rafmacalaba/datause-encoder-data huggingface dataset:
## **5.6 Robustness**

In the Appendix we discuss the robustness of our core results for each age cohort to: (i) using randomization inference to test the null of no treatment effects; (ii) adjusting for multiple hypothesis testing; (iii) only controlling for district fixed effects (that are the randomization strata); (iv) using alternative measures of the intensity of Ebola-related disruption. Our core pattern of results are robust to these checks and permutations.

# **6 Discussion**

Viruses are a major threat to human health: over the last century, more deaths have been caused by viruses than all armed conflict combined [Adda 2016]. Given that the long run incidence of highly infectious diseases will be shaped by urbanization and rising global temperatures (and offset by advancement in medical technologies), they are expected to remain a worldwide threat.<sup>42</sup> In this paper, given the nature of timing between our pre-planned evaluation of ELA, and the coincidental outbreak of Ebola just as our baseline survey was completing, our study presents a unique opportunity to understand the microeconomic mechanisms through which this kind of aggregate shock impacts the economic lives of young women.

We conclude by discussing three open issues for future research: the impacts of the intervention on health outcomes related to the epidemic, the behavior of men during the crisis, and policy implications going forward.

## **6.1 Health**

Community engagement is one important component in successfully controlling disease outbreaks. As such, the crisis might open up new forms of community organization, such as ELA clubs, to help organize and coordinate actions to control the epidemic. Does this have any measurable impact on Ebola related health outcomes? Table 12 examines the possibility, where impacts are identified from the endline cross-section because only then did we ask respondents about Ebola experiences, and so we do not control for baseline outcomes.<sup>43</sup>

> 42Smallpox has caused the deaths of 300mn individuals worldwide, influenza 100mn and HIV 30mn. Alongside viruses, climate driven aggregate shocks are also becoming increasingly commonplace. As Cameron and Shah [2015] note, over the last decade, direct losses from natural disasters in the developing world averaged US$35 billion annually. These losses are increasing and are more than eight times greater than the losses suffered as a result of natural disasters during the 1960s. The issue is becoming increasingly important as climate change scientists have predicted an increase in the frequency of disasters like floods and tropical cyclones [IPCC 2001].

43To improve precision, the sample is the same set of individuals tracked from baseline to endline used throughout, but we also add in respondents from the refresher sample collected at endline, who were resident in the village since before the outbreak. The results are near identical not using this second group.

34
**Table C.8: Balance of exposed and non-exposed line characteristics**

||(1)<br>Effciency|(2)<br>Alteration rate,%|(3)<br>Absenteeism,%|(4)<br>Product complexity|
|---|---|---|---|---|
|exposed|_−_1_._02|0_._02|_−_0_._34 **|_−_0_._14|
||(0_._87)|(0_._51)|(0_._14)|(0_._72)|
|N|18025|24960|21247|22148|
|Not exposed mean|48_._16|8_._52|4_._23|17_._28|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The non-exposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. All regressions include factory and day fixed effects, and are limited to the time before project start in each factory. Standard errors are clustered at the line level.

# **Table C.9: Main outcomes – Wave 1 only**

**Panel A: Effects on women’s promotions, income and decision-making**

||(1)<br>Supervisor<br>(Dummy)|(2)<br>Personal<br>income<br>(Taka)|(3)<br>Household<br>income<br>(Taka)|(4)<br>IV<br>Personal<br>income<br>(Taka)|(5)<br>Decision-making<br>(Index)|
|---|---|---|---|---|---|
|selected|0_._28***|654_._82|107_._50||0_._42*|
||(0_._06)|(574_._56)|(1571_._10)||(0_._24)|
|supervisor||||1078_._52<br>(2021_._36)||
|N|181|181|181|181|181|
|Nominated mean|0_._08|10119_._92|24213_._89|10119_._92|_−_0_._48|
|Nominated SD|0_._28|3063_._81|8502_._81|3063_._81|1_._38|

**Panel B: Effects on assignable goods expenditures and remittances**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcom|es as % in hous|ehold income)||
|selected|1_._92*|0_._08|_−_0_._27|_−_0_._16|3_._08*|
||(1_._02)|(0_._92)|(0_._79)|(0_._82)|(1_._68)|
|N|181|72|181|80|181|
|Nominated mean|5_._19|2_._19|4_._18|2_._54|5_._08|
|Nominated SD|4_._62|3_._02|4_._19|3_._19|8_._22|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso (ivlasso for Panel A, Column 4) from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. In Panel A, Column 4 Supervisor is being instrumented by selected, for which the first stage is shown in Panel A, Column 1. The sample in Panel B, Column 2 (Column 4) is limited to those respondent with a daughter (son), or a female (male) child in the household.

78
addition to greater demographic pressures and an ongoing structural transformation of the economy can strengthen the rationale for policy action.

Given these reasons that might justify government involvement, the key question is then how should the government get involved in order to ensure these policies have higher chances of being successful?

# **What Are the Most Promising Avenues for Government Involvement in Job Training and Job Search?**

Jobseekers may struggle to find jobs for three main reasons. First, there may be a shortage of firms wishing to hire them. This may reflect a lack of overall labor demand in the economy, in which case the appropriate policy actions will be in the area of private sector development policy. In some cases, it may also reflect discrimination, with firms not willing to hire individuals with certain characteristics. Second, jobseekers may struggle because they lack the skills and experience needed for jobs. Job training and internships and apprenticeships can then be used to help overcome this problem. Finally, even if jobs are available and individuals have the right skills, they may struggle to find and match with employers who want their labor, in which case job search assistance can be useful.

It is difficult to find systematic evidence to assess the relative importance of these three reasons, and the answer will almost surely vary with context. Our sense is that in many cases the largest issue is a lack of labor demand for jobs, and especially “good” jobs, and that policies to help firms to grow and demand more labor need to be a primary part of any jobs policy solution (although it is not the topic of this paper). Data from the Mexican National Survey of Occupation and Employment (ENOE) provides one data point, asking unemployed individuals why they are not looking for work: 4.6 times as many individuals say it is because they think there are no jobs available near them than say it is because they lack the skills or experience needed for jobs. Lack of experience and knowledge of where to look for jobs may be bigger barriers for young jobseekers. A survey of high school graduates in one part of Mexico with a strong labor market 9
> intemational economic integration) is good for the poor. Nevertheless, as for financial integration, there is significant controversy as to the exact magnitude (if not direction) of the benefits associated with trade liberalization. In a detailed

> review of some the existing empirical studies, for instance, Rodriguez and Rodrik

> (1999) suggest a cautious assessment of their robustness.

Moreover, it is now increasingly recognized that the process of globalization entails significant risks and potentially large economic and social costly. Openness to global capital markets has brought greater volatility in domestic financial markets, particularly in countries whose financial systems were weak to begin with and economic policies lacked credibility. Large reversals in short-term capital flows (induced by the volatility of world capital markets) have led to severe financial crises and sharp increases in unemployment and poverty in the short run. Similarly, trade liberalization has led in some countries to reduced demand for unskilled labor and lower real wages in the short run; combined with a low degree of inter-sectoral labor mobility, job losses and income declines have often translated into higher poverty rates.<sup>4</sup> As a result, there have been growing concems about the negative effects of globalization, and an increasingly polarized debate on the plight of the world's poorest--namely, whether many of the 1.2 billion people who still live on less than $1 a day are sharing in the benefits of greater integration among economies and instead are disproportionately hit by short-run crises and economic downtums.

The main purpose of this paper is to try to assess, using cross-country econometric techniques, the extent to which globalization may indeed hurt the poor. Cross-country regressions--most notably in the context of empirical growth economics--have been the subject of criticism for their _ad hoc_ specification and the fragility of many of the results that they lead to (see Temple (1999)). They are,

> 3 As mentoned earlier, foreign direct investment provides another, perhaps more direct,

> route through which technology and advanced managerial and producton techniques can flow

> from industrial to developing countries.

6
in partner countries to reduce the costs and risks associated with development of export markets, while at the same time obtaining know-how from suppliers. OPT is frequently restricted, at least in the initial stages, to labor intensive, low value added activities. These can, however, create significant employment.

In the period following the implementation of the agreements with the CEECs, exports after outward processing accounted for about 18 percent of total CEEC exports to the EU in 1993, up from 10 percent in 1989. For Romania, processing activities generated 30 percent of exports to the EU in 1993. In contrast, exports to the EU of processed goods represented only 1.7 percent of total MENA exports in 1993, a share that has remained constant since 1989 (Table 4). Most of the processing occurs in leather/footwear, clothing, electrical machinery, precision instruments and furniture. OPT

# **Intra-Industry Trade with the EU**

<!-- Start of picture text --> 0.60<br>0.55<br>0.50<br>0.45-<br>X 0.40 _ * CEEC * * . . . . . .<br>16. 0.35<br>D 0.30 - . Israel<br>0.2<br>* 0.20; _, _ _ _ _ _<MENA<br>'E 0.15<br>0.10<br>0.05<br>0.00<br>1987 1988 1989 1990 1991 1992 1993<br>Years<br>Figure 4<br><!-- End of picture text --> 20
# **Online Appendix (not for publication)**

Table A1: Heterogeneous effects of savings default on food spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|Assets|Above median:<br>Patience|
|Savings Default|719.535<br>(512.267)|3.352<br>(740.115)|751.418<br>(553.440)|741.532<br>(663.829)|1020.331<br>(806.345)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Cash group<br>R-squared|5412.12<br>0.343|5753.05<br>0.425|5400.02<br>0.346|5968.22<br>0.279|5442.41<br>0.260|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|Assets|Below median:<br>Patience|
|Savings Default|749.872<br>(770.867)|1080.755*<br>(560.881)|680.691<br>(678.413)|525.873<br>(590.914)|669.625<br>(478.437)|
|Observations<br>Mean of dependent variable in Cash group<br>R-squared|162<br>4305.00<br>0.149|320<br>4640.64<br>0.203|239<br>4617.03<br>0.194|246<br>4030.43<br>0.204|282<br>4727.24<br>0.251|
|P-value: equal efect of savings default|0.973|0.223|0.934|0.801|0.696|
|The top panel includes respondents from fe<br>transactions at NBS in the 90 days precedin<br>those with above-median values of a princip<br>at baseline by amount required to prefer a t<br>Savings Default coefcients in Panels A and <br>value of the outcome measured at survey 1. <br>* p_<_0.10, ** p_<_0.05, *** p_<_0.01|male-headed <br>g the transf<br>le-index of a<br>ransfer in o<br> B. All spec<br> Exchange r|households, thos<br>er, those who liv<br>sset ownership, <br>ne month instead<br>ifcations include<br>ate: MK 420 per|e with abo<br>e above the <br>and those w<br> of immedi<br> village and<br> USD. Rob|ve-median v<br> median dist<br>ith above-m<br>ately. P-valu<br> week-of-frs<br>ust standard|alues for number of<br>ance from the bank branch,<br>edian patience, as measured<br>es are from tests of equality of<br>t-survey fxed efects, and the<br> errors in parentheses.|

43
Table 12: Alternative crosssectional IV: Second-stage coefficient estimates by time period – relative to course start All MDIC registrants

|Outcome:||**Empl**|**oyed [0/1]**||
|---|---|---|---|---|
|Specifcation:|Month FE|Class FE|Class FE<br>+ educ|Class FE<br>+ educ + _Et−_1_,_3_,_6_,_9|
||(1)|(2)|(3)|(4)|
|t+6 (course ends)|0.081***|0.070***|0.067***|0.110***|
|t+7|0.071***|0.060***|0.058***|0.098***|
|t+8|0.073***|0.069***|0.066***|0.104***|
|t+9|0.068***|0.059***|0.057***|0.092***|
|t+10|0.069***|0.059***|0.056***|0.090***|
|t+11|0.058***|0.051***|0.048***|0.078***|
|t+12|0.058***|0.051***|0.047***|0.077***|
|t+13|0.053***|0.047***|0.043***|0.072***|
|t+14|0.048***|0.041***|0.038***|0.065***|
|t+15|0.052***|0.050***|0.046***|0.070***|
|t+16|0.055***|0.057***|0.053***|0.075***|
|t+17|0.050***|0.046***|0.043***|0.064***|

Notes: Table presents second-stage coefficients from the estimation of equation 5 in the text. Each row comes from a separate cross-sectional estimation of employment at the indicated month relative to course start. Specification variants are indicated in the column headings. Heteroskedasticity-consistent robust standard errors two-way clustered by course and month*year reported in parentheses. All specifications include an unreported constant term. Significance indicated by: _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01.

39
**Table 1. Data sources**

||**199**|**0 (+)**<br>|**200**|**0 (+)**<br>|
|---|---|---|---|---|
|**_Country - Age group_**|**_Origin_**|**_Education_**|**_Origin_**|**_Education_**|
|Australia (25+)|Census (#)|Census (#)|Census (#)|Census (#)|
|Austria (25+)|Census|Census|Census|Census|
|Belgium (25+)|Census|Census|Improved EC<br>(**)|LFS|
|Canada (25+)|Census (#)|Census (#)|Census (#)|Census (#)|
|Czech Rep (25+)|Census (#)|-|Census (#)|Census (#)|
|Denmark (25+)|Register|Register|Register|Register|
|Finland (25+)|Register|Register|Register|Register|
|France (25+)|Census (#)|Census (#)|Census (#)|Census (#)|
|Germany (25-65)|Microcensuz*<br>(Cit)|Microcensuz*<br>(Cit)|Microcensuz*<br>(Cit)|Microcensuz*<br>(Cit)|
|Greece (25+)|EC (Cit)|LFS (Cit.)|EC (Cit)|LFS (Cit.)|
|Hungary (All;25+)|EC (Cit)|-|Census|Census|
|Iceland (All)|Register|-|Register|-|
|Ireland (25+)|Census|Census|Census|Census|
|Italy (25+)|EC (Cit)|-|Census (Cit)|Census (Cit)|
|Japan (All/25+)|Register (Cit)|-|Census (Cit)|-|
|Korea (All)|Register (Cit)|-|Register (Cit)|-|
|Luxemburg (25+)|Census (#)|Census (#)|Census (#)|Census (#)|
|Mexico (25+)|Ipums (+) 10%|Ipums (+) 10%|Ipums (+) 10.6%|Ipums (+) 10.6%|
|Netherland (All)|Census*|Census*|Census*|Census*|
|New Zealand (15+)|Census|Census|Census|Census|
|Norway (25+)|Register|Register|Register|Register|
|Poland (13+)|Census (#)|-|Census (#)|Census (#)|
|Portugal (25+)|Census|LFS|Census|LFS|
|Slovak Rep (25+)|See Czech Rep|See Czech Rep|Census (#)|Census (#)|
|Spain (25+)|Census|Census|Census|Census|
|Sweden (25+)|Census|Census|Census|Census|
|Switzerland (18+)|Census (#)|Census (#)|Census (#)|Census (#)|
|Turkey (15+)|Census (#)|Census (#)|Census (#)|Census (#)|
|United Kingdom<br>(15+)|Census*|Census*|Census*|Census*|
|United States(25+)|Ipums(+)5%|Ipums(+)5%|Census 100%*|Census 100%*|

Notes: EC = European Council (register data); LFS = Labor Force Survey; (*) = limited level of detail.

(**) European Council data corrected by the country specific "foreign born/foreign citizen" ratio in Census 1991.

(+) Year around 1990 and 2000 (for example, the Australian censuses refer to 1991 and 2001) (#) Data available in Release 1.0.

(+) See Ruggles et al. (2004) on the US and Sobek et al. (2002) on the Mexican sample.

In 2000, we use census, microcensus and register data for 29 countries. European Council data are used in the case of Greece. Information on the country of birth is available for the large majority of countries, representing 88.3 percent of the OECD immigration stock.

12
Appendix LA.19 Historical Demographic Data, Japan 1920-1970

|Year|1920|1930|1940|1950|1960|1970|
|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|11.07|12.36|12.58|12.09|11.22|9.75|
|Population aged 20 to 59/<br>population over 60 years old|5.54|6.19|5.94|6.05|5.76|5.31|
|Percentage of population<br>over 60 years old|8.23|7.41|7.75|7.70|8.87|10.64|
|Percentage of population<br>under 15 years old|36.47|36.56|36.04|35.37|30.04|24.03|
|Women over 60 years old/<br>Men over 60 years old|1.18|1.23|1.26|1.26|1.20|1.22|

Ratios calculated using data found in Mitchell, B. R 1982 "International Historical Statistics: Asia and Africa".

# Appendix l.A.20 Historical Demographic Data, Kenya 1962-1969

|Year|1962|1969|
|---|---|---|
|Population aged 15 to 64/<br>population over 65 ye|-|13.84|
|Population aged 20 to 59/<br>population over 60 ye|7.98|6.86|
|Percentage of population<br>over 60 years old|4.90|5.28|
|Percentage of population<br>under 15 years old|46.33|48.42|
|Women over 60 years old/|||
|Men over 60 years old|0.78|0.94|

Ratios calculated using data found in Mitchell, B. R. 1982 "Intemational Historical Statistics: Asia and Africa".
countries of the world would be much more arbitrary than the use of per capita calculations. Similarly, we lack any information about within-household inequalities.<sup>22</sup> Another issue on which there is agreement is that whatever “income” is, it needs to be adjusted using a country’s relative price level. In other words, we need to use PPP exchange rates to translate domestic currencies into international dollars. Ideally, of course, one would like to move toward a better adjustment where at least for some large countries with less than fully integrated markets, PPP exchange rates would differ between different parts of the country (e.g. the price level in the richest Chinese province is estimated to be 76 percent higher than in the poorest; see Brandt and Holz, 2006). Another concern is that the relative prices faced by different parts of the income distribution are not the same. According to Pogge and Reddy (2002), relative food prices faced by the poor in poor countries are higher than what is implied by the use of a single all-consumption PPP.<sup>23</sup> The food prices are what really matters for the poor, and the use of an overall (lower) price index will artificially boosts poor people’s income in India or elsewhere in poor countries.<sup>24</sup> Pogge and Reddy advocate a cross-country project akin to the one currently conducted by the World Bank’s International Comparison Project which would generate PPPs relevant for the very modest, principally food, basket consumed by the poor across the world. But so long as within-country (provincial) PPPs and PPPs differentiated by income class are not available, we shall be obliged to use a single PPP exchange rate per country.

22 Schultz (1998) however tries to account for intra-household inequality by using genders gap in schooling (for each country) to estimate gender gaps in income at the household level.

23 The meaning of this sentence is that the ratio of food prices consumed by the poor in (say) Indonesia to the food prices of the same goods in the US is higher than the ratio between the overall price level in India and that in the US.

24 This reason behind income overestimation is as follows. The weights in the “world” consumption basket of goods and services are decisively influenced by the prices and the structure of consumption in rich countries since they are obviously the largest consumers. Then, a relatively high consumption of services in poor countries (which are cheap there) but are assessed at much higher “world” prices tends to show poor countries’ (and poor people’s) income higher than it “really” is. One possible way to adjust for this is to move from the commonly used Geary-Khamis index which has the just explained property to more “neutral” price indexes (Afriat or EKS) where the weight of rich countries is less (see Dowrick and Akmal, 2001).

13
27 TABLE !3: Average Annual Kwacha/Dpllar Exchanqe Rates and Inflation: Selected Years

||US$per Kwacha|Urban<br> <br>Low Inc|CPI<br>High Inc|Annual % c<br> <br>Low Inc|hange in CPI<br>High Inc|
|---|---|---|---|---|---|
|1970|1.40|||||
|1975|1.55|100.0|100.0|10.1|8.5|
|1980|1~27|202.9|189.4|11.7|11.9|
|1981|1.15|231.3|209.1|14.0|10.4|
|1982|1.08|260.2|236.7|12.5|13.2|
|1983|0.80|311.2|278.6|19.6|17.7|
|1984|0.56|373.5|336.8|20.0|20.9|
|1985|0.37|513.5|446.6|37.5|32.8|
|1986 July-Sept|0 <sup>.</sup>1 6<sup>(a)</sup>|778.4|707.2|51.6|58.4|
|1987 April(b)<br>**May**|0.0 5(a)<br>0.125(a)|938.3|921.6|37.4|52.4|

Notes: (a) From October 1985 onwards the value of the Kwacha was determined at a weekly auction of foreign exchange. Wide fluctuations took place during 1986. The value shown is the average value obtaining in the months July-September. Further deterioration took place. The lowest value reached is that shown for April 1987. As from May 1987 the auction was suspended, and the Kwacha's value was pegged at US$0.125.

- (b) CPI figures for 1987 are for the month of January. Inflation rate for 1987 calculated by comparing these data with January 1986 CPI, as reported by CSO.

Sources: World Bank 1986, Bank of Zambia, and GRZ 1987.
- Mankiw, Gregory, N., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. _The Quarterly Journal of Economics_ , 107(2), 407–437. 4

- Minist`ere de la Justice (2013). D ́ecret 2013-1071 du 6 aoˆut 2013. _Journal Officiel_ , (6753). 9

- Visaria, S. (2009). Legal reform and loan repayment: The microeconomic impact of debt recovery tribunals in india. _American Economic Journal: Applied Economics_ , 1(3), 59–81. 2

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