rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 : Well-Being and Own Labor Force Status Ordered Probit Regressions, BHPS Waves 1-16 Pooled

||Simple|With Some|Broad|
|---|---|---|---|
||Specification|Controls|Specification|
|Self-Employed|0.04|-0.046|-0.04|
||(3.70)**|(4.09)**|(3.57)**|
|Unemployed|-0.433|-0.412|-0.409|
||(31.29)**|(28.29)**|(28.00)**|
|Annual Income in ‘k||0|0|
|||-1.37|(2.46)*|
|Male||0.209|0.215|
|||(28,97)"*|(29,23)**|
|Age||-0.021|-0.021|
|||(8.70)**|(8.19)**|
|Age2/100||0.319|0.308|
|||(11.13)**|(10.09)**|
|Married||0.019|0.03|
|||(1.99)*|(2.78)**|
|Separated||-0.32|-0.329|
|||(14.45)**|(14.67)**|
|Divorced||-0.08|-0.07|
|||(5.51)**|(4.69)**|
|Widowed||-0,194|-0.18|
|||(6.13)**|(5.64)**|
|Health<br>: Good||-0.244|-0.245|
|||(28.98)**|(28.86)**|
|Health<br>: Poor||-0.775|-0.779|
|||(77.65)**|(77.40)**|
|Education<br>: High||-0.174|-0.186|
|||(18,22)**|(19,17)**|
|Education<br>: Medium||-0.084|-0.093|
|||(8.57)**|(9.40)**|
|Children Dummies|No|No|Yes|
|Wave Dummies|No|No|Yes|
|Regional Dummies|No|No|Yes|
|Observations|111761|110140|109390|
|Absolute value of z statistics i|n parentheses|* significant at 5%; *|* significant at 1%|
|Reference Dummy for Labor M|arket Status is<br>Paid Emp|loyment||
|Reference Dummy for Civic Sta|tus is Never Married|||
|ReferenceDummyforHealthi|sPoorandLowforEduca|tion||
4 normal. This distribution imposed that the majority of the firms are almost quasi efficient. There is however no theoretical reason that impedes that inefficiency be distributed symmetrically as Vj.4 The estimation procedure is somewhat cumbersome and requires some additional theoretical assumptions. It requires first running the Standard Least Squares (SLS) to obtain consistent estimates of the slope parameters. The constant term is biased and has to be modified by substracting the average u. In the case of the half normal distribution:

# E(u) = cu(2/7c)"2.

While the inefficiency component cannot be observed directly, it can be inferred from the error term si. Jondrow, Lovell, Materov and Schmidt (1982) present an explicit form to decompose this error term when ui is distributed as a an average-normal. Both the expected value (E) and the mode (M) of the distribution of the inefficiency term constrained by the composite error term can be used in the estimation of **ui.** E(ui/Ei) = OX/(+X<sup>2</sup> ){p( _siX/c)/AD(-EiX/a)_ - Ei/c}, M(Ui/Ei) =E (&2U/Ca2), if si > 0, M(ui/si) = 0, if Ei < 0, where =(&v±cu,)12, =u/c, (P.) is the function of probability density of the distribution and (D(.) is the function of accumulated density of the standardised normal distribution function. The parameters a, and _cy,_ can be computed from the moments of the SLS. The efficiency then simply comes from:

# Efficiency = exp(-uj)

> 4Green has notice that the results depend on the distribution used. He found that the gamma distribution produces results which differ noticeably from those of three (truncated normal, exponential and half normal) alternative formulations (Greene, 1990). However, this is an empirical problem that can be assessed using the consistency condition approach presented below.
28

**Table** 2: Covereac of Health Insurance in Su-Saharan Africa*

|Country|Year|Population<br>Size<br>(Millions<br>(mid-1986)|<br>Pomulatfon<br>Covere<br>) Social<br>Other Public<br>Security<br>Insurance|d<br>by<br> <br>Private<br>Insurance|Totalt*|Percent<br>of<br>Population<br>Insured<br>(Including<br>Family<br>Mefners)|
|---|---|---|---|---|---|---|
|BurkinaFaso|1981|6.7<br>(1982)|Caisse<br>Nationale de<br>taSecurite<br>Sociale (CNSS)<br>covers<br>60,000<br>workers<br>in<br>private<br>sector**and**<br>government<br>emploo nt,<br>but no number<br>of goverroent<br>employeesis<br>given for the<br>goverrumnt<br>-ployees<br>covered.||||
|Burun.di|1986|4.9<br>(1986)|070,||.070|1.4X|
|Cameroon|1984|<br>9.3<br>(1982)|b||||
|Coted'Ivoire|1985|<br>10.2<br>(1985)|**Data**not**available**<br>**on nuWb**<br>peopleinsured,<br>but a smel<br>health Insurance.|**ers**<br>of<br>l private|||
|Ethiopia|19e|43.5|c<br>d|060|060|.001X|
|_Ghna_|1967|13.2||-|||
|Guinea|1985|6.1|Governrent esployees are e|xempt|||
|||(1985)|frm hospital adission<br>ch|arges.|||
_Note:_ The values show the percentage of each category of workers with each level of education. For example, 9.3 percent of all manufacturing workers in the sample have not completed any level of education.

_Source:_ Round Three of the 1994 Regional Program on Enterprise Development for Ghana.

**_Impact on wages._** The estimated coefficients on the education variables in the

wage equation reveal the impact of human capital obtained from education on wages controlling for other individual and firm characteristics (see appendix table B-1).8 The impact on wages of each of the six categories of completed education (primary, middle, secondary, vocational, technical, and university) is statistically significantly different from zero (no education completed being the category of reference). This result indicates that the more education a worker has completed, the higher wage the worker receives, conditional on a variety of individual and enterprise covariates, including occupation. The estimates show that the size of the wage premium to education increases rapidly with completed level of education.

The wage gap is 51 percent for workers wh1o completed primary education, compared with those who did not complete any level of education. An employee who has completed middle school obtains a wage premiurn of 41 percent. The wage gap is 56 percent for secondary graduates and 186 percent for university graduates. These results are in line with those of Canagarajah and Thomas (1997), who find that workers in Ghana who have completed tertiary education earned 2.7 times more than illiterate workers in 1991.

**8** The results reported in the text are exponential transformed estimates reported in appendix B

14
# **5. Conclusions**

The last few years have seen the attention of policy makers and the general public to be increasingly focused on Ninis. The perception is that Ninis are associated with other social problems in the region, such as shortcomings in the quality and relevance of education, which may lead to early dropout, and high levels of youth unemployment, crime, and violence. A related concern is that being Ninis will prohibit a sizeable part of today’s youth population from accumulating the human capital necessary for their own future success in the labor market, as well as for the economic prosperity of their countries.

This paper contributes to our understanding of the decision process behind being a Nini and the factors with which it is associated, by testing the implications of the theoretical model developed by Berhman et al. (2014). To achieve this, the analysis employs almost a decade of information from the quarterly ENOE labor survey in Mexico, exploiting the longitudinal nature of the data, and verifying the robustness of the main results using data from Argentina and Brazil.

Our initial results show that nearly a fifth of Mexican youth are Ninis, with a majority of this group, 76.3 percent, being composed of women and, in particular, women aged 19 to 24. Descriptive statistics also show large difference by gender, rural/urban status, and household income. Moreover, Nini status is also much more persistent among youth from poor families, especially when they are younger, and among women living in rural areas. Approximately a fifth of male Ninis and a tenth of female Ninis aged 15-24 years old were found to be studying a year earlier, composed mostly of younger individuals who are disproportionally from disadvantaged socioeconomic backgrounds and urban areas.

The results of the multinomial logit models also support several of the predictions of the theoretical model: (i) the history of past decisions matters for future decisions, at least in the periods in which youths are observed; (ii) the relative welfare position of the household, as measured by income per capita quintiles, is associated with decreased probabilities of being Ninis for men (but not for women) ; (iii) for all youth, we find a robust negative association between current education level and being a Nini; (iv) socioeconomic context, as measured by parental education is associated with female youths’ decisions to be Ninis, but not those of young men; (v) other demographic characteristics also show significant associations with the outcomes under study, in particular being married or cohabitating (for young women), or living in a female headed household (for young men); (vi) macroeconomic conditions seem to be
<!-- Start of picture text --> $10.00<br>$8.00<br>56.00 . \<br>\4.00<br>$2.00<br>0.00- 1996 1997 1998 1999 2000 2001 2001<br>Figure 15: International Direct Call Charges to US & Europe<br>(USS/3minutes, End of Year).<br>Note: Tariffs and exchange rates are for end of year.<br>Source: Price data for Malawi is from 1996 amendments to the 1994<br>Telecommunications act and MTL's webpage (www.mtlonhne.com).<br><!-- End of picture text --> In contrast to connection fees and local and national long-distance calls, international tariffs were reduced. Per minute charges for calls to Europe and the Americas were reduced by 60 percent from US$3.00 per minute to US$1.20 (see Figure 15) and calls to COMESA countries were reduced from US$1.00 per minute to US$0.50 per minute (see Table 3). Because intemational tariffs are denominated in dollars rather than Kwacha, they have not declined as significantly in real terms as local calls have. The re-balancing significantly lowered the ratio of international to local tariffs, which fell from 159:1 to 35:1 and the ratio of international to domestic long-distance tariffs from 27:1 to 5:1 at the end of 1999. As discussed previously in section 11.3, this comprehensive tariff re-balancing decision seems to have been partly caused by competitive pressures from Internet telephony, potential future call backs, and potential GMPCS telephony.

> The introduction of competition in cellular telephony did not result in reduced prices for

> calls from cellular phones. Prior to reform, a three-minute call from a TNM cellular phone to a

> fixed line phone cost about US$0.21 (see Table 4). However, when Celtel entered the Malawian market, it set its pre-paid prices considerably higher than the TNM's prices. Further, because Celtel set its prices in US dollars rather than Malawi Kwacha, prices remained high in dollar terms despite the high level of inflation in the late 1990s and early 2000s (see Table 1 for inflation rates in Malawi over this period). A three-minute call from Celtel's network to a fixed

> line phone on MTL's network cost US$1.05 during peak hours (7AM to 6PM), US$0.90 during off-peak hours (6PM to 1OPM), and US$0.60 during discount hours (1OPM to 7AM). Although

> Celtel did not have a monthly subscription fee for its pre-paid service, the monthly subscription

> fee for TNM was relatively modest before reform (about US$6.84 per month in 1998). The

44
that converts high-resolution marine-light intensity to actual boat counts. This algorithm enables us to obtain a monthly gridded panel dataset of fishing intensity (i.e. boat counts) for the entire Vietnam’s maritime Exclusive Economic Zone (EEZ) between 2013 and 2016.

# **3.1.1 Vietnam labor force surveys**

The Labor Force Survey is conducted annually by the General Statistics Office of Vietnam. The surveys in 2015 and 2016 include 689,747 and 814,611 individuals, respectively. LFS provides primary labor-market information including employment status, income and workload of workers at a quarterly basis. Household members are selected from a stratified random sampling method, which ensures representativeness by province and industry. The sample includes all family members of interviewed households. In addition, there is a representative sub-group of households randomly selected to be surveyed twice a year, with the first survey visit taking place in the first or second quarters, and the subsequent revisit taking place in the third or fourth quarters.<sup>7</sup> For the purpose of analysis, we restrict our sample to only working-age individuals between 18 and 70 years old.

We focus on the labor outcomes specifically in saltwater fishery industry, and examine the changes in these outcomes before and after the _Formosa_ incident. Our main sample includes all individuals who worked in the saltwater fishery industry before the disaster in 2016 (i.e. in Q1-2016). Thus, the fishers living in Ha Tinh, Quang Binh, Quang Tri, and – – Hue the four _Formosa_ -affected provinces serve as the treatment group. As we will elaborate further, we exploit both the location-specific and industry-specific characteristics of _Formosa_ to obtain the two separate groups of counterfactuals. First, because _Formosa_ took place strictly in the central provinces, we form a “geographic” control group consisting of non-affected fishers located in other coastal provinces. To avoid the concerns related to contaminated counterfactuals, we restrict to only fishery workers living in the southern provinces distant from Formosa, specifically between Phu Yen to Ca Mau (see Figure 1). Second, the fact that _Formosa_ mainly affected the fishery industry allows us to form an “industry” control group consisting of individuals living in the affected zone, who worked in non-affected industries such as manufacturing, construction, and retail.<sup>8</sup> The first Panel (Panel I) in Table 1 presents descriptive statistics for the primary labormarket and observable characteristics of the saltwater fishery industry, using LFS 2015. The

> 7 This survey design allows us to additionally control for the individual-level unobservables with the inclusion of individual-specific fixed effects in one of our empirical specifications.

> 8 We subsequently check for the validity of both control groups in several empirical exercises.

6
_33_

# **Table VEL** India: External Debt(millions of dollars)

||Debt Outstandi|ng and Disbursed|Disbur|sements|Principal Re|payments|Interest|Payms|
|---|---|---|---|---|---|---|---|---|
||1976-77|1986-87|1976-77|1986-87|1976-77|1986-87|1976-77|1986-87|
|_C_<br>_dme_<br>_aObw_<br>_Of_|12.486|23,219|1,374|1,861|342|756|211|348|
|1DA|3,333|10.529|533|714|9|61|21|90|
|gm|240|173|3|-|33|13|16|11|
|_?Jon-Cionssbalof,*kh_|796|8.694|164|1,781|154|825|52|766|
|gmBRD|216|2,494|73|641|24|139|15|284|
|Bilut"aLONS|288|1.185|33|111|63|138|18|106|
|COMmec_l Louis|293|5.014|58|1.029|68|548|18|375|
|_Plva_<br>_bN.gum'nt_|295|2.598|61|849|62|773|19|244|
|Tca (udkt gF)|13.577|34.511|1,600|4,491|559|2,354|282|1.358|

_Source:_ India: _Recent Developments and MediumTerm Issues,_ Vol 11; World Bank, Washington D.C. April 27, 8; pp. 48-5L
# Research Components of World Bank Education Projects, since 1982.

|_Area_|l<br>Country<br>and Loan/Creditl<br>Number|Approved|r-<br>_<br>--<br>_<br>_<br>ResearchComponents|X<br>-<br>_<br>Current<br>Status<br>of Studies,<br>including<br>CompletedDocuments/Data|
|---|---|---|---|---|
|AFRICA|<br>Benin CR 1246-BEN|1982|Second Education. (i) Study of impactof<br>educational materials (para. 2.17 p.12).<br>(ii) Report on Ministry data collection systems.|(i) stilt outstanding<br>(ii) not initiated<br>(iii) Richard Sack undertaking preparation<br>with UNDP funding.|
||||(iii) Prepare for future education projects<br>(2.21-2, 13).||
|AFRICA|Botswana CR2057-HT|1982|Third Education. Completion of tracer study<br>begun under Second Education Project.|No information availabie|
|AFRICA|<br>Botswana CR2644-BT<br>I<br>|<br>l|1986<br>|Fourth Education. Proposed studies: (i) cost<br>effectiveness of vocational and skill training;<br>(ii)<br>corretation between format and non-format |<br>education<br>and training, and employment;<br>|<br>|<br>(iii)<br>development of investment options in<br>post-secondary education;<br>(iv) construction technology;<br>|<br>l<br>project (total 54 marwnonths)fpara. 3.16 p.18].<br>l<br>lv<br>reine|Completed data for projects (i), (ii) and (iii).<br> <br>Projects (iv) and (v) being implemented.<br> <br>i) V. Gottsleben and G. Rosch (1984).<br>Strengthening theIndustrial<br>Training Trade<br> <br>Testing System of Botswana.<br>(doc. # 110.075)<br>(ii) Ministry of Education, Botswana (1985).<br> <br>"Community Junior Secondary Schools guilding<br>Program" tdoc. # 114.541)<br>(ii) Ministry of cducation, Botswana (1984).<br>'~~~~~~~~~~~~~~~The<br>rote of Technical and Vocatio-al Training in<br>the Economic Development of Botswana."<br>l(doc# 111.083)|
|||||TOR supplied for (iii), (iv) and (v).<br>ldoc.#C00856-7)|
|AFRICAI|Burkina Faso CR1598-UV|1985|Primary Education Development. No research<br>component.|Confirmed|
|AFRICA|<br>AFRICAI|Burundi CR1358-BU<br>Burundi CR1881-BU|1983<br>1988|Third Education.<br>School sapping project<br>**l**<br>l<br>Education Sector Deve<br>nt.|Completed: CEGIR "Cartes Scotaires"<br>(doc. # 106.990)|

25
we calculate downstream exposure to robots as: where _ETRi,t_ is defined as in (1).

We use _ETRl,t_<sup>_d_asanadditionalregressorin(3)tomeasuretheimpactofautomation</sup> on plants via backward linkages. As we are interested in the spillovers on non-adopting plants, we also add an interaction of _ETRl,t_<sup>_d_withtheplantlevelexposuremeasure.</sup> The results in column 2 of Table 7 suggest that automation has a positive, albeit not significant, effect on employment in upstream non automating plants, while the effect on automating plants is precisely zero. The larger employment effect of backward linkages on non-adopters is consistent with the idea that automating plants upstream would absorb the increased demand for inputs at least in part through robots as opposed to workers. As a result of the inclusion of these backward linkage variables, the coefficient of exposure to automation turns positive and significant. This result provides suggestive evidence that positive spillovers from automation on upstream non automating plants may drive the negative impact of automation on employment in 5. This is a plausible channel also considering that the bulk of manufacturing inputs are used by plants in the same 2-digit industries as the input-producing plants. Hence most of these beneficiary upstream plants are part of the relevant control group in (3).

As the downstream linkages are computed at the 5-digit industry-level, 2-digit industryyear fixed effects are not sufficient to address the endogeneity concerns as it was the case in the previous tables. Therefore, in column (3) of Table 7 we present 2SLS estimates instrumenting both _ETRl,t_<sup>_d_anditsinteractiontermthroughavariablebasedonrobots’</sup> imports by OECD countries rather than Indonesia. As the instrument is used extensively in the aggregate analysis, we postpone its description to Section 4.1. Column (3) reports the 2SLS estimates which show a positive and significant effect of automation on employment in upstream plants with a coefficient almost double the size than that in column (2). This result is consistent with the positive impact of ETR on demand of local inputs (column 1). Again, non-automating plants experience a larger employment 22
predictions. They hypothesize that if education quality is a normal good, the existence of private education leads parents past a threshold income to exit the public sector. This is the income at which the cost of private school tuition is just offset by the utility gain from consuming higherquality education. In societies with higher income and more unequal income, more people use private schools, and thus vote for low or no spending on public education. Under majority voting, this means less public education. Stiglitz (1974) showed that preferences are not singled-peaked when private education is available. Several approaches address this and obtain existence of a majority voting equilibrium: imposing a single crossing property in a median voter model (Epple and Romano, 1996; Gutirrez and Tanaka, 2009), identifying the decisive voter (Barzel, 1973; Glomm and Ravikumar, 1998), and using a probabilistic voting model (de la Croix and Doepke 2009). In the next section, I present a model similar to de la Croix and Doepke (2009). However, the reader should be aware that a variety of modeling assumptions deliver similar results.

The second literature describes a “political power” channel. It finds that the rich exercise control over public policy that is roughly proportionate to their share of income (or a function of it), as opposed to their share of votes. Chambers (1983) argues that the inability to influence policy is a defining feature of relative poverty. A number of papers show that inequality reduces the political influence of the poor. It is less clear what is the effect of higher median income. However, the _key_ implication of papers in this literature is that the distribution of income _affects_ policy, by affecting the distribution of political power and influence.

Several prominent empirical studies describe the political power channel. Foster and Rosenzweig (2004) use panel data on villages in rural India to show how the share of the population made up of landless poor affects public investment when the village leader is or is not elected. They find that in democratic villages, an increase in the share of landless increases road investment (which the landless want) and decreases irrigation investment (which they do not want) more than in non-democratic villages.<sup>2</sup> Bardhan and Mookerjee (2006), using a longitudinal

> 2Several studies find similar results. Bates (1973, 1976, 1981) shows that some ethnic groups in postindependence Africa increased their access to public funds by increasing their political salience. Varshney (1995), Jaffrelot (2003), Pande (2003), Chandra (2004), and Banerjee and Somanathan (2007) find similar results for low caste groups in India. And Mansuri and Rao (2004) demonstrate, in a review of World Bank community-driven development projects, that most projects are dominated by local elites and do not target the poor.

5
|TableA8.FDI inflow|s andlocalschoolenroll<br>|ment: by gendera<br>|ndFDIskill intensity<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||Panel A: F<br>|emale<br>|Panel B:<br>|Male<br>|
|Dependent var|∆Highschool|∆College|∆Highschool|∆College|
|∆FDI_skills_H|1.553**|0.862**|0.591|0.754**|
||(0.687)|(0.368)|(0.608)|(0.353)|
|∆FDI_skills_L|1.413***|0.274|1.261***|0.572***|
||(0.450)|(0.209)|(0.432)|(0.198)|
|Observations|609|609|609|609|
|R-squared|0.827|0.681|0.769|0.651|
|Prov-Year FE|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|
|Excludingmigrants|Y|Y|Y|Y|

Note: This table reports the effect of FDI shock on city level school enrollment by gender and FDI skill intensity. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

40