31<br>0.21<br>0.16<br>0.11<br>0.05 0.07<br>0.00 0.00<br>250 230 212 180 (137.6) for 35+(34-) 50% of the Median Wage<br>(193.6)<br>Minum wage (Gross, 2018 Euros)<br>Short-run Long-run<br><!-- End of picture text --> _Source: Own estimates using the 2018 Kosovo LFS._

# **V. Conclusions and Policy Insights**

**Currently the minimum wage in Kosovo is 130 euros for people under the age of 35 and 170 euros for people 35 and older; this minimum wage is relatively high, when measured as the ratio of the minimum wage to the median wage.** <mark>The ratio of the minimum wage to the median wage in Kosovo is slightly lower than this ratio in European countries: 0.38 for those aged 15-34 and 0.40 for those aged 35-64.</mark> The minimum wage has not changed since 2011 and has not grown with the inflation rate. In this study we analyze the potential employment, income, and poverty effects of an anticipated minimum wage increase.

## **Kosovo’s labor market is characterized by high unemployment and informality rates, which must be**

**taken into consideration when designing minimum wage policy** . According to Rutkowski (2003), in countries with high unemployment among young and low-skilled workers, <mark>the ratio of the minimum wage</mark> to the median wage should not surpass one-third. <mark>High and “binding” minimum wages may</mark> hinder job creation and/or increase job destruction among low-skilled workers. This could result in even higher rates of unemployment and informality. Moreover, given the high unemployment rate, a minimum wage increase might

32
||||Use of|Loans|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Micro|-0.03|0.0006 *|-0.03|-0..02|10.102|-'0..03|
||(-1.09)|(-2.24)|(-1.29)|(-0.87)|(-0.69)|(-1.27)|
|Qwner's Education|10.106 FFF|0.02|0.06* **|0.06***|0.05***|10.1006 °F|
||(-3.99)|(-1.26)|(-3-91)|(-3.53)|(-2.83)|(-4.27)|
|Owners Job in Fonmal Sector|0.017<br>|0.04<br>|O.01<br>|0.02<br>|0.02<br>|O.01<br>|
||(-0.28)|(-1.29)|(-0.50)|(-0.69)|(-0.65)|(-0.40)|
|Ln Age|0.01|0.01|0.01*|o.01*|0.01|0.01**|
||(-1.54)|(-1.46)|(-1.89)|(-1.84)|(-1.44)|(-2..00)|
|Female Owner/Decision Maker|0.03*|0.03**|0.03**|0.03**|0.03**|0.03**|
||(-1.93)|(-2..06)|(-2.12)|(-2.07)|(-2.08)|(-2.16)|
|Manufacture|-0.007<br>|-0.01<br>|0.01<br>|0.0<br>|10.0 F<br>|0.101<br>|
||-0.06|(-0.53)|-0.42|(-0.14)|-'0.23|-0.44|
|LAC Region|10.00|0.08|0.06***|0.08***|o.07***|0.008"<br>**|
||(-4.53)|(-1.53)|(-4.22)|(-4.33)|(-3.74)|(-4.92)|
|Private Credit to<br>GDP||0.001*|||||
|||(-1.87)|||||
|Financial Freedom|||0.002***||||
||||(-4.99)||||
|Contract Viability/Expropration||||oO.04+**|||
|||||(-3.32)|||
|Law and Order|||||0.03***||
||||||(-3.21)||
|Property Rights||||||o.001***|
|||||||(-3.12)|
|PseudoR2|0.09|0.04|0.06.|0.04|0.05|o.04|
MINIMUM WAGES IN CAMBODIA 30 Table A3. Impact of minimum wage hikes at the household level

|lo|g(monthly wage)<br>(1)|log(hours)<br>(2)|log(hourly wage)<br>(3)|
|---|---|---|---|
||_All workers_|||
|Log(minimum wage)|-0.086|-0.039|-0.048|
||(0.249)|(0.241)|(0.186)|
|x garment|0.241***|-0.032|0.293***|
||(0.069)|(0.070)|(0.071)|
||_Female worker_|_s_||
|Log(minimum wage)|-0.221|-0.371|0.128|
||(0.263)|(0.250)|(0.252)|
|x garment|0.222**|0.056|0.181**|
||(0.091)|(0.092)|(0.092)|

_Source_ : Author’s calculations from Cambodia SES 2007-2015. _Note_ : The table presents the estimates of _β_ and _δ_ of estimating: _Yijyt_ = _αG_ + _β_ log(minwage)+ _δ_ ( _G ·_ log(minwage))+ _δy_ + _δm_ + _δj_ + _t·δj_ +( _t · G_ ) _·δj_ + _X ·ξ_ + _χshock_ +( _χshock · G_ )+ _εijyt_ . Sample restrictions and additionals covariates are identical to Table 2. Standard errors, clustered by survey year _×_ psu shown in parentheses. ***: p _<_ 0.01; **: p _<_ 0.05; *: p _<_ 0.1.
53. To summarize, there is considerable evidence that the global savings glut did not cause the global imbalances.

**Figure 34: US Real Consumption Expenditures**

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2005 US$, trillions<br>1947-01-01 1949-07-01 1952-01-01 1954-07-01 1957-01-01 1959-07-01 1962-01-01 1964-07-01 1967-01-01 1969-07-01 1972-01-01 1974-07-01 1977-01-01 1979-07-01 1982-01-01 1984-07-01 1987-01-01 1989-07-01 1992-01-01 1994-07-01 1997-01-01 1999-07-01 2002-01-01 2004-07-01 2007-01-01 2009-07-01<br><!-- End of picture text --> Source: U.S. Department of Commerce, Bureau of Economic Analysis.

# **III.2 Hypothesis II: U.S. Policies Led to the Housing Bubble, Global Imbalance and the Global Financial Crisis**

54. Instead, it appears that global imbalances reflected policies and structural factors affecting savings in the United States. Lin, Dinh and Im (2010) argue that the housing bubbles were triggered by the Fed‘s low interest rate policy following the burst of the ―dot-com‖ bubble in 2001 and magnified by the financial deregulation in the 1980s, which will be discussed later. In combination with a range of policies aimed at expanding the availability of mortgages to lowincome borrowers in the subprime market, the above policy changes led to excessive risk-taking and higher leverage, resulting in excess liquidity, and ―bubbles‖ in both housing and equity markets in the United States. The wealth effect of these bubbles enabled US households to overconsume, which, together with the public debts arising from the Afghanistan and Iraqi wars and a range of tax cuts, increased the United States‘ current account deficits. If this hypothesis is correct, a causal link needs to exist between monetary, fiscal and housing policies of the United States, and its large and growing current account imbalance.

55. The Fed‘s monetary policy had been loose from the 1980s and turned extremely expansionary when the burst of the technology bubble in early 2000 caused a recession. In the run-up to the bubble‘s burst, and as stock prices continued to rise, the Fed did not intervene. However, when the bubble burst, consumers experienced a significant wealth loss. With 41
produces an optimal mix of energy supply sources (e.g., coal, oil, gas, LNG, hydro, solar, wind, biomass) to meet the end-use energy demand (e.g., space heating, space cooling, lighting, electric motors, motive power) in various sectors (i.e., residential, commercial/service, industrial and transport). While determining the optimal energy supply- mix, the model simultaneously determines the cheapest path to transform/transmit/transport these energy commodities to energy end-uses.

**Figure 1: The Framework for the TIMES Model**

<!-- Start of picture text --> Resources Transformation Utilization<br>Coal<br>Gasoline Cooking<br>Crude Oil Oil refinery Diesel Heating Residential<br>commercial<br>Air Cond.<br>Fuel Oil<br>Gas and<br>Natural Gas<br>Processing Kerosene Elec. motor industrial<br>sectors<br>Hydro Electronics<br>LPG<br>Nuclear Power Jet fuel Lighting<br>Plants<br>Others Transport- Transport<br>Renewables ation sector<br>Gas<br>Electricity<br><!-- End of picture text --> _Source: Timilsina and Jorgensen (2018)_ End-use energy demands in TIMES are projected using driving factors such as economic growth, population growth, expected structural change in the economy, energy efficiency improvement in different technologies. Government plans and policies, such as building energy efficiency standards, industrial process energy efficiency standards, vehicle mileage standards are taken into consideration while projecting the demand.

# **2.1.2. Modeling the electricity sector**

The TIMES model is frequently used in the literature for long-term power as well as energy system planning (see, for example, Timilsina and Jorgensen, 2018; Chen, 2005). Here 6