rafmacalaba/datause-encoder-data huggingface dataset:
# Figure 2 :  Irrigation water and Poverty linkages

Key Dimensions Key Impacts <!-- Start of picture text --> • Increased crop yield<br>• Increased crop areas<br>Production • Increased cropping intensity<br>• Increased crop diversification<br>• Opportunity for year round cropping<br>• Increased income from crop<br>production<br>Income/ • Increased family consumption of food<br>• Stabilisation of family income<br>• Reduced food prices<br>Consumption<br>• Increased on farm employment<br>Access to Employment opportunities<br>• Increased off farm employment<br>opportunities<br>good<br>• Stabilisation of employment<br>opportunities<br>irrigation<br>• Increased rural wage rate<br>water<br>• Enhanced food availability<br>• Increased opportunity to produce and<br>Vulnerability/ retain food for home consumption<br>• Reduced level of consumption shortfall<br>• Reduced risk of crop failure<br>Food security<br>• Reduced seasonality effects of production<br>• Reduced out migration<br>Other • Reduced indebtedness<br>• Increased resource for health and<br>education<br><!-- End of picture text --> <!-- Start of picture text --> • Reduced out migration<br>• Reduced indebtedness<br>• Increased resource for health and<br>education<br>• Improved overall resource base<br><!-- End of picture text --> Source: Based on Hussain and Hanjra (2003).

23
# **Table 7: IV estimates of EMPLOYED and EARNINGS (IFLS3, n= 823 & 493)**

||_IV estimates_||
|---|---|---|
|Independent|Probit|Heckman 2-step estimates|
|variable|Estimates of EMPLOYED|of EARNINGS using IV|
|RURAL|-0.403***|-548,234|
||(0.107)|(365,006)|
|FEMALE|-0.042|171,557|
||(0.093)|(286,221)|
|AGE|0.010|399,444***|
||(0.036)|(122,032)|
|EDU_HH|-0.062***||
||(0.013)||
|HH INCOME|-0.031***||
|(million IDR)|(0.010)||
|EBTANAS1|-0.035***|62,790*|
||(0.008)|(32,543)|
|AGE 0 TO 5|-0.004||
||(0.078)||
|AGE 6 TO 14|-0.032||
||(0.046)||
|AGE ≥ 15|0.005||
||(0.024)||
|P_SMK|0.764***||
||(0.138)||
|P_PUBLIC|-0.083||
||(0.129)||
|Predicted SMK||962,842|
|||(1,083,657)|
|Intercept|1.274|-8,345,745***|
||(0.831)|(3063216)|
|_Chi_<sup>_2_</sup>|128.05|61.16|
|_Rho:_||0.105|

18
Nonfarm activity is often the only way for women to generate income and it comprises a significant share of their income in Ethiopia (Fuje 2017, World Bank 2009). This is because it is considered inappropriate for women to engage in certain agricultural activities (such as using oxen and plowing land), which discourages single or widowed women from becoming independent farmers (Bezabih et al., 2016, World Bank 2009).

About a quarter of households own a nonfarm enterprise but there is heterogeneity by household type, as shown in Table 1. Households are classified by gender categories where female-only households are defined as those with female adults only whereas male or dual adult households are households with male adults or both male and female adults. In Appendix 2, households are also disaggregated by the sex of the household head.<sup>10</sup> Results show that a higher share of female only households compared to male or dual adult households owned nonfarm enterprises in period 1, but the reverse is observed in period 2. The results are largely consistent when households are disaggregated by sex of the household head (see Appendix 2). In Table 2, when household characteristics are controlled for in a linear probability model, female only households are more likely than male or dual adult households to own an enterprise (which is significant in period 1 and marginally significant at 10% in period 2). In period 1, female- _headed_ households are also more likely than their male counterparts to own an enterprise, but the difference is not significant in period 2.

There is also heterogeneity by poverty status: the probability of business ownership increases with household consumption for male or dual adult households, but the probability does not increase as steeply for female only households. Appendix 3 shows that smaller enterprises - measured by their turnover - tend to be owned by poorer households and poor female-only households. The question of whether low revenue enterprises face a disproportionate tax burden therefore is important from an equity and gender perspective.

**Table 1: Percentage of households with a non-farm enterprise (NFE), by household type**

||**Period**<br>**(2018/2**|**1**<br>**019)**|**Period 2**<br>**(2021/2022)**|
|---|---|---|---|
|% of households with NFEs|22.9||26.8|
|_Number of households_|_6,770_||_4,959_|
||**Period**|**1**|**Period 2**|
||**Male or dual**<br>**adult household**|**Female only**<br>**household**|**Male or dual adult**<br>**household**<br>**Female only**<br>**household**|
|% of households with NFEs|22.5|25.5|27.2<br>21.2|
|_Number of households_|_5,518_|_1,252_|_4,337_<br>_622_|

10 This paper primarily classifies households by whether there is a male adult, as the assignment of headship is often arbitrary across countries (see World Bank 2018 for a discussion). The presence of a male adult also results in women having better access to land and agricultural labor (see Komatsu et al. 2022).

7
**Table 5: Distribution of secondary activities among full time students, 2005/06**

|**Secondary Activity**|**All fulltime students**|**Working age students**<sup>**a**</sup>|
|---|---|---|
|Own account worker|0.5|1.5|
|Contributing family worker|54.2|62.0|
|Wage employment|0.5|1.6|
|Full time student|1.0|1.2|
|Children not at school|0.3|0.07|
|Other|1.3|1.3|
|None|42.2|32.4|

_Source: Author calculation based on the UNHS 2005/06; Note: respondents aged between 14 and 65 years, 12-month recall_ In sum, when detailed screening questions are used, broader economic participation is effectively captured because it brings clarity to the term as opposed to relying on the respondent’s own interpretation. Secondly, the use of screening reduces the possibility that inactivity responses will be bundled with activity responses in employment status questions. This unbundling also improves the measurement of economic participation.

# **3.3 Measurement of primary economic activities – The role of the recall period**

The measurement of the primary employment status is influenced by the recall period used. The recall period can make a significant difference to the measured participation in a typical African country where the majority of the people eke their living out of rain fed agriculture while participation in self-employment activities and casual wage work is seasonal. Under these circumstances, the use of shorter recall periods potentially understates participation in specific economic activities. For example, if an interview takes place in a month after the agriculture season, a person who primarily works on a family farm during the agriculture season could accurately report being inactive or a non-farm main activity if asked their main employment in the 7 day recall but would report the farm activity in the 12 month recall. Consequently the 7 day recall questions can underestimate overall participation (if the person reported no economic activity during the post harvest season) as well as producing a distorted picture of the categorization by activity type.

Do the differences between the answers to the 7 day and 12 month recall matter? In the UNHS 2009/10 respondents were asked if their primary activity in the past 12 months was the same as their 7 recall primary status, 7 day recall secondary activity or some other activities. As shown in Table 6 about 10 percent of the respondents reported a 12 month main employment status different from the 7 day recall main employment status. Below, we consider how in practice different recall periods actually affect the analysis of employment outcomes, at the aggregate level (e.g. structure of employment) and the individual level.

16
Table A.15: Fertility: Heterogeneity by socioeconomic status using the cross-sectional approach

||**Mothe**|**r’s Education**||**Wea**|**lth**||**Mother’s **|**Employment**||
|---|---|---|---|---|---|---|---|---|---|
||Illiterate|Literate||Bottom 40%|Top 20%||Paid employment = 0|Paid employment =|1|
||(1)|(2)|(1)-(2)|(3)|(4)|(3)-(4)|(5)|(6)|(5)-(6)|
|_Firstborn girl_|0.113***|0.198***|-0.085***|0.126***|0.183***|-0.056|0.172***|0.142***|0.030|
||(0.020)|(0.018)|(0.032)|(0.031)|(0.019)|(0.039)|(0.014)|(0.021)|(0.025)|
|_Firstborn girl_ * _Post1_|-0.063*|-0.110***|0.047|-0.060|-0.099***|0.039|-0.114***|-0.054*|-0.060*|
||(0.031)|(0.023)|(0.045)|(0.042)|(0.029)|(0.056)|(0.018)|(0.030)|(0.036)|
|_Firstborn girl_ * _Post2_|-0.059**|-0.148***|0.089**|-0.069*|-0.121***|0.052|-0.132***|-0.088***|-0.044|
||(0.028)|(0.024)|(0.040)|(0.036)|(0.027)|(0.048)|(0.021)|(0.029)|(0.035)|
|N|46,597|72,066||36,831|36,352||78,717|39840||
|Baseline mean|3.230|2.812||3.101|2.769||3.026|2.964||
|||**Caste**||**Rura**|**lity**|||||
||SC|General/OBC/ST||Rural|Urban|||||
||(7)|(8)|(7)-(8)|(9)|(10)|(9)-(10)||||
|_Firstborn girl_|0.146***|0.154***|-0.008|0.134***|0.190***|-0.056**||||
||(0.039)|(0.012)|(0.039)|(0.017)|(0.015)|(0.023)||||
|_Firstborn girl_ * _Post1_|-0.014|-0.098***|0.084|-0.095***|-0.079***|-0.017||||
||(0.056)|(0.016)|(0.056)|(0.023)|(0.023)|(0.034)||||
|_Firstborn girl_ * _Post2_|-0.032|-0.124***|0.092**|-0.108***|-0.122***|0.015||||
||(0.041)|(0.018)|(0.041)|(0.026)|(0.018)|(0.030)||||
|N|17,043|101,620||71,155|47,508|||||
|Baseline mean|3.243|2.966||3.109|2.850|||||

NOTES: Estimates of specification (3). The dependent variable is the number of births at the time of interview. Standard errors in parentheses are clustered by state. Baseline mean refers to the mean fertility of mother at time of interview, with the specific socioeconomic status who had their first and last birth in the pre-ultrasound period 1972-1984. The third column in every panel shows a test of the significance of the difference between the coefficients for the sub-groups shown in the first two columns. Data: NFHS. *** 1%, ** 5%, * 10%.
In total 2,269 announcements were sent for these jobs. In our follow-up surveys over the phone with job candidates, applicants claimed to have applied for 33.5 percent of the announcements (298/890). However, according to the firm records, only 76 applications were received from all of the announcements (3.5%). This was particularly notable for a waiter position at a restaurant run by the family of one of the research team: zero applications were received, yet more than 10 percent of those in the survey claimed to have applied for the job. We therefore view the firm records as much more accurate. 25 out of the 76 applications were invited to be interviewed, of which 21 interviews took place. In the end none of these 21 interviews resulted in a job offer being made.

Comparing the high and low prestige jobs in Table 7, the rate of application according to the firm data was 3.8 percent for the high prestige jobs, and 2.7 percent for the low prestige jobs. So youth are less likely to apply for the low prestige jobs. If we consider their self-reported (overinflated) application rates, 12.4 percent of those who said they were willing to work in 3 or more low prestige jobs applied reported having applied for a low prestige job we announced to them, compared to 14.3 percent of those who said they were not willing to work in many such jobs. So we do not see much difference in reported behavior according to this initial measure of willingness to work in less prestigious jobs.

The phone survey asked those candidates who had not applied for a job what the main reason for not applying was. Only 0.15 percent said that they thought the salary was too low, and only 2.8 percent said the job was too far away. 19.4 percent said they had already found work, but the modal answer, given by 51.4 percent of non-applicants, was that “I think this is a job that doesn’t suit me”, while 12.7 percent answered simply that they were not interested. Hence educated unemployed youth are largely not applying for jobs because they do not consider them suitable.

Given that this second experiment did not result in any jobs, we did not conduct a follow-up survey to measure the treatment-control difference in employment outcomes.

# **4.4 Discussion**

23
<u>Table 2. Correlates of caregiver under- and over-estimation using the within-community sample, Madagascar, 2016, N=3,361</u>

||<br>Model (1)||<br>Model (2)||
|---|---|---|---|---|
||Under-estimation<br>OR (95% CI)|Over-estimation<br>OR (95% CI)|Under-estimation<br>OR (95% CI)|Over-estimation<br>OR (95% CI)|
|Child Characteristics|||||
|Age (months)|0.97** (0.94 - 0.99)|1.01 (0.99 - 1.02)|0.96** (0.94 - 0.99)|1.00 (0.99 - 1.02)|
|Gender|0.89 (0.70 - 1.12)|1.05 (0.92 - 1.19)|0.88 (0.69 - 1.11)|1.00 (0.88 - 1.14)|
|Birth Order|0.99 (0.85 - 1.14)|1.01 (0.94 - 1.08)|0.99 (0.85 - 1.14)|1.01 (0.94 - 1.08)|
|HAZ|0.93 (0.83 - 1.05)|0.71** (0.66 - 0.76)|--|--|
|WAZ|--|--|0.99 (0.87 - 1.13)|0.80** (0.74 - 0.85)|
|Caregiver Characteristics|||||
|Age (years)|0.99 (0.96 - 1.01)|1.00 (0.99 - 1.01)|0.99 (0.96 - 1.01)|1.00 (0.99 - 1.01)|
|Education|||||
|No school [ref]|1.00|1.00|1.00|1.00|
|<br>Primary or less|1.07 (0.76 - 1.51)|0.95 (0.77 - 1.18)|1.07 (0.76 - 1.51)|0.96 (0.78 - 1.19)|
|<br>Secondary or Higher|0.92 (0.57 - 1.48)|0.70** (0.54 - 0.91)|0.92 (0.57 - 1.49)|0.72* (0.56 - 0.93)|
|Depression Score|1.01 (0.86 - 1.19)|1.01 (0.93 - 1.10)|1.01 (0.86 - 1.19)|1.02 (0.94 - 1.10)|
|Belief of influence on child intelligence|||||
|<br>None [ref]|1.00|1.00|1.00|1.00|
|<br>Some|0.94 (0.66 - 1.35)|0.97 (0.77 - 1.21)|0.95 (0.66 - 1.36)|0.97 (0.78 - 1.22)|
|A lot|0.68* (0.49 - 0.94)|0.99 (0.80 - 1.22)|0.68* (0.49 - 0.94)|0.99 (0.81 - 1.22)|
|Household Characteristics|||||
|Household Size|1.04 (0.98 - 1.10)|1.01 (0.98 - 1.04)|1.04 (0.98 - 1.10)|1.02 (0.99 - 1.05)|
|Family Care Indicator Score|0.96 (0.80 - 1.16)|1.00 (0.89 - 1.12)|0.96 (0.79 - 1.15)|0.98 (0.88 - 1.10)|
|<br>Wealth Quintiles|||||
|<br>Q1 (lowest) [ref]|1.00|1.00|1.00|1.00|
|<br>Q2|0.80 (0.51 - 1.25)|0.91 (0.71 - 1.15)|0.80 (0.51 - 1.25)|0.92 (0.73 - 1.17)|
|<br>Q3|<br>0.84 (0.54 - 1.32)|<br>1.06 (0.84 - 1.33)|<br>0.84 (0.54 - 1.31)|<br>1.06 (0.84 - 1.35)|
|<br>Q4|<br>0.95 (0.59 - 1.52)|<br>0.90 (0.71 - 1.13)|<br>0.94 (0.59 - 1.50)|<br>0.91 (0.73 - 1.14)|
|<br>Q5(highest)|<br>0.71(0.43 - 1.17)|<br>0.95(0.73 - 1.23)|<br>0.70(0.42 - 1.16)|<br>0.95(0.73 - 1.24)|

Multinomial logistic regression with matched caregiver-perceived child intelligence and ASQ-I ranking as the reference outcome category. All estimations adjusted for treatment arm and region, and corrected for clustering at the village level. Model (1) included height-for-age z-score while Model (2) included weight-for-age z-score.

** p<0.01, * p<0.05

15
Policy Research Working Paper 6662

# **Abstract**

The European Union Emissions Trading Scheme is the first international cap-and-trade program for carbon dioxide and the largest carbon pricing regime in the world. A significant concern over the Emissions Trading Scheme has been the potential impact on the competitiveness of industry. Using data on 5,873 firms in ten European countries during 2001–2009, this paper assesses the impact on three variables through which the effects on firm competitiveness may manifest—unit material costs, employment and revenue. The analysis focuses on the three most heavily-emitting industries under the program—power, cement, and iron and steel. Empirical results indicate that the Emissions Trading Scheme has had different impacts across these three sectors. Although no impacts are found on any of the three variables in the cement and iron and steel industries, a positive effect is found on both material costs and revenue in the power sector. The effect on material costs likely reflects fuel-switching to reduce carbon dioxide emissions, while that on revenue may be partly due to cost pass-through to consumers in a market that is less exposed to competition outside the Europen Union. Overall the findings do not substantiate concerns over carbon leakage, job loss or industry competitiveness during the study period.

This paper is a product of the Energy Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fzhang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
<!-- Start of picture text --> 2<br>@ x @<br>3 ! \ a2<br>z 1 g<br>! oF<br>gB<br>r=)31 rf<br>Ba 1 £<br>“§<br>a a f] z<br>- 4 fi 3<br>te) 4 é fo}<br>cN « ¥ = Oa<br>2 A iN / £<br>8 / ‘ee / NO S<br>ire 4 a<br>lo of c<br>= ° -<br>1985 1990 1995 2000 2005 ~<br>yeare<br>——— Fraction of births with ultrasound use<br>——-—-- Imported ultrasound machines per million persons<br><!-- End of picture text --> <!-- Start of picture text --> No. of domestically produced ultrasound machines (per million persons)<br>a<br>‘oe<br>hd<br>|. i:<br>1988-91 1992-95 1996-99 2000-03<br>Year<br><!-- End of picture text -->
The third concern is the potential for coefficient heterogeneity on _DV A_<sup>_j_</sup> _xit_<sup>,principally</sup> due to the presence of imports in the denominator of _βijxt_ . We address this issue here by substituting ln( _DV A_<sup>_j_</sup> _xit_<sup>) for</sup><sup>_DV Aj_</sup> _xit_<sup>.The logic is as follows.</sup><sup>_DV Aj_</sup> _xit_<sup>and bilateral final goods</sup> imports are strongly positively correlated in the data, with a raw correlation of 0.75. Because _βijxt_ is inversely related to the level of bilateral final goods imports, we therefore expect that a $1 change in _DV A_<sup>_j_</sup> _xit_<sup>atlowlevelsof</sup><sup>_DV Aj_</sup> _xit_<sup>tobemoreinfluentialthana$1changein</sup> _DV A_<sup>_j_</sup> _xit_<sup>athighlevelsof</sup><sup>_DV Aj_</sup> _xit_<sup>.Thelogfunctionisaconvenienttransformationofthe</sup> data that captures this mechanism and so allows us to estimate a homogeneous coefficient for domestic value added.

Based on this discussion, the first specification that we take to the data is: where Φ _xit_ and Φ _xjt_ are importer-industry-year and exporter-industry-year fixed effects. The DVA sign prediction is _β <_ 0.

# **2.1.1 Reciprocal vs. Non-Reciprocal Tariffs**

Thus far, our discussion has focused on unilateral (non-reciprocal) tariffs. As discussed in Section 1.4, reciprocal trade agreements may nullify the influence of domestic value added on tariffs. This result depends on full-reciprocity, which may or may not obtain given the institutional design of particular bilateral trade negotiations. Little is known empirically about the extent to which reciprocal trade agreements actually neutralize bilateral termsof-trade externalities. We therefore initially adopt an agnostic approach to the question of whether domestic value added effects are present in RTAs.

We start by pooling data on non-reciprocal and reciprocal tariffs, treating Equation (16) as describing all bilateral tariffs. We then (quickly) proceed to test whether domestic value added has similar effects on tariffs inside and outside reciprocal trade agreements. To do so, we augment Equation (16) to allow trade agreements to alter the responsiveness of tariffs to domestic value added, as well as shift the level of tariffs directly.<sup>29</sup> The augmented specification is:

> 29Level effects are implied by the discussion in Section 1.4, in that the additive inverse export supply elasticity term in the non-reciprocal optimal tariff disappears in the reciprocal tariff.

17
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Table 1: Baseline regressions for the difference in bank loan-to-asset ratio between 2006 and 2009<sup>_†_</sup>

||**A1**|**A2**|**A3**|**A4**|**A5**|**A6**|
|---|---|---|---|---|---|---|
|Crisis experience|-0.079<br>(0.02)<sup>_∗∗∗_</sup>|-0.073<br>(0.02)<sup>_∗∗∗_</sup>|-0.082<br>(0.04)<sup>_∗∗_</sup>|-0.080<br>(0.04)<sup>_∗∗_</sup>|-0.077<br>(0.04)<sup>_∗∗_</sup>|-0.110<br>(0.05)<sup>_∗∗_</sup>|
||||_Bank co_|_variates_|||
|Size||0.000|-0.000||0.000|-0.000|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Solvency||-0.179|-0.170||-0.178|-0.164|
|||(0.09)<sup>_∗∗_</sup>|(0.08)<sup>_∗∗_</sup>||(0.09)<sup>_∗∗_</sup>|(0.09)<sup>_∗_</sup>|
|Proftability||-0.018|-0.015||-0.018|-0.015|
|||(0.02)|(0.02)||(0.02)|(0.02)|
|Interest margin||0.613|0.605||0.614|0.611|
|||(0.20)<sup>_∗∗∗_</sup>|(0.21)<sup>_∗∗∗_</sup>||(0.19)<sup>_∗∗∗_</sup>|(0.20)<sup>_∗∗∗_</sup>|
||||_Host countr_|_y covariates_|||
|GDP per cap.|||-0.000|||-0.000|
||||(0.00)|||(0.00)|
|Lagged GDP|||-0.100<br>|||-0.055<br>|
|growth|||(0.73)|||(0.72)|
|Current account|||-0.189|||-0.235|
|balance|||(0.17)|||(0.16)|
|Infation|||0.236<br>(0.41)|||0.279<br>(0.40)|
|Host fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|1st-stage _F_||||0.00|0.00|0.00|
|Underid. test||||0.00|0.00|0.00|
|Weak id. test (rel. bias)||||_<_5%|_<_5%|_<_5%|
|Overid. test||||0.61|0.59|0.44|
|_R_<sup>2</sup>|0.02|0.06|0.08||||
|2nd-stage _F_|0.00|0.01|0.00|0.03|0.02|0.01|
|Estimation|OLS|OLS|OLS|IV|IV|IV|
|Obs. (Host countries)|365 (77)|362 (76)|361 (76)|350 (62)|347 (61)|346 (61)|

> _†_ The dependent variable is the difference in bank loan-to-asset ratios between 2006 and 2009. Heteroskedasticity and host country correlation-robust standard errors are reported in parentheses. A constant term was included in the regressions, but not reported. The underidentification test is the Kleibergen-Paap rk _LM_ , the weak identification test is the Kleibergen-Paap rk Wald _F_ , and the overidentification test is the Hansen _J_ .<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

management experience from their rich financial and banking sectors, which compensates for their lack of direct crisis experience. A related possibility is that stricter prudential regulation due to their status as global financial centers may forestall the incidence of crises. In order to take these possibilities into account, Section 6 considers introducing additional banking and financial-sector variables, along with an alternative measure of financial experience.

To gauge the economic significance of our results, we examine in greater detail the average foreign-owned bank of our sample, which carries a loan-to-asset ratio of 48.09 in 2006, and initially 17