rafmacalaba/datause-encoder-data huggingface dataset:
advanced economies and EMDEs at modestly negative values. Importantly, the distribution does not display bi or multi-modal features. Therefore, the negative impact does not appear to be a categorical effect, i.e. countries that have a specific feature will see employment fall but rather the are a continuous distribution across economies.

A forecast error variance decomposition (FEVD) is one method of assessing the importance of technology shocks in driving the variation of employment relative to other shocks in the typical economy. For both advanced economies and EMDEs, technology shocks drive around 30-40% of the forecast error variance of employment at the 10-year horizon (Table 1).

**Table 1:** Forecast error variance contribution of technology shocks to employment

||**Year 1**|**Year **|**5**<br>**Year 10**|
|---|---|---|---|
|**Advanced economy**|13%|24%|31%|
|**EMDE**|19%|31%|42%|

Note: Average of individual EMDE and advanced economy country-by-country estimated forecast error variance decompositions

# **3.3 What country-specific factors are associated with falling employment following a technology shock**

The degree of labor market disruption in each economy can depend on multiple factors, including the types of technologies introduced over the sample period, and the degree to which they complement skilled or unskilled labor; the policies enacted by governments in each economy to engage workers in new roles following technology-driven labor market disruption; and measurement or data issues which may influence the results. To examine the underlying driver and correlates of employment disruption following technology shocks, the median scaled employment IRF for each country is treated as the dependent variable in cross-section regressions containing potential structural factors associated with the employment effect.

Proxies for the degree to which economies experiencing large scale technological change are rare, particularly on a consistent basis across countries. Sectoral employment shares, both in levels and how much they have changed in recent decades are one proxy for economies going under significant technological transformation. In particular, the literature has widely found that in advanced economies, manufacturing jobs have particularly been at risk of replacement, making industrial employment, which includes manufacturing and is widely available across economies, a useful covariate to test. Data on sectoral employment shares are taken from the World Bank’s World Development Indicators, as are data on foreign direct investment net inflows, which may also be a proxy for the implementation of new technologies. Educational attainment is measured 13
# **The Role of Skills and Gender Norms in Sector Switches:**

# **Experimental Evidence from a Job Training Program in Nigeria**

Kevin Croke, Markus Goldstein, and Alaka Holla<sup>*</sup>

# **_Updated January 2021_**

# **JEL classification:** J24, D91

**Keywords:** Formal training programs, occupational mobility, active labor market programs, randomized experiment

> * Croke: Harvard T. H. Chan School of Public Health,1818 H St. NW, Washington DC 20433 (e-mail: kcroke@hsph.harvard.edu). Goldstein: World Bank and BREAD, 1818 H St. NW, Washington DC 20433 (e-mail: mgoldstein@worldbank.org). Holla: World Bank, 1818 H St. NW, Washington DC 20433  (e-mail: aholla@worldbank.org).

> We thank Maria Elena Garcia Mora and Harsha Thirumurthy for work on the initial stages of the evaluation.   We also thank our operational collaborators, in particular Toks Fayomi, Marito Garcia, Peter Materu and Anubha Verma. Tigist Ketema and Daniel Kirkwood provided valuable research assistance.  Project Implicit provided critical support in implementing the implicit association tests.   This product is an output of the Africa Gender Innovation Lab of the World Bank. Support from the Umbrella Facility for Gender Equality is gratefully acknowledged. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **Figure 3:  Deviation from the base (of 100) of the YWI and the standardized score from the behavior category (used to create the YWI)**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>State<br>Figure 4: YWI, C-YWI, and GWI by state: 2002<br>110<br>105<br>100<br>95<br>90<br>85<br>Full YWI YWI CYWI<br> M P P B<br>Standardized Score Rondônia A cre azonas A m Roraima Pará pá a A m Tocantins a ranhão iauí Ceará araíba A lagoas Sergipe ahia São Paulo Paraná G oiás<br> Rio G rande do N orte Pernambuco M inas G erais Espírito Santo Rio de Janeiro Santa Catarina Rio G rande do Sul M ato G rosso do Sul M ato G rosso D istrito Federal<br>Index (national average=100<br> R ondônia Acre Amazonas R oraima Pará Amapá Tocantins M aranhão Piauí C eará R io Grande do Norte Paraíba Pernambuco Alagoas Sergipe B ahia M inas Gerais Espírito Santo R io de Janeiro São Paulo Paraná Santa C atarina R io Grande do Sul M ato Grosso do Sul M ato Grosso Goiás Distrito Federal<br><!-- End of picture text -->

**Figure 4:  YWI, C-YWI, and GWI by state:  2002**

23
tests can be established. The calibration of the dispersion of the tests relies on holding the score variance constant within a group of countries with stable education systems (defined in terms of secondary school attendance rates) over time. For this, Hanushek and Wößmann (in process) use the thirteen OECD countries who had half or more students completing upper secondary education around the beginning of international testing in the 1970s as the “stable” country group, and standardize variances to their group performance on the 2000 PISA tests. The details of the transformation are found in Hanushek and Wößmann (in process).

# **Table A4.1: The International Student Achievement Tests**

|**Abbr.**|**Study**|**Year**|**Subject**|**Age**<sup>a,b</sup>|**Countries**<sup>c</sup>|
|---|---|---|---|---|---|
|1 FIMS|First International Mathematics Study|1964|Math|13,FS|11|
|2 FISS|First International Science Study|1970-71|Science|10,14,FS|14,16,16|
|3 FIRS|First International Reading Study|1970-72|Reading|13|12|
|4 SIMS|Second International Mathematics Study|1980-82|Math|13,FS|17,12|
|5 SISS|Second International Science Study|1983-84|Science|10,13,FS|15,17,13|
|6 SIRS|Second International Reading Study|1990-91|Reading|9,13|26,30|
|7 TIMSS|Third International Mathematics and Science<br>Study|1994-95|Math/Science|9(3+4),<br>13(7+8),FS|25,39,21|
|8 TIMSS-Repeat|TIMSS-Repeat|1999|Math/Science|13(8)|38|
|9 PISA 2000/02|Programme for International Student<br>Assessment|2000+02|Reading/<br>Math/Science|15|31+10|
|10 PIRLS|Progress in International Reading Literacy<br>Study|2001|Reading|9(4)|34|
|11 TIMSS 2003|Trends in International Mathematics and<br>Science Study|2003|Math/Science|9(4),13(8)|24,45|
|12 PISA 2003|Programme for International Student<br>Assessment|2003|Reading/<br>Math/Science|15|40|

Notes:<sup>a</sup> Grade in parentheses where grade level was target population.<sup>b</sup> FS = final year of secondary education.<sup>c</sup> Number of participating countries that yielded internationally comparable performance data.

To create our measure of quality of education employed in this study, we use a simple average of the transformed mathematics and science scores over all the available international tests in which a country participated, combining data from up to nine international testing occasions and thirty 49
42 Table A1.7: Distribution of offsetting coefficients by income and region for 1999–2008 (percentage of countries in each category except for the median)

|**Category**|**< -20%**|**-20–0%**|**0–10%**|**10–20%**|**>20%**|**Median**<sup>**a**</sup>|
|---|---|---|---|---|---|---|
|**Income**|||||||
|Low|16|23|10|29|23|10|
|Lower middle|18|24|20|24|13|5|
|Upper middle|8|18|16|22|35|11|
|High|7|11|20|44|18|14|
|**Region (excluding high-incom**|**e countries))**||||||
|East Asia & Pacific|19|31|6|25|19|1|
|Europe & Central Asia|10|0|10|15|65|26|
|Latin America & Caribbean|11|26|26|26|11|8|
|Middle East & North Africa|8|17|25|25|25|12|
|South Asia|14|0|0|71|14|17|
|Sub-Saharan Africa|16|30|16|21|16|5|

_Sources:_ Table A1.4 and author’s calculations. a. The median is in percentage of the price effect for consumption.

Table A1.8: Decomposition analysis for 1999–2009 (percentage of GDP)

|**Country**|**V**<br>**1999**|**V**<br>**2009**|∆**V**|∆**P**|∆**C**|**Data**<br>**source**|
|---|---|---|---|---|---|---|
|Albania|2.7|1.1|-1.6|-0.9|-0.7|IEA|
|Algeria|-17.1|-23.2|-6.1|-8.3|2.2|IEA|
|Angola|-76.5|-52.0|24.6|25.6|-1.0|IEA|
|Antigua and Barbuda|3.4|8.7|5.2|0.0|5.2|EIA|
|Argentina|-0.8|-1.3|-0.5|-3.7|3.2|EIA|
|Armenia|1.4|2.1|0.7|0.0|0.7|IEA|
|Australia|0.4|1.0|0.5|-0.3|0.8|EIA|
|Austria|0.7|1.5|0.7|-0.1|0.8|EIA|
|Azerbaijan|-24.3|-48.3|-24.0|-12.2|-11.7|IEA|
|Bahamas, The|2.4|10.1|7.7|0.0|7.7|EIA|
|Bangladesh|0.9|2.3|1.3|-0.1|1.4|EIA|
|Barbados|2.1|4.8|2.8|0.0|2.7|EIA|
|Belgium|1.5|3.0|1.5|0.0|1.5|EIA|
|Belize|2.9|5.4|2.5|-6.7|9.2|EIA|
|Benin|2.8|10.5|7.8|0.0|7.8|IEA|
|Bhutan|1.6|3.0|1.4|0.0|1.4|EIA|
|Bolivia|-0.4|1.0|1.4|-3.1|4.5|IEA|
|Bosnia and Herzegovina|3.0|3.7|0.7|0.0|0.7|EIA|
|Botswana|1.2|3.5|2.3|0.0|2.3|IEA|
|Brazil|0.8|-0.1|-1.0|-2.0|1.0|EIA|
|Brunei Darussalam|-26.1|-33.2|-7.1|-8.6|1.5|IEA|
|Bulgaria|4.8|5.1|0.2|0.0|0.3|EIA|
|Burkina Faso|1.6|2.4|0.9|0.0|0.9|EIA|
|Burundi|2.2|3.6|1.4|0.0|1.4|EIA|
|Cambodia|3.0|6.9|3.9|0.0|3.9|IEA|
|Cameroon|-6.0|-4.2|1.9|-0.3|2.2|IEA|
21 Table 6

|||**Net Effe**|**cts of Shocks**<br>**1998**||
|---|---|---|---|---|
||**Terms of Trade**|**Spread**|<br>**Rel Interest**|**RFFR**|
|**Argentina**|<br>-3.7|-5.0|-2.0|-4.5|
|**Brazil**|-0.4|-3.0|-2.3|-1.1|
|Chile|-1.0|-0.6|-3.1|-1.0|
|**Colombia**|<br>**-0.5**|-0.8|-5.2|-2.1|
|**Mexico**|-4-2|-2-9|-0-3|25|

Table 7

|||**Net Effe**|**cts of Shocks**<br>**1999**||
|---|---|---|---|---|
||**Terms of Trade**|**Spread**|<br>**Real Interest**|**REER**|
|**Argentina**|<br>**-7.0**|-2.4|-2.2|-1.5|
|**Brazil**|**-1.3**|-4.7|-1.5|-0.8|
|**Chile**|**-1.7**|**-3.0**|**1.3**|-2.4|
|**Colombia**|<br>-0.8|-2.7|0.1|-4.9|
|**Mexico**|**-3.1**|-2.1|-2.8|3.6|

For the whole period 1998-1999 (Table 8), this methodology predicted big falls in output growth rates in Argentina, Brazil and Colombia. This actually happened in Argentina and Colombia. Similarly, in those countries where the method predicted the smallest falls, these took place. In general the forecast error is not very big as a percentage of the forecasted value, except in Brazil and Mexico. In both cases the observed slowdown was
(1981), hereafter the HT estimator, and the Amemyia and MaCurdy (1989), hereafter the AM estimator. These are discussed below.

_The Within Estimator:_ To simplify notation, rewrite equation (6) as: where **xi,** is a _K_ x I vector collecting all the time-variant explanatory variables in equation (7),z, is a G x 1 vector collecting all the time-invariant explanatory variables, and **it1** and **it 2** are conformably dimensioned parameter vectors. We assume that the disturbances **si,** are _iid N(0,a6_<sup>_2_</sup> _)_ and the individual effects **m1** are _iid_ N(O,u,,<sup>2</sup> ). The time and household-variant component **6,,** are assumed to be orthogonal to both the explanatory variables and the individual effects, whilerli may be correlated with parts of x and z.

Combining all _NT_ observations we can write (8) as: where y and _£_ are _NT x_ 1, x _is NT x K,_ z is _NT x_ G, and V is an _NT x N_ matrix of individualspecific dummy variables. Now define the matrix Pv _= V(V'V)-'V_ as the projection onto the column space of V. Thus, _Pv_ is a matrix that transforms a vector of observation into a vector of individual means across time: i.e., _Pvy,,_ = _(=I_)zyi,_ yi. Then, Qv _=_ **_I_** - _Pv_ is defined as the projection onto the null space of _V,_ i.e., Q, produces a vector of deviations from individual means: i.e., _Pvy,= yj, -yp._ The within estimator is computed by projecting (9) onto the null space of V and performing leastsaquares. Because Q^z = **0,** and **Q,n** = **0,** only **7c1**<sup>can be estimated. Therefore, the within estimator</sup> of **_Rit_** is: and is consistent whether or not the explanatory variables are correlated with the individual effects **77i.** The problem with the within estimator is that it does not allow for the estimation of the vector **_7it2_**<sup>**_._**In what follows we explore two estimators that will be consistent under some mild</sup> orthogonality assumptions.

_2SLS Estimators:_ As explained in Hausmann and Taylor (1981), a more efficient IV estimator can be computed if we are willing to assume that some explanatory variables in x and z are orthogonal to the individual effects 17k. Consider the partition ofx and z given by x = (xi, x2) and z
# **Table A- 6: Mass Merchants: Private-Label Sourcing Strategies**

[Sales Revenues in $US Billions for 2008]

|**Retailer**|**Sales**|**Sourcing**|**Description & Known Countries**<br>|
|---|---|---|---|
|**Walmart**|302.6|Direct Sourcing;<br>Intermediary:<br>Li<br>&<br>Fung|80% from 3<sup>rd</sup>parties; <20% sourced directly from<br>manufacturers (2009); Countries: China: ~90%;<br>othersMexico,Bangladesh, & Jordan|
|**Target**|64.9|Own Intermediary|Target owns (subsidiary) a domestic agent,AMC|
|**Sears**|25.3|Direct Sourcing|60-70% direct sourcing via 8 sourcing and 4<br>quality assurance offices worldwide (2005)|
|**Macy's**|24.9|Own Intermediary;<br>Intermediary:AMC|Macy’s owns (subsidiary) a domestic agent,<br>MDSI, thathas officesin 10 countries|
|**J.C.**<br>**Penney**|18.5|Direct Sourcing|16 overseas buying offices: concentrate on 15<br>countries including Bangladesh, Hong Kong,<br>Pakistan|
|**Kohl's**|16.4|Intermediary:<br>Li<br>&<br>Fung|Kohl’s is currently Li & Fung’s largest supplier|
|**Marks**<br>**&**<br>**Spencer**<br>(UK)|15.3|Direct Sourcing|Domestic Importers: 70% provided from <15 UK-<br>based full service importers/vendors. 30% direct<br>sourcing with 120 suppliers via 7 owned sourcing<br>offices; Turkey/Morocco office responsible for<br>12% (2006). Others:Bangladesh**, **Sri Lanka.|

Sales from: (Asaeda, 2009); sales represent all divisions-not just apparel

# **Table A- 7: Specialty Retailers: Sourcing Strategies**

[Sales Revenue for 2008 in $U.S. Billions]

|**Retailer**|**Sales**|**Private-Label**<br>**Sourcing**|**Description & Known Countries**|
|---|---|---|---|
|**Gap**|14.5|Direct Sourcing|900 vendors in 60 countries. China 27%;<br>U.S.: 3%. Others: Bangladesh, Sri Lanka<br>Pakistan,<br>Philippines,<br>Jordan,<br>Vietnam,<br>Cambodia (Gap largest buyer), Morocco,<br>Turkey, and India.|
|**H&M**<br>(Sweden)|13.1|Direct Sourcing|20 offices (10 each in Europe & Asia);<br>relationships with 750 factories: 60% Asia<br>(incl. Bangladesh, Pakistan, Cambodia) and<br>40%Europe (2007).|
|**Limited**<br>**Brands Inc**|9.0|Own Intermediary|Own MAST Industries (agent, contract mfg.,<br>design): mfg. facilities in 35 countries in<br>Asia (Sri Lanka),Europe, S.America,Africa|
|**Abercrombie**<br>**& Fitch**|3.5|Direct Sourcing|Domestic Importer: use MAST Industries;<br>relationships with 38 countries: primarily<br>Asia and Centraland South America|
|**Talbots**|2.4|Intermediary:Li&F|ung|
|**Aeropostale**|1.9|Direct Sourcing|>67% ofbusiness with five vendors|
|**Gymboree**|1.0|Intermediary:Li&F|ung|

Sales are for 2008 from (Apparel's top 50, 2009); Talbot’s (Euromonitor, 2009).

36
Table A1.1: Missing districts in the Sri Lanka Household Income and Expenditure Survey

|Province|District|2002|2006/07|2009/10|
|---|---|---|---|---|
|Western|Colombo<br>Gampaha||||
||Kalutara||||
|Central|Kandy<br>Matale||||
||Nuwara Eliya||||
|Southern|Galle||||
||Matara||||
||Hambantota||||
|Northern|Jaffna|missing|missing||
||Mannar|missing|missing|missing|
||Vavuniya<br>Mullaitivu|missing<br>missing|missing<br>missing|missing|
||Kilinochchi|missing|missing|missing|
|Eastern|Batiacaloa<br>Ampara|missing<br>missing|||
||Trincomalee|missing|missing||
|North-Western|Kurunegala<br>Puttalam||||
|North-Central|Anuradhapura<br>Polonnaruwa||||
|Uva|Badulla||||
||Monaragala||||
|Sabaragamuwa|Ratnapura<br>Kegalle||||

For the sake of comparison, only the seven provinces consisting of 17 districts covered in HIES 2002 are considered in the analysis.

# **Questionnaire**

The survey schedule was designed to collect data at household level and separate schedules are used for each household identified according to the definition of the Household within the housing units selected for the survey. In HIES 2002, the survey schedule consists of three main sections, namely, _Demographic_ , _Expenditure_ and _Income_ section. The demographic characteristics and usual activities of the inmates belonging to the household were reported in the demographic section of the schedule. The expenditure section has two sub sections to report food and non-food consumption data separately. Expenditure incurred by boarders and servants on their own decisions were recorded in a sub section under the expenditure section. The income section has seven sub sections categorized according to the main sources of income. HIES 2009/10 questionnaire further improved by introducing a _School Education_ section for persons aged 5-19, a _Health_ section, an _Inventory of durable goods_ and household _Debts_ , _Access to facilities_ , _Housing Information_ , _Agriculture holdings and Livestock_ . Compared to HIES 2002 there are some changes in HIES 2009/10 and HIES 2012/13 questionnaire with regards sections focused in this paper such as demographic information, consumption and income and only comparable variables are used in this analysis, as explained in the following sections.

29
11 Ghana, Malawi and Uganda are under this threshold while South Africa, Algeria and Tunisia are well above it.<sup>4</sup> Column 3 of Tables 1 and 2 shows the results of the estimations testing the role of human capital in the trade-poverty relationship. The beneficial impact of an increase in trade openness on poverty reduction is larger when the investment in human capital is stronger. We find that when the share of the population over age 15 with completed primary education exceeds 46.7 percent (the average in the sample is 55 percent), trade openness starts being favorable to the poor. In other words, with appropriate learning skills, people are better able to take advantage of the new opportunities offered by trade.

Finally, we examine whether the relationship between the openness to trade and poverty may hinge on a country’s institutional environment (columns 4). Results suggest that the negative association between trade openness and poverty could diminish with improvements with respect to law and order, and even reverse. We find that trade openness could be favorable to the poor when institution quality (measured by the _Law and order_ variable) reaches 3.3 (the average of our sample is 2.9).<sup>5</sup> In other words, an environment where laws and contracts are enforced seems to be more favorable for the emergence of new enterprises and the closing of older ones, allowing the economy the needed flexibility to adjust.

# _Robustness tests_

We conducted several robustness tests:

- First, we used an alternative measure of absolute poverty, the poverty gap. The results using the poverty gap confirmed the observations for the poverty headcount ratio (see Table 2);

- We also removed outliers. The results reported in Table A.1 and Table A.2 confirm and reinforce our previous observations.

> 4 See Appendix 6.

> 5 We also used another ICRG indicator measuring Institutional strength and quality of the bureaucracy (ranging from 0 to 4, with a higher figure indicating a bureaucracy which has the strength and expertise to govern without drastic changes in policy or interruptions in government services), as a proxy of the institutional environment. We found similar results.
**<u>Table 2.5. Irrigation ratios of the areas transferred by DSI, 1999-2006</u>**

||Transferred Area<br>(1,000ha)|Irrigation Ratio<br>(%)|Total Irrigation Ratio<sup>a</sup><br>(%)|
|---|---|---|---|
|1999|1,304|76|84|
|2000|1,609|66|76|
|2001|1,664|64|73|
|2002|1,687|68|79|
|2003|1,826|67|76|
|2004|1,861|69|79|
|2005|1,922|66|76|
|2006|1,976|65|77|

Note:<sup>a</sup> Includes the area irrigated by farmers and the area outside the Scheme _Source: DSI, 2008b._ The significant role of irrigation for improving the performance of the agricultural sector is recognized in the Ninth National Development Plan for 2007-2013 (SPO, 2007). The Plan establishes the priority for the efficient use of water resources. The development of irrigation infrastructure takes precedence according to the targeted development toward 2023.

The transfer of operation and maintenance of the schemes developed by DSI is complete (Table 2.6). However, the sustainability of transferred schemes to the beneficiary is questioned in the recent development plan. The Plan recommends to develop participatory mechanisms together with the necessary legal provisions for efficient and sustainable use of soil and water resources.

**<u>Table 2.6. Management of Irrigation Schemes, 2007</u>**

||Number|Area (1,000 ha)|
|---|---|---|
|Developed by DSI|2048|2,573|
|Managed by DSI|86|82|
|Transferred to users’ organizations|697|2,053|
|Transferred to ICs|1,290|438|
|Developed by GDRS and managed by the|n.a|1,394|
|farmers|||
|Developed and managed by the farmers|n.a|1,034|
|Total|n.a|5,001|

_Source: DSI (2008a)_ Specific legislation regarding the water sector with comprehensive water management and defining water rights does not exist. Efforts in the past couple of years to enact a “water law” have yet to be successful. Sectoral priorities are set in the DSI law, but clear definitions of water 14