rafmacalaba/datause-encoder-data huggingface dataset:
To examine the impact of directly buying from an FDI textiles firm or the impact from directly selling to an FDI apparel firm on firm-level gender labor market outcomes, we estimate the specification below on the sample of domestic apparel and textiles firms: i F F s i **(2a)** FF ii XX ii ii YYi = ββF F FF_s c ssiis + ββ XX + μμ + εε i (2 ) FF ii XX ii ii YY = ββ FF_ccs ccs + ββ XX + μμ + εε where all variables are defined as above, F s is the dummy variable for domestic ii firms which are selling to FDI apparel firms, and FF_s F ssiis c is the dummy variable for ii domestic firms which are buying from FDI textile firms. FF_ccs ccs

## _3.3 Impact of being FDI siblings or partners_

To examine the impact of sharing a local input supplier with an FDI apparel firm or the impact of sharing a local customer with an FDI textiles firm on firm-level gender labor market outcomes, we estimate the following two specifications, respectively, on the sample of domestic apparel or textiles firms: i F F i<sup>**(3a)**</sup> YYi = ββF FFF FF_ssiis iis ii<sup>+ ββ</sup> XX<sup>XX</sup> ii<sup>+ μμ</sup> ii<sup>+ εε</sup> i **(3b)** FF ii XX ii ii YY = ββ FF_ssppssccs ss + ββ XX + μμ + εε where all variables are defined as above, F<sup>is the dummy variable for domestic</sup> ii apparel firms which share a local input supplier with an FDI firm, and FF_ssiis iis F is the ii dummy variable for domestic textiles firms which share a local customer with an FDI firm. FF_ssppssccs ss

# **4. Results**

12
lost due to the COVID-19 pandemic to each adult in group _G_ with positive earnings and simulate her income based on a Mincer equation. The simulated income for worker _i_ , _xic_<sup>_V_</sup> , is obtained from the following expression: where _e_<sup>_0_</sup> is years of education, and _X_<sup>_0_</sup> are other variables observed in _to_ (and assumed to be the same in _T_ ) _._ <sup>0</sup> and <sup>0</sup> are parameters estimated with _to_ data and the last term is the estimated error that captures unobserved factors. Importantly, (5.1) implicitly assumes that the parameters do not change after a change in the distribution of education.

According to the discussion in previous sections, we carry out two simulations: (1) we subtract months of instructional loss to all adults in _G_ due to school closures, following the methodology explained in section 4, and (2) we subtract years of education to dropouts associated to the pandemic according to the results in section 3.

# **Months of instructional loss**

In this case, _kic_ in (5.1) is the loss of education for individual _i_ in country _c_ estimated following an updated version of the Neidhöfer _et al._ (2021) methodology detailed in the previous section. There is, however, a relevant difference between the analysis in section 4 and the input we need for the microsimulations. In the previous section we compute _k_ for children and youths in 2019 for whom we observe the actual access to internet _A_ and estimate the parental factor of substitution  with information about the actual parental background. The nature of the simulation in this section is different. We are pretending we are in 2045, so for instance _Aic_ should be the access to internet in 2020 of those adults in 2045 who were students during the pandemic.<sup>14</sup>

> 14 Since retrospective questions are not included in most household surveys at our disposal, we do not know the parental background of these adults. Anyway, it is not clear that retrospective questions would be very helpful. For instance, retrospective questions on access to internet in childhood would be senseless for adults in 2019 who were children in the late 1980s, when internet was unknown for most people.

17
<mark>Third,</mark> in contrast with most of the literature we exploit data on the task content of occupations that is specific to the country of study (in our case the Chilean PIAAC survey).<sup>2</sup> This is an important contribution as most of the previous literature exploited information on the task content of occupations for the U.S. assuming this content was similar in other countries.

The remainder of the paper proceeds as follows. Section 2 reviews the literature while Section 3 describes the data and summary statistics. Section 4 discusses the conceptual approach and our testable hypotheses. Section 5 presents the econometric strategy and Section 6 discusses the main results. Section 7 focuses on robustness checks and heterogeneity in the effects while Section 8 discusses additional results and section 9 concludes.

# **2. Literature Review**

Our paper relates to two important literatures. First, we relate to the literature studying the impact of ICT adoption on the composition of employment and the demand for skills, focusing heavily on developed countries. A first group of studies introduces an intermediate nexus between technology and labor market outcomes given by tasks and how skills are allocated to tasks to produce output. Using this framework, they analyze the impacts of ICT adoption on tasks performed by workers and on the demand for skills (Acemoglu, 1999; Autor et al., 2003; Acemoglu and Autor, 2011; Autor and Dorn, 2013 and Autor, 2014 for the U.S.; Goos and Manning, 2007 for the United Kingdom; and Goos et al., 2014 and Michaels et al., 2014 for European countries more broadly). These studies measure technological development with the adoption and use of computers in the workplace. Using industry, occupation or industryoccupation data obtained from census, or household and labor force surveys, the studies attempt to provide causal evidence. Evidence is supportive of the idea that computers substitute middleskilled workers by carrying out activities following explicit rules (i.e., routine tasks), while they complement high-skilled workers as the latter perform activities difficult to automate, such as problem-solving and creative activities (i.e., non-routine cognitive tasks). A pattern of employment polarization emerges as a consequence, where low- and high-skilled occupations gain share in total employment at the expense of middle-skilled occupations as a result of computerization.

> 2 In a related study <mark>Almeida et al. (2017) assess the impacts of internet access on the skill content of occupations exploring a unique concordance between Brazilian firm-level data and measures of the task content of occupations, but in contrast to our study they use task content measures as defined in the U.S. Department of Labor's Occupational Information Network (O*NET).</mark>

5
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- WHO (2006). Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. http://www.who.int/childgrowth/en/.

8
||World|USA|JPN|GER|IND|KOR|MYS|
|---|---|---|---|---|---|---|---|
|World|1|0.941|0.841|0.876|0.965|0.919|0.979|
|USA||1|0.960|0.958|0.929|0.971|0.946|
|JPN|||1|0.964|0.858|0.941|0.874|
|GER||||1|0.887|0.965|0.907|
|IND|||||1|0.899|0.978|
|KOR||||||1|0.927|
|MYS|||||||1|

**Table 1.** Correlation coefficients between domestic and global oil prices

Following the above notation the daily change in the price of crude oil is denoted ρd, where Computing daily changes for all six countries respectively reveals a pattern similar to the daily changes in the _global_ oil price, depicted in Figure 3. The mean daily oil price change is not found to be significantly different from zero. Following Hamilton (1983), the oil price πd can be modeled as a random walk process, where the innovation ud = σεd, with εd~ _iid_ N(0,1) _._ The Ljung-Box test for squared residuals, confirms all six oil price return series to be following an autoregressive conditional heteroskedasticity (ARCH) process. This is graphically confirmed by Figure 3, in which distinct high volatility clusters are evident (e.g. 1986, 1990, 2008).

<!-- Start of picture text --> 80 0.4<br>60 0.3<br>40 0.2<br>20 0.1<br>0 0<br>-20 -0.1<br>-40 -0.2<br>-60 -0.3<br>-80 -0.4<br>1947 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011<br><!-- End of picture text -->

**Figure 2.** Percentage change in the quarterly price of crude oil (Source: Dow Jones & Co., Thomson Reuters)

**Figure 3.** Daily global oil price changes 19832011 – similar patterns for all countries.

# **3.2. Realized Volatility**

From 1947 to 1986, oil prices remained (relatively) stable, whereby shocks were almost exclusively positive and moderate in size. However, since the mid-1980s, oil prices have undergone substantial positive and negative shocks. The classical approach, such as by Mork (1989), which considers oil price innovations in levels, fails to remain statistically significant in subsequent sample periods. Subsequently, various studies (Hamilton, 1996, 2003; Hooker, 1996) found direct measures of volatility to be more powerful in explaining the oil-GDP relationship than oil prices in levels. Based on this, this study employs the Realized Volatility (RV) measure as suggested by Andersen et al. (2003). Drawing on conventional finance literature, a price process πt is expressed as a stochastic differential equation: where μt denotes a predictable drift term with finite variance, σt corresponds to volatility and Wt denotes standard Brownian Motion. The continuously compounded price change rt in the unit time interval is denoted 5
|Calorie supply|Per capita kilocalorie<br>supply from all foods per day|Unbalanced panel from<br>1961 to 2018 (58 years) with<br>a minimum of 143 countries<br>to a maximum of 172|Food and Agriculture<br>Organization of the United<br>Nations (FAO)|
|---|---|---|---|
|Poverty headcount|Percentage of people<br>living below the $1.90 per<br>day poverty line|Unbalanced panel from<br>1981 to 2019 (40 years) with<br>a minimum of 160 countries<br>to a maximum of 165|Poverty and Inequality<br>Platform (PIP)|
|Polity V|Score ranging from +10<br>(strongly democratic) to -10<br>(strongly autocratic)|Unbalanced panel from<br>1800 to 2018 (219 years) with<br>a minimum of 22 countries to<br>a maximum of 168|Polity Project|

42
In order to assess also the short run effects of liberalization, Table 4 reports an estimated error **correction model for saving.19 The** reported estimates are OLS; unreported GMM estimates lead to the same conclusions conceming the effect offli, and r,.

Despite the fact that we have here corrected the omission of other dimensions of financial liberalization, there is -- except for the OLS estimate for Mexico -- no evidence from the countryby-country estimates of a significant distinct positive effect of the real interest rate on savings. In most cases the long run point relationship is negative, and significantly so in the case of Ghana and Indonesia. The evidence based on the time series for individual developing countries confirms, therefore, the general conclusion derived from previous studies using pooled time series-cross country data that it is not possible to pin down a systematic positive effect of increases in the interest rate on savings.

So far as the effect of financial liberalization itself is concerned, the picture is mixed. For Korea and Mexico (and Zimbabwe when rt, and O] are excluded) the coefficient of the index of financial liberalization is negative and significant in the long run (using the Dynamic GLS estimates). For Korea, there is also evidence of a significant negative short run effect. On the other hand, for Turkey and Ghana (only Turkey, when r,, and O, are excluded) there is evidence of a positive and significant long-run effect.<sup>20</sup> The estimated impacts of the index on private savings are sizable; for example, the results of Table 3 Part II imply that liberalization in Korea and Mexico has permanently lowered savings by 12% and 6% of GNDI respectively. On the other hand we estimnate that liberalization has raised the savings rate in Turkey by 13% and in Ghana by 6%.

So far as the other variables are concerned, the _income_ variable is significant in most cases (both in the long and short run). The sign of the coefficient of the _inflation_ rate differs across countries -

> **19 rt** and ort are included in the cointegrating vector.

> 20 Note that these are also the two countries whereflit is uncorrelated with private credit, suggesting that borrowing constraints may not have been much eased.

15
# **<u>Construction of Core Indexes:  Primary outcomes are indicated in bold text.</u>**

1. <u>Core Respondent-level outcomes:  Can be analysis with simple cross-sections comparing</u> UCT, CCT, and control.  No extensive margin issue with any of these variables.

- a. Schooling and Marriage  (replication of QJE results with age-appropriate dependent variables):

- i. **Highest grade completed (S7, Q7)**

- ii. Highest educational qualification achieved (S7, Q9)

- iii. **Achievement, replacing the test scores with the ‘competencies’ (see end of this appendix for further details on construction). We will show the components, as well as the index for the quality index; and show only an index for the quantity.**

- iv. **Ever married (Part II CS, Q2e), ever pregnant (S18, Q1, Q2), number of live births (S18, Q17).**

- **v. Hazard model of age of first marriage (S14, Q1 (and Round 3 data for those already married at Round 3)) and age at first birth (construct using age of respondent and DOB), with ‘uncompleted spells’ for those never married or never first birth.**

- vi. Sexual behavior:  ABC, # partners ever, as in previous papers.  Ever had sex (S12, Q2, Q3, Q4) age at first sex (S12 Q4), total number of partners ever (S12, Q5), sexually active in past 12 months (S12, Q7), condom use last sex with most recent partner (S12, Q23), having a partner with an age difference of more than 5 years (S12Q12)

- b. Health:  Replication of Lancet results (Baird, Garfein, McIntosh and Özler 2012)

- i. **HIV prevalence in R4,** HIV incidence R3-R4.

- ii. **Anemia: construct binary measure based on definition of mild anemia from WHO and used in survey (with different thresholds if pregnant),** (with bednets, breastfeeding, timing of last meal, taking medical for anemia, and menstruating as secondary moderator channel) (VCT, S1).

- iii. Use of reliable birth control:  S12 Q27 anything except withdrawal, periodic abstinence, other

- iv. Desired fertility S16 Q4 or Q10.

- v. Mental health, calculated as in Baird, de Hoop, Ozler (2013) (S9, Q9-20): binary

- vi. Number of meals eaten with meat, eggs, fish in past 7 days (S9, Q 6-8)

- c. Empowerment & aspirations:

- i. Index of self-efficacy:  S11a Q1-10.

- ii. Index of preferences for child education:  S11a Q17-25.

- iii. Index of social participation:  S11a Q13,14,16.

- iv. Aspirations:  Change in ladder from five years ago to five years from now (S9, Q23-Q21)

- **v. Change in ladder from five years ago to today (S9, Q22-Q21)**

- **Super-index of overall empowerment i-iv.**

55
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE 165 percent of sector revenues—are substantially higher than for the other regional economic communities. This is mainly attributable to very low collection rates in ECOWAS, as well as high levels of distribution losses. On a more positive note, the ECOWAS power utilities have the best record of cost recovery of any of the regional economic communities, with power tariffs covering 79 percent of the full capital costs of service provision.

**Table 3.1  Benchmarking power infrastructure and capacity, access and utility performance**

||ECOWAS|CEMAC|COMESA|EAC|SADC|Low-<br>income<br>countries|Middle-<br>income<br>countries|
|---|---|---|---|---|---|---|---|
|Installed generation capacity (MW)|3,912|583|1,085|774|9,855|2,110|36,971|
|Net generation per capita, annual<br>(kWh/capita/year)|171|147|114|82|1214|165|4,479|
|Outages, number, annually (number/year)|165|152|119|132|91|134|71|
|Outages, value lost, annually (% of sales)|7|5|7|8|2|5|2|
|Firms with own generator (% of firms)|54|51|43|56|19|33|18|
|Access (urban, % of population)|50|31|34|23|35|43|50|
|Growth in access of population to electricity,<br>annual (%)|2|1|1|1|1|3|2|
|System losses (% of generation)|29|31|32|23|12||10|
|Cost recovery ratio, historical (%)|79|45|73|69|68|100|87|
|Total hidden costs (% of revenue)|159|107|102|65|4|544|0|
|Collection rate, reported by utility, electricity (% of<br>billing)|71|93|93|94|89||91|
||WAPP|CAP|P<br>E|APP|SAPP|||
|Average historic cost (US$/kWh)|0.21|0.4|9<br>|0.19|0.14|─|─|
|Long-run marginal cost (US$/kWh)|0.18|0.0|9<br>|0.12|0.07|─|─|

Source: Eberhard and others 2009.

Note: CEMAC = Economic and Monetary Community of Central Africa; COMESA = Common Market for Eastern and Southern Africa; EAC = East African Community; SADC = Southern African Development Community.

For the remainder of this section, attention will focus on the countries of the West Africa Power Pool (WAPP). This is because power sector issues in West Africa can be analyzed only in the context of this regional trading arrangement, within which all of the ECOWAS countries except Cape Verde exchange power. Similarly, the benchmarks will be the other regional power pools—the Central Africa Power Pool (CAPP); the East Africa Power Pool, expanded to include important trading partners in the Nile Basin, notably Egypt, Ethiopia, and Sudan (EAPP/NB); and the Southern Africa Power Pool (SAPP).

The baseline total net demand for power in WAPP was 31.3 terra-watt-hours in 2005, making it the third-largest power market in Sub-Saharan Africa behind SAPP and EAPP/NB. Owing to widespread outages, power production meets just 70 percent of existing demands, by far the lowest ratio for any of the regional power pools. Extreme power reliability problems in Nigeria, which is able to meet only 61 percent of its existing demand, pull down the regional average. When Nigeria is excluded, the WAPP countries are meeting 93 percent of existing demand, a fraction much closer to that observed in the other regional power pools.

45
# Table 2: **Effects of Inpres Schools on Intergenerational Educational Mobility: Dependent Variable: Children’s Years of Schooling**

||**Lin**|**ear Model**|**Quadratic**|**Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**<br>**(Relevant CEF)**|**Sons**<br>**(Relevant CEF)**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|1.585***|0.513|1.534***|0.476|
||(0.387)|(0.466)|(0.380)|(0.492)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.129**|0.043|-0.255***|-0.020|
||(0.054)|(0.062)|(0.086)|(0.099)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.015**<br>(0.007)|0.008<br>(0.008)|
|R2|0.322|0.409|0.323|0.409|
|Observations|1199814|848350|1199814|848350|

Notes: The **Relevant CEFs** imply correct functional form, which are based on Table A.2 in the online appendix. Quadratic model is adopted following Becker et al. (2015). Robust standard errors are in parentheses, clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Sample corresponds to children born between 1957 and 1962 (comparison), or 1968 to 1972 (treatment). Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table. All coefficients are reported in Table A.1. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

39
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# **Figure 3. Ghana: Household amenities of remittance-receiving and other households**

## **<u>(a) Concrete house</u>**

<!-- Start of picture text --> Before matching After matching<br>77%<br>77%<br>68%<br>Comparable<br>households not<br>49% 49%<br>44% 45% receiving remittance<br>(%)<br>Remittance-receiving<br>households (%)<br>No Remittances Remittances Remittances from Remittances from<br>remittances from Africa from OECD Africa OECD<br>(b) Mud house<br>Before matching After matching<br>Comparable<br>53% 49% 52% 50% households not<br>receiving remittance<br>30% (%)<br>21% 21%<br>Remittance-receiving<br>households (%)<br>No Remittances Remittances Remittances from Remittances from<br>remittances from Africa from OECD Africa OECD<br><!-- End of picture text -->

## **<u>(c) Concrete roof</u>**

_Before matching After matching_ <!-- Start of picture text --> 98%<br>83%<br>79%<br><!-- End of picture text --> <!-- Start of picture text --> 98%<br>92%<br>81% 83%<br><!-- End of picture text --> 14