rafmacalaba/datause-encoder-data huggingface dataset:
were delayed: their expectations about future income changed, but were unfulfilled (at least within the survey recall period).

# **7 Conclusion**

Public works programs can stabilize income and improve food security of beneficiaries by providing earnings opportunities, and can achieve targeting through low wages and work requirements that promote self-selection. While Malawi’s PWP offers households the opportunity to earn approximately $22 at planting season and an additional $22 later in the year, it does not have a measurable short-term effect on lean season food security for treated households. Even improving the structure of the program by rescheduling the second work cycle from the harvest season to the lean season does not generate measurable improvements in the food security of treated households.

While the maximum possible income from PWP is substantial in light the country’s percapita GNI of $320, the magnitude of the transfer is low compared to other social protection tools such as cash transfers being studied in Malawi,<sup>18</sup> or other PWP programs in Sub-Saharan Africa.<sup>19</sup> Households may have spread consumption across the four to eight month (depending on treatment group) PWP period or saved for even longer durations. Then, changes in weekly spending (the interval captured in our survey period) may be too small to detect, especially since extra spending may have been spread across many different categories of goods. We can rule out significant improvements in the two outcomes specifically targeted by the program – food security and use of fertilizer – but there may have been small, diffuse increases in these or other outcomes that we do not detect. This interpretation allows for the possibility that the PWP was welfare-improving for households that chose to participate and thus is consistent with their revealed preferences, while still ineffective in achieving its main policy objectives.

In Malawi, the public works program is designed with an additional goal: it is timed to coincide with the planting season to promote take-up of the country’s fertilizer subsidy scheme. However, our results do not support the hypothesis that the two programs are complementary. While households included in PWP are more likely to receive fertilizer

> 18In their study of the cash transfer project in one district of Malawi in 2007, Miller, Tsoka & Reichert (2011) find large, positive effects on beneficiary households in program villages compared to households in control villages screened as eligible but not given the program. In this program the size of the benefit is significantly larger, with transfers totaling $168 per household over the course of a year (equivalent to about $250 in 2012 price levels), an amount more than five times what households received from PWP 2012/2013.

> 19Comparable PWP programs in the region, such as the ones in Ethiopia, Sierra Leone and Ghana, have maximum earnings of between $60 and $190.

29
# **Gender Differences in Time Use:**

# **Allocating Time between the Market and the Household**<sup>*****</sup>

Eliana Rubiano-Matulevich<sup>†</sup> The World Bank Mariana Viollaz<sup>‡</sup> CEDLAS-FCE-UNLP

## Keywords: gender; work; time allocation.

JEL classification: J16, J22, D13

> * Authors would like to thank Bénédicte de la Brière and Caren Grown for their advice, insights and comments. Authors are grateful to Kathleen Beegle, Niklas Buehren, Tamoya Christie, Isis Gaddis, Alicia Hammond, Lucia Hanmer, Aphichoke Kotikula, and Michael O’Sullivan for useful comments. The team also benefited from inputs provided by Ana Maria Muñoz and Beth Zikronah Rosen at early stages of the research. Angel Iglesias and Chang He provided outstanding research assistance.

> † The World Bank, 1818 H Street NW Washington DC, USA. email: erubiano@worldbank.org

> ‡ Centro de Investigación y Docencia Económica (CIDE), Carretera México-Toluca 3655, Ciudad de México, México, and Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), La Plata, Argentina. email: <u>mariana.viollaz@cide.edu</u>
- 14 -

the marginal transfer dollar to the poorest persons.14/

# IV. Application of the Method to Survey Data from C6te d'Ivoire

In this section the application of the method preserted in Section III is applied to data from the 1985 C6te d'Ivoire Living Standards Survey (CILSS), which is described in detail in Grootaert (1986) and Ainsworth and Mufioz (1986). The choice of country is primarily due to data availability, yet it is of some interest to apply the method to an African country since poverty is quite severe in many African countries, though C6te d'Ivoire is relatively well off by African standards. The variable to be predicted is per capita household expenditures, which includes imputed values of owner-occupied housing in urban areas. The construction of the expenditure variable is explained in detail in Glewwe (1987) except, unlike in that paper, household equivalence scales are not employed here.

At this point it is instructive to discuss the Ivorian economy briefly. C6te d'Ivoire is found in West Africa on the Gulf of Guineau. It received its independence from France in 1960 and up to the late 1970's was considered to be one of Africa's success stories (cf. den Tuinder, 1978). Its main export crops are coffee and cocoa. Since the early 1980's the economy has declined, in part due to declining prices of coffee and cocoa. Yet it is still better off than most other West African countries and has a relatively high proportion of the population in urban areas, about 43Z. The vast

> - 14/ For the income gap ratio this strategy is sufficient but not necessary one need only ensure that all transfers go to individuals whose incomes are below the poverty line.
**Table E7: Relationship between food security measures and weight-for-age z-scores (Bangladesh): Expanded Controls Version**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES||Weight-for-|age z-score||
|HFIAS Score|-0.034***||||
||(0.011)||||
|Child DDS||-0.036***|||
|||(0.014)|||
|FCS|||0.034***||
||||(0.011)||
|MDDS||||0.034***|
|||||(0.010)|
|Age (in month)|-0.085***|-0.081***|-0.085***|-0.086***|
||(0.002)|(0.003)|(0.002)|(0.002)|
|Age squared|0.001***|0.001***|0.001***|0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Female|-0.077***|-0.079***|-0.077***|-0.077***|
||(0.018)|(0.018)|(0.018)|(0.018)|
|Household size|-0.011*|-0.012**|-0.013**|-0.013**|
||(0.006)|(0.006)|(0.006)|(0.006)|
|Share of kids under 5|-0.352***|-0.348***|-0.338***|-0.346***|
||(0.106)|(0.106)|(0.106)|(0.106)|
|_Household head education_|||||
|Functional education and/or < 5 yrs of education|0.036|0.040|0.038|0.037|
||(0.027)|(0.027)|(0.027)|(0.027)|
|[5-7 years of education]|0.055**|0.060**|0.057**|0.058**|
||(0.026)|(0.026)|(0.026)|(0.026)|
|[8-10 years of education]|0.111***|0.119***|0.113***|0.114***|
||(0.030)|(0.030)|(0.030)|(0.030)|
|11+ years of education|0.235***|0.248***|0.237***|0.239***|
||(0.054)|(0.055)|(0.054)|(0.054)|
|Female-headed HH|0.022|0.018|0.018|0.019|
||(0.030)|(0.030)|(0.030)|(0.030)|
|_Mother's Education_|||||
|functional education and/or < 5 yrs of education|0.005|0.010|0.004|0.003|
||(0.028)|(0.028)|(0.028)|(0.028)|
|[5-7 years of education]|0.005|0.017|0.007|0.008|
||(0.026)|(0.026)|(0.026)|(0.026)|
|[8-10 years of education]|0.127***|0.149***|0.129***|0.131***|
||(0.031)|(0.031)|(0.031)|(0.031)|
|11+ years of education|0.310***|0.341***|0.305***|0.310***|

50
38

**Table 7: Regressions results across the 4 themes showing differences between women and men in households where the women is working (vs households where she is not)**

||1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|
|Outcome|Workingfemale|Working<br>female|Working<br>female|Working<br>female|Working<br>female|Working<br>female|Working<br>female|Working<br>female|Working<br>female|Working<br>female|
|Theme|**WW**|**WW**|**PM**|**PM**|**GR**|**GR**|**GR**|**PR**|**PR**|**PR**|
|Respondents|Women<br>respondents|Male<br>respondents|Women<br>respondents|Male<br>respondents|Both|Women<br>respondents|Male<br>respondents|Both|Women<br>respondents|Male<br>respondents|
|**Social Empirical**|||||||||||
|**Index**|0.0613***|0.0967***|0.0104|0.0110|0.0696***|0.0609***|0.0745***|0.0118|-0.0222|0.0420*|
||[0.0180]|[0.0198]|[0.0170]|[0.0202]|[0.0135]|[0.0168]|[0.0206]|[0.0148]|[0.0173]|[0.0219]|
|**Social**|||||||||||
|**Normative**|||||||||||
|**Index**|-0.0338**|0.00805|-0.0466***|0.0171|-0.00732|-0.0398**|0.00739|0.00960|-0.0139|0.0259|
||[0.0156]|[0.0208]|[0.0176]|[0.0201]|[0.0130]|[0.0186]|[0.0192]|[0.0180]|[0.0235]|[0.0273]|
|**Personal Belief**|||||||||||
|**Index**|-0.0188|0.00912|0.0291|0.0214|0.0352**|0.0286|0.0366*|-0.0393**|-0.0532**|-0.0163|
||[0.0283]|[0.0130]|[0.0206]|[0.0206]|[0.0139]|[0.0205]|[0.0197]|[0.0183]|[0.0232]|[0.0288]|
|**Counterpart**|||||||||||
|**Expectation**|||||||||||
|**Index**|0.00266||0.00952|||0.0373*||0.0143|0.0186|-0.00168|
||[0.0164]||[0.0179]|||[0.0195]||[0.0183]|[0.0232]|[0.0283]|
|**Controls**|||||||||||
|Age|0.0141***|0.00344|0.0149***|0.00359|0.00792***|0.0143***|0.00345|0.00895***|0.0150***|0.00377|
||[0.00219]|[0.00230]|[0.00225]|[0.00228]|[0.00156]|[0.00219]|[0.00228]|[0.00178]|[0.00247]|[0.00262]|
|Yrs Education|0.0309***|0.00152|0.0325***|0.00534|0.0164***|0.0290***|0.00305|0.0199***|0.0348***|0.00409|
||[0.00359]|[0.00456]|[0.00368]|[0.00448]|[0.00284]|[0.00362]|[0.00441]|[0.00306]|[0.00385]|[0.00484]|
|Married|-0.134**|-0.108|-0.133**|-0.109|-0.125***|-0.121**|-0.106|-0.162***|-0.161**|-0.116|
||[0.0609]|[0.0777]|[0.0627]|[0.0777]|[0.0472]|[0.0603]|[0.0776]|[0.0531]|[0.0689]|[0.0846]|
|Erbil|0.0961**|0.0789*|0.0103|0.0731|0.0591*|0.0640|0.0355|0.0365|0.0179|0.0486|
||[0.0461]|[0.0461]|[0.0504]|[0.0462]|[0.0324]|[0.0461]|[0.0460]|[0.0399]|[0.0558]|[0.0552]|
|Suli|0.0450|0.120***|-0.0205|0.120***|0.0431|-0.0132|0.0813*|0.0319|-0.0435|0.103|
Table 2: Estimated e¤ects of harmonization and MRAs

|Regressand: ln(import)|I|II|
|---|---|---|
|Harmonization on intra-regional trade|0.2749***|0.2562***|
|(HAR)|(0.010)|(0.011)|
|importers with stricter initial standards||0.0989***|
|(HAR�strictness)||(0.022)|
|Harmonization on imports from the ROW|0.0950***|0.0310***|
|(HAR_M)|(0.005)|(0.005)|
|importers with stricter initial standards||0.2477***|
|(HAR_M �strictness)||(0.011)|
|Harmonization on exports to the ROW|0.6438***|0.6393***|
|(HAR_E)|(0.005)|(0.005)|
|MRAs with rules of origin on intra-regional trade|2.3540***|2.3589***|
|(MRA_RO)|(0.031)|(0.031)|
|MRAs with rules of origin on imports from the ROW|-0.4768***|-0.4799***|
|(MRA_RO_M)|(0.037)|(0.037)|
|MRAs with rules of origin on exports to the ROW|0.3956***|0.3963***|
|(MRA_RO_E)|(0.036)|(0.036)|
|MRAs without rules of origin on intra-regional trade|0.6362***|0.6390***|
|(MRA_NRO)|(0.074)|(0.074)|
|MRAs without rules of origin on imports from the ROW|0.7794***|0.7950***|
|(MRA_NRO_M)|(0.037)|(0.037)|
|MRAs without rules of origin on exports to the ROW|1.6235***|1.6154***|
|(MRA_NRO_E)|(0.037)|(0.037)|
|RTA on intra-regional trade|1.7225***|1.7266***|
|(RTA)|(0.011)|(0.011)|
|RTA on imports from the ROW|0.0458***|0.0404***|
|(RTA_M)|(0.005)|(0.005)|
|RTA on exports to the ROW|0.0309**|0.0292***|
|(RTA_E)|(0.005)|(0.005)|
|Number of observations|4160352|4160352|
|Log likelihood|-7840111|-7839841|

Standard errors are rep orted in parentheses. Exp orter/Im p orter-industry-year, pair-industry ...xed e¤ects are controlled. ***, **, and * represent 1% , 5% , and 10% signi...cance levels, resp ectively.

19
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics

# **5 Regional Analysis**

## **5.1 Empirical Specification**

Our regional analysis compares the evolution of outcomes in markets facing larger tariff cuts to those in markets facing smaller cuts. For each year _t_ following the beginning of liberalization (1992 to 2010), we estimate an equation of the following form: where _yrt_ is the value of a regional outcome such as earnings or employment, _θt_ is the effect of liberalization on outcomes by year _t_ , _αst_ are state fixed effects, and ( _yr,_ 1990 _− yr,_ 1986) is a preliberalization trend in the outcome variable. While the change in outcome varies with the year _t_ under consideration, the liberalization shock, _RTCr_ , does not. Instead, it always reflects the regional measure of tariff changes _during liberalization_ , from 1990 to 1995.<sup>22</sup> Using this strategy, each year’s _θt_ estimates one point on an impulse response function describing the local effects of liberalization in each post-liberalization year.

We use 1991 as the base year for outcome changes to make results comparable between RAIS and the decennial census. We include state fixed effects to account for any state-specific policies that might commonly affect outcomes for all regions in the same state, such as state-specific minimum wages, introduced in 2002 (Neri and Moura 2006). We include pre-liberalization trends in outcomes ( _yr,_ 1990 _− yr,_ 1986) to address the possibility of confounding ongoing trends. For our main outcomes, we present results with and without state fixed effects and pre-trends, with little effect on the coefficients of interest. Since many of our dependent variables are themselves estimates, we weight regressions based on the inverse of their standard error to account for heteroskedasticity. We also cluster standard errors at the mesoregion level to account for potential spatial correlation in outcomes across neighboring regions.

To consistently estimate _θt_ , _εrt_ must be uncorrelated with _RTCr_ , conditional on the state fixed effects and outcome pre-trend. For this identification assumption to be violated, there would need to be an omitted variable that i) drives wage or employment growth across regions within a state and ii) is correlated with _RTCr_ but iii) is _not_ captured by pre-liberalization outcome trends. While such a feature is unlikely to exist, in Section 5.3 we confirm that our results are robust to a variety of potential confounders and alternative specifications.

## **5.2 Regional Earnings and Employment**

We begin by examining the local labor market effects of liberalization on formal sector earnings and employment in each year following liberalization. Because hours information is not available 22Recall from Section 2 that tariffs declined between 1990 and 1995, after which they remained relatively stable.

15
41 “transitional” agreement with the International Monetary Fund. In this phase, central bank president Alfonso Prat-Gay has pursued a monetary target and gradually liberalized foreign exchange controls to dampen the tendency of the peso to regain strength, in light of the need for a highly competitive exchange rate to boost import substitutes and exports and, especially, revenue from export taxes. The alternative of holding the exchange rate through intervention in the exchange market would have placed more pressure on monetary expansion. An overarching feature of the successive phases, many in Argentina would argue, has also been the end of the large capital outflows (including into “mattress” local holdings of US currency) that occurred in the second half of 2001 and first half of 2002.

# _B..  Output and inflation_

As noted, the financial crisis and extreme uncertainty had already caused a major decline in output in the second half of 2001, and following the bank deposit freeze in December, the devaluation, and the default, in the first quarter of 2002 output plunged further. Industrial production fell to about 22 percent below its already weak average of the first three quarters of 2001, before staging a moderate but persistent recovery during the second through the fourth quarters of 2002 (figure 7). Open unemployment, which had been 15 percent in 2000 and had risen to 18.3 percent by October 2001, rose further to 21.5 percent in May 2002 before easing to 17.9 percent by October 2002 (MECON, 2003b).

Figure 7 Industrial production (1997=100) <!-- Start of picture text --> 110<br>100<br>90<br>80<br>70<br>60<br>50<br>99.1 99.3 '00.1 '00.3 '01.1 '01.3 '02.1 '02.3 '03.1<br><!-- End of picture text --> Source: MECON (2003b) Like those for output and employment, inflation trends were severe in the first half of 2002 but showed surprising improvement by the second half. Monthly inflation peaked in April 2002 at 20 percent for the wholesale price index (the more sensitive to traded goods prices) and 10 percent for the consumer price index. The cumulative rise in prices above the December 2001 level reached about 120 percent for the wholesale price index and about 40 percent for the consumer price index. Crucially, almost all of this increase occurred by August, and thereafter inflation fell back close to zero rather than spiraling upward (figure 8).
recommended business schools that they should improve effectiveness of business programs, by putting more emphasis on ―Core Skills‖.

Hill and Petty (1995) conducted a similar analysis but focused on occupational work ethics. By using factor analysis, forty eight skills were grouped into four factors: interpersonal skills, initiative, being dependable, and ―reversed items on instrument‖ (See Table 2). The last factor was interpreted as negative perceptions, in which there are (lack of) skills such as _irresponsible_ , _careless_ , _selfish_ , etc. The study recommended that school curriculum should address the four factors to make student skills more relevant to the workplace.

**Table 2: Work Ethics under Four Factors**

|**Factor 1: Interpersonal Skills**|**Factor 2: Initiative**|**Factor 3: Being Dependable**|**Factor 4: Reversed Items**|
|---|---|---|---|
| Courteous| perceptive| following directions| hostile|
| friendly| productive| following regulations| rude|
| cheerful| resourceful| dependable| selfish|
| considerate| initiating| reliable| devious|
| pleasant| ambitious| careful| irresponsible|
| cooperative| efficient| honest| careless|
| helpful| effective| punctual| negligent|
| likeable| enthusiastic|| depressed|
| devoted| dedicated|| tardy|
| loyal| persistent|| apathetic|
| well groomed| accurate|||
| patient| conscientious|||
| appreciative| independent|||
| hard working| adaptable|||
| modest| persevering|||
| emotionally stable| orderly|||

- stubborn

These prior works guided us in developing the survey methodology analysis for this employer survey in India.

# **3. Survey Methodology**

FICCI and World Bank conducted an on-line employer satisfaction survey from September to November, 2009. 157 employers across sectors and regions in India fully completed the questionnaire. The questionnaire (Annex 3) has a list of skills that engineering graduates are typically expected to possess at graduation. Employers were requested to rate on a scale from 1 (not at all) to 5 (extremely) how important each skill is for an engineering graduate to be an effective employee, (Importance Level). The survey also asked employers to rate their satisfaction level with regard to each of the skills, (Satisfaction Level).

6
# **1. Introduction**

More than half the world’s population still cooks with wood, dung, coal or agricultural residues on simple stoves or open fires. Dependence on solid-fuel for cooking leads to high exposures to household smoke, particularly where ventilation is limited, and is associated with significant health risks, particularly for women and children.<sup>4</sup> In analyzing the risks associated with Household Air Pollution (HAP), the Global Burden of Disease Study 2010<sup>5</sup> (GBD 2010) considered its contribution to the following health outcomes: lower respiratory infections; trachea, bronchus, and lung cancers; ischemic heart disease (IHD); cerebrovascular disease; chronic obstructive pulmonary disease (COPD); and cataracts.<sup>6</sup> GBD 2010 determined that the three leading risk factors for the global burden of disease were high blood pressure (7.0% of global Disability-Adjusted Life Years - DALYs<sup>7</sup> ), tobacco smoking including second-hand smoke (6.3%), and HAP from solid fuels (4.3%). In most of Sub-Saharan Africa (SSA) childhood underweight, HAP, and non-exclusive/discontinued breastfeeding were the leading risk factors in 2010.<sup>8</sup> In Asia and Sub-Saharan Africa, where a large share of ambient particulate matter originates from solid fuel use, exposure to ambient pollution and HAP are related, and cleaner fuels would bring benefits both for people who currently use solid fuels, as well as for their neighbors who do not.

While the transition to cleaner sources of domestic energy will ultimately reduce the disease burden associated with this risk factor, during the transition good ventilation, improved stoves, and public information leading to behavior change can significantly reduce exposure to household smoke. While reduction of HAP has been recognized as an important development objective, the design of HAP reduction strategies has been hindered by lack of information about actual air quality in households and health benefits of potential mitigation measures. Data on HAP have been scarce because monitoring in households has been difficult and costly. While many studies have measured gaseous and sometimes particulate emissions from standardized or laboratory tests, it is not clear whether these emissions are representative of cook-stoves in use.<sup>9</sup>

> 4 WHO, 2009

> 5 The Global Burden of Disease Study 2010 is a collaborative project of nearly 500 researchers in 50 countries led by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington. 6 Lim et al., 2012

> 7 Disability-adjusted life years (DALYs) quantify both premature mortality and disability within a population. 8 Ibid.

> 9 Roden et al., 2009

2