rafmacalaba/datause-encoder-data huggingface dataset:
NEET compared to more than 70 percent of their female counterparts—a very large difference that points to severe retrenchment on the women’s side. It can be seen that, as age increases, the malefemale gap widens markedly. Additionally, although this widening of the male-female gap holds both in 2007 and in 2018, in relative terms it is far worse in 2018 than in 2007, because of the great gains the girls made in early education but then lost as they entered their 20s. In 2018, about 10 percent of women aged 24 were in school, and fewer than 20 percent were working, compared to about 20 percent of their male counterparts in school and about 60 percent working.

Both Figures 3 and 4 portray a situation for Morocco not dissimilar from other MENA region countries, where the share of employed youth is higher among young men than young women, and the share of NEET is higher for young women. Interestingly, both in Morocco and in the rest of the region (Doss et al., 2018), when the NEET information is crossed with marital status, married women—either with or without children—represented a significant percentage of female NEETs. When this married group is removed from the NEET calculation, however, the share of female NEETs (for example, those who are neither married nor have children) becomes comparable to the share of NEET men.

<!-- Start of picture text --> Figure 4: School-to-Work Transition for Population Aged 15 to 24 years old, 2018<br>Male Female<br>100% 100%<br>NEET<br>80% 80%<br>School only<br>60% 60%<br>40% 40% NEET<br>20% 20% School only<br>Work only<br>Work only<br>0% 0%<br>15 16 17 18 19 20 21 22 23 24 15 16 17 18 19 20 21 22 23 24<br>Age Age<br><!-- End of picture text --> _Source:_ authors’ own elaboration based on the _Enquête nationale sur l_ ’ _emploi_ (ENE).

# 2. Logit regression

Following Bilgen Susanli (2016), we first estimate the probability of being NEET using a simple logit model based on a set of <mark>individual (e.g. age, gender, and level of education) and household characteristics, geographical location, and housing conditions.</mark> l l i i i . (1) ii <mark>The model calculates the probability that the dependent variable acquires value 1:</mark> ll(pp ) = ββ xx + εε 12
**<u>Table 3: Regression Results: OLS Estimation</u>**

||**Rem**|**/GDP**|**Log o**|**f Rem**|**Log of Rem**|**per capita**|**Rem/**|**GDP**|**Log**|**of Rem**|
|---|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(1)**|**(2)**|**(1)**|**(2)**|**(1)**|**(2)**|**(1)**|**(2)**|
|**Log of migrants abroad**|0.592|0.644|0.427***|0.444***|-0.007|0.008|||||
||[0.811]|[0.791]|[0.811]|[0.791]|[0.811]|[0.791]|||||
|**Migrants/Population**|||||||0.174|0.181|0.054**|0.058**|
||||||||[0.116]|[0.111]|[0.024]|[0.025]|
|**Bank deposit/GDP**|1.464||0.609||0.798||0.663||0.551||
||[1.329]||[0.667]||[0.773]||[1.392]||[0.690]||
|**Bank credit/GDP**||0.670||0.490||0.447||0.190||0.416|
|||[0.995]||[0.512]||[0.649]||[1.009]||[0.535]|
|**Log of GDP**|-1.012**|-1.035**|0.538***|0.527***|||-0.220|-0.199|0.920***|0.928***|
||[0.468]|[0.457]|[0.110]|[0.105]|||[0.341]|[0.326]|[0.116]|[0.118]|
|**Log of GDP per capita**|-0.721|-0.644|-0.400**|-0.410**|0.229|0.245|-1.298***|-1.266***|-0.683***|-0.701***|
||[0.461]|[0.453]|[0.165]|[0.174]|[0.148]|[0.174]|[0.323]|[0.336]|[0.178]|[0.195]|
|**GDP growth**|-0.232|-0.221|-0.05|-0.048|-0.039|-0.033|-0.192|-0.184|-0.041|-0.038|
||[0.254]|[0.259]|[0.066]|[0.067]|[0.075]|[0.074]|[0.258]|[0.263]|[0.069]|[0.071]|
|**Diff. in the ratio of tertiary edu.**|-0.04|-0.036|-0.011|-0.011|-0.033***|-0.031***|-0.058*|-0.056*|-0.026***|-0.026***|
||[0.039]|[0.038]|[0.011]|[0.011]|[0.011]|[0.012]|[0.030]|[0.029]|[0.010]|[0.010]|
|**Constant**|26.961***|26.485***|4.648**|4.807**|2.172|1.974|19.480**|18.845**|3.022|3.002|
||[7.960]|[7.863]|[1.868]|[1.858]|[1.838]|[1.972]|[8.147]|[7.833]|[2.298]|[2.344]|
|**Observations**|85|85|85|85|85|85|85|85|85|85|
|**R2**|0.285|0.280|0.636|0.635|0.289|0.281|0.321|0.320|0.618|0.617|
|**F-statistic (p-value)**|5.73 (0.00)|5.33 (0.00)|22.05 (0.00)|22.60 (0.00)|8.08 (0.00)|7.93 (0.00)|5.06 (0.00)|5.01 (0.00)|20.53 (0.00)|20.72 (0.00)|

Note: Figures in parentheses are robust standard errors. ***significant at 1% level, **significant at 5% level, and *significant at 10% level. The ratio of bank deposit to GDP is used in (1) and the ratio of bank credit to GDP is used in (2).

20
50. Under the above assumptions and rules, the minimum subsistence level for this family is SK12,790, and child allowances can amount to SK3,700/month. This implies that if both adults are unemployed the full household monthly income is SK16,490. Most important, should one of them find employment at the minimum wage level, that amount would continue to be the same because social assistance/support would complement their income to bring it up to the minimum subsistence level, and the family would still receive child allowances. In these circumstances, there is very little reason for either adult to look for a job, and even less so for both of them to do so, since that would deprive the famil of child allowances, making them worse off overall.

|**Benefitregime**<br>**Income**|**UI**<br>**First3 months**<br>**(RR****_50%)_**|**UT**<br>**Afterfirst3 months**<br>**(RR45%)**|**SA/SS**<br>**(Objective)**|
|---|---|---|---|
|**Adultl'sincome(MW)**|3,230<br>(0.90)|3,230<br>(0.90)|3,230<br>(0.90)|
|**HHincome(MW)**<br>|16,490<br>I__<br>_ _(1.00)|16,490<br>(1.00)|16,490<br>(1.00)|

_51._ Even though each example has its own specificities, a few general conclusions can be drawn from the analysis:

- (a) Individuals who expect to be re-employed at the minimum wage level have few incentives to look for a job (except maybe those whose spouse works for the average wage). Moreover, if we are willing to accept that workers on minimum-wage jobs are most likely workers with low levels of education, this means that the disincentive effects of unemployment insurance and social assistance/support should be strongest for this group;

- (b) Disincentives are stronger when receiving unemployment insurance than when receiving social assistance/support because the payments are larger under the former (i.e. replacement ratios are higher). However, the unlimited duration of social assistance/support, together with its conditionality, can also have pervasive effects, especially at the household level;

- (c) The smaller the worker's potential contribution to total household income, the more important the opportunity cost of working and the weaker the incentives for job search and further employment. This mechanism is specially relevant in the case of secondary earners;

- (d) Disincentive effects are aggravated by the presence of children and the payment of child (or parental) allowances, since these bring the replacement ratio of actual income to potential income closer to one, or even above one in the case of families with low levels of education/low income; and

- (e) Finally, all this implies that it is possible for certain households to be worse off when both adults are employed than when only one of them is because the potential net contribution to total household income of the secondary earner is very small, or even negative.

14
Although these beliefs, or “stereotypes,” may be held only on average, they would result in applicants from one group being treated differently than applicants from the other group, _even though_ they possess the same skills and potential productivity. Furthermore, these models, along with research in social psychology, claim that these beliefs and stereotypes may be selfconfirming—that is, when certain groups are stereotyped as less productive, these groups will become less productive because the payoff from investing in skills may depend on employers’ beliefs that likely—and arbitrarily—disregard such investments.

Finally, recent research in psychology suggests that the underlying source of prejudice is rooted in social identity. When a group is part of the social identity, in-group members may be favored, and out-group members can suffer from negative evaluations, even if these groups are completely arbitrary ones. Similar research suggests the existence of unconscious and unintentional biases triggered by exposure to out-group members. In all these arguments, socialization and social norms are the ultimate drivers of discrimination.

Although these theories attempt to offer explanations as to why one might see employer-based discrimination, compiling empirical proof that such discrimination exists is no easy task (see Bertrand and Duflo 2016). For example, earlier studies of discrimination simply regressed different individual-level outcomes on a “minority group” indicator and a host of other covariates as controls. It is now well known that interpreting the estimate of the “group” variable as discrimination is vulnerable to omitted variable bias, among other concerns, which would lead to biased estimates of the variables of interest. To address these limitations, two experimental methodologies have been used: audit and correspondence studies.

In _audit studies_ , pairs of individuals, often called auditors or testers, are matched—and trained— so that all relevant characteristics other than the one being investigated (such as gender) do not vary. The pair then applies for the job, and the entire job search experience, which contains a wealth of qualitative information, is carefully documented and analyzed. A study by Neumark, Bank, and Van Nort (1996) is the canonical example. Comparably matched pairs of men and women applied for jobs as waiters and waitresses at 65 restaurants in Philadelphia. The study found discrimination against female applicants in high-priced restaurants, but also discrimination in favor of female applicants in low-priced restaurants. Papers with audit-style research designs have found similar mixed results. Goldin and Rouse (2000) studied gender discrimination in hiring musicians 14
Figure 5: Contributions of Labor Reallocation and Sectoral Labor Productivity Increase to Economy-wide Labor Productivity Growth <!-- Start of picture text --> 0.06<br>0.05 D5<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>-0.02<br>-0.02<br>1981-83 1984-86 1987-89 1990-92 1993-95 1996-97<br>U Labor Reallocation *Sectoral Labor Productivity Increase<br><!-- End of picture text --> 20
> <sup>13</sup>

a substantially higher fraction of co-ethnic respondents express a partisan preference, the coefficient β1 will be large and significant.

Fixed country effects,  _j_ are likely to be (and are) large. The base specification controls for these. In general, the addition of group indicators to conditional likelihood estimators like the logit yields inconsistent estimates. However, this is only a concern when the number of groups is large and the observations in any group are small. In the sample here, with only 16 groups (countries) and more than a thousand observations per group, bias is not a concern (Chamberlain 1980).

The effects on rates of partisan preference of ethnic clustering and other control variables may also vary across countries:  _j_     _j_ . For a large number of countries, each with a relatively small number of observations, the appropriate econometric response is to take parameter heterogeneity explicitly into account with a generalized least squares approach. However, since the country-specific idiosyncratic effects  _j_ are likely to be uncorrelated across countries, it is inefficient to use GLS, particularly because the number of countries is small relative to the number of observations per country. Instead, to examine the possible effects of parameter heterogeneity, the final section of the paper simply reports country-by-country estimates.

The data on partisan preferences, ethnic clustering and the controls are taken from the 2005-06, third round of the Afrobarometer surveys. Afrobarometer uses a nationallyrepresentative sample (generally, 1,200 respondents, but in larger countries 2,400). Only this round includes questions key to the analysis here. For example, earlier rounds did not ask for the ethnicity of respondents, but instead asked respondents to name the social group with which they most closely identified (used in Eifert, et al. (forthcoming)), a question that, in turn, was not asked in 2005-06.
interested in. However, it is worth<sup>noting thatexante analysiscanalsobe usefully</sup> informed by the application of the<sup>same techniquesex posttosee if thepredicted effects</sup> actually did occur. Such ex post<sup>analysis can point to weaknesses in the modeling</sup> methodology and provide information<sup>on the relativeimportanceof different effects upon</sup> welfare, allowing future modeling<sup>to take theseintoaccount.More generally,it is</sup> important that the effects of structural<sup>reform are monitoredduring implementationand</sup> adjustments made if it becomes<sup>clear that the predictedandactual effectsarefar apart.</sup> In conclusion, the choice of technique<sup>for modeling the link betweenstructural reforms</sup> and poverty should be determined<sup>by the precise natureof the question,the data available</sup> and the characteristics of economic<sup>activityand povertyin the country in question -there</sup> is no single "best" model. This<sup>paperhas presenteda basic methodology whichcan be</sup> applied using only household<sup>survey data and limited timeresources,along with a set of</sup> elaborations upon this methodology<sup>which may beadoptedas circumstancesand</sup> resources dictate.

# **References**

- Ahmad, E. and N.H. Stem (1991),<sup>The Theory andPractice of Tax Reformin Developing</sup> Countries, Cambridge: Cambridge<sup>University Press.</sup>

- Ajwad, M.I., A. Aksoy and B.<sup>Sivri(2002), Tradeand Poverty in Malawi, Mimeo, World</sup> Bank.

- Ajwad, M.I., B. Duygan and B.<sup>Sivri(2002), Tradeand Poverty in Armenia,Mimeo,</sup> World Bank.

- Alwang, J., B. Mills, et al. "Changes<sup>in Well-beingin Zimbabwe,1990-1996:Evidence</sup> Using Semi Non-parametric<sup>Density Estimates."</sup>

- Arulpragasam, J. and P. Conway<sup>(2002). "PartialEquilibriumand multi-market</sup> approaches" in Draft PSIA Toolkit,<sup>World Bank.</sup>

- Baulch, B. and J. Hoddinott<sup>(2000). "EconomicMobility and Poverty Dynamicsin</sup> Developing Countries." Journal<sup>of Development Studies36(6):1-24.</sup>

- Bourguignon, F., P. Fournier, et<sup>al.(2000)."Distribution,Developmentand Educationin</sup> Taiwan, 1979-1994." World<sup>Bank. mimeo.</sup>

- Braverman, A. and J. Hammer<sup>(1986)."Multi-marketAnalysisof Agricultural Pricing</sup> Policies in Senegal", in _Agricultural_<sup>_household models: extensions,applications_</sup> _and policy,_ eds. I. Singh, L. Squire<sup>and J.Strauss,Washington D.C.: The World</sup> Bank.

Carter M. R. and J. May (2001).<sup>"One kind of freedom:poverty dynamicsin post-</sup> apartheid South Africa", World<sup>DevelopmentVol. 29, No.12.</sup> Chen S. and M. Ravallion (2002).<sup>HouseholdWelfare Impactsof TradeReformin China,</sup> Mimeo, World Bank.

31
section and specifically within the list of fully liberalized sectors, we include Water management, Land transportation, and transportation through pipelines, Warehousing and transportation support activities, Telecommunication, and Education when discussing sustainable investment.

We find that growth in FDI was mainly driven by the paper products industry and base metal industry (Table 6). Related to investments that can be linked to SDGs, only transport and logistics infrastructure in the form of warehousing and transportation support activities saw an increase in the post-policy period. There was either no statistically significant coefficient in all other sectors or a decline, most notable in Water management.

The increase in DDI of the fully liberalized sectors (as reported in autoreftab:DDI _lib_ ) Finally, the third column in Table 6 reports growth by sector in the fully liberalized sectors for planned FDI. Growth was mainly in Printing and reproduction media for recording media, Base metal industry, Water management, Food and Beverage Supply, Real Estate, Other professional, scientific and technical activities, Office administration activities, and office support activities and other business support activities. In terms of SDG related investments, there was an increase in water management but a decline in infrastructure.

Overall, the sector that saw growth in realized FDI, DDI, and announcements of FDI was the base metal industry. This is no surprise since Indonesia is a major producer of nickel in the global market and has many mines of other minerals, such as copper, bauxite, iron ore, coal, and gold. Therefore, this is an industry that will likely keep growing. Several sectors (about 50 percent) saw growth in the same direction for FDI and DDI, further signaling a possible crowding-in effect. Finally, while we can expect an increase in FDI in water management going forward due to the increase in planned FDI, and increase in some infrastructure-related sectors, other SDG-related sectors saw mixed results and even a decline (such as education). Further analysis would be needed sector by sector to observe further both the quality and quantity of FDI following the reform.

18
preferences. We do not test the median voter theory per se but are interested in knowing how preferences over government spending on health care, education, pensions and transfers to poor households, which together account for the bulk of provision in advanced economies, change across age groups while controlling for other individual characteristics. Indeed, race, demographics, socio‐economic characteristics and gender are important determinants of attitudes towards redistribution and government spending on social welfare (Keely and Tan, 2008).

One of our main findings is that there is indeed a strong correlation between a person’s age and his or her preferences over policy, which is in turn reflected in how governments allocate scarce budgetary resources to competing programs. In particular, and in line with previous literature, we find that older individuals have stronger preference for allocating additional government spending to health care and pensions, whereas younger people tend to support additional spending on education. These findings are also fairly homogeneous among the countries covered in LITS II.

Second, the effect of ageing on preferences over policy is shaped by participation in the political process. As before, we find that older people are less likely than their younger counterparts to support an increase in government spending on education. In addition, older people who do not vote are even less supportive of raising spending on education than older people who do vote. The same is true for pensions: the positive relationship between age and support for increasing spending on pensions is less pronounced among people who vote than among non‐voters. In other words, preferences over policy tend to be more nuanced among voters than among non‐voters of different age groups.

Third, these findings are fairly homogeneous across countries and do not seem to be solely driven by cohort effects, a hypothesis that we tested using annual waves of Eurobarometer data between 2004 and 2011. In other words, the fact that older people are more (less) likely to support increases in spending on pensions (education) is not driven by a generational change, but by individuals changing their preferences as they grow older. In contrast, the fact that preferences over spending on health care or housing vary by age seems to be highly driven by cohort effects rather than along the life cycle.

The paper is organized as follows. The next section reviews the main findings in the literature on the associations between age and preferences over government spending. Section 3 describes the data set, the estimation strategy, the main empirical findings and robustness checks. Section 4 concludes.

4
ponent of loans.<sup>66</sup> The Locational Banking Statistics by Residence (LBSR) historically only break the counterparty sector for Bank lending into banks and non-banks, though recent data includes additional sector splits. We employ the BIS Consolidated Banking Statistics (CBS) and the Locational Banking Statistics by Nationality (LBSN), both of which have further counterparty breakdowns, in order to construct estimates for Bank lending flows for all B. 4 sectors for the entire period, as described in Appendix After augmenting the Loans (or Currency and Deposits) with the BIS data, we sum them with instruments within other investment debt. This sum be- any remaining non-missing comes our estimate for other investment debt from BIS data.<sup>67</sup> Our corresponding stock measures are similarly constructed. We rely first on IIP data, with an internal fill. We next fill any missing data with QEDS measures. And finally any remaining missing observations are filled with our BIS stock estimates derived above.<sup>68</sup> Table A5 shows the percentage of observations for inflows that are filled by each step of our filling exercise for each sector-instrument category for each country group. For outflows (asset flows), there are few external datasets to do comparable filling. Thus, we rely primarily on our internal filling strategy and end up with a much smaller sample of countries. In one case, we can and do fill using external data. The BIS banking data has data for cross border lending of banks in countries that report to the BIS, separated into loans and bonds.

> 66IMF Credit is a subcomponent of the Loans instrument in other investment debt for general government and central banks. This figure is known by the IMF even if the actual loans by sector are not reported by the country. For central banks, since we fill the currency and deposits instrument with BIS loans, we add IMF Credit to the central bank back in only if the Loans instrument is missing.

> 67In general, when other investment debt is missing, most data on the underlying instruments are also missing. A few countries are exceptions to this, and only for a very few periods: Eritrea and Equatorial Guinea in the annual data, and Eritrea and Kosovo in the quarterly data. None of these countries are included in our analysis with this data.

> 68Even though the sector data may be missing in the BOP, the total for portfolio debt or other investment debt inflows often is not. We do not constrain our filled series by sector to match the total of the flow type as reported in the BOP. However, the two series correlate highly (.86 for total debt inflows) and exhibit similar patterns.

75