rafmacalaba/datause-encoder-data huggingface dataset:
sample, we test whether the stark differences across groups are partly due to the diversity of Group A, which includes women with children of any ages 6 years or above, as well as women with no children. The sample restriction effectively narrows the age range of the children in the reference category to 6–12 years and 6–8 years, respectively.

Again, the main findings and conclusions remain unchanged—if anything, the penalty experienced by mothers of young children grow larger, with those without any childcare support experiencing the largest disadvantage compared to the reference group. As shown in Table 3, under Subsample 2, women in Group C are estimated to have a lower probability of being earners, participating in the labor force, and working than women in Group A by 37.6, 35.3, and 38.8 percentage points, respectively. Similarly, compared to Group A, Group C is estimated to have a lower probability of being earners, participating in the labor force, and working by 35.8, 34.8, and 33.7 percentage points, respectively, under Subsample 3. Comparing Groups B and C, women with children aged 0–5 years face a smaller penalty when childcare support is available. The penalty that women with children aged 0–5 years who have access to childcare face in their probability of working in the last 30 days is 13.5 percentage points lower than that of women with children aged 0–5 who do not have access to childcare. The Wald tests confirm that the difference in marginal effects for Groups B and C are statistically significant (see Appendix B, Table B2).

# _3.2. Time Allocation on Market Work, Unpaid Work, and Other Activities_

On any given day, individuals allocate 24 hours across various activities, with any change in the time spent on one activity affecting the amount of time remaining for other activities. Figure 3 shows the time allocation by women in Group A, Group B, and Group C across three primary activities: market work, unpaid work, and other activities. As expected, women in Group A, who face the least amount of pressure for childcare (because they have older children or no children), spend 4.1 hours on average per day on market activities, the largest among the three groups. Women with young children spend on average 2.3 hours per day on market work, while those in Group C spend only 0.9 hours. Compared to Group A, the latter two groups also spend two additional hours on unpaid work that includes domestic chores and caregiving. In particular, compared to 4.5 hours per day of unpaid work performed by women in Group A, those in Group B spend 6.4 hours and those in Group C spend 6.6 hours per day.

Although the bivariate difference in time spent on market work between Group B and Group C are statistically significant, with women in Group C being significantly constrained in their ability to take up market work, there is no such difference between these two groups in the amount of time spent on 18
people in rural Kenya still depend on natural sources of water such as streams and springs for their household water needs. Rural women as the primary users and managers of household water supplies continue to obtain their water from unprotected sources often at great distances from their villages (World Bank, 1989).

Table 6 Water Supplies

|Distance from<br>source|Average time<br>per trip|Average time<br>per day|Amount per hour<br>spent finding|
|---|---|---|---|
|Short|.75 hr.|3.5 hrs.|25 litres|
|Short-middle|<br>1.0 hr.|3.0 hrs.|23 litres|
|Middle|1.25 hrs.|<br>4.0 hrs.|19 litres|
|Middle-long|1.75 hrs.|<br>4.5 hrs.|16 litres|
|Long|2.75 hrs.|<br>5.25 hrs.|9.5 litres|

Source: Krystall and Gommes, 1979 cited in PHRWD, 1988.

Programs for the supply of water to rural areas include construction and maintenance of new supplies, the rehabilitation of existing schemes, and self-help water supply systems (GOK and UNICEF, 1988). When women are trained in the maintenance of water supply systems, the efficiency of water projects is enhanced. More accessible water supplies can also be provided through water conservation or harvesting which involves the accumulation of water in catchment systems. Roof catchment systems appear to be a feasible technology for village level operation and maintenance. Women have traditionally been involved in the mabati movement to purchase tin roofs that allow rainwater to be collected and stored. Studies from Kenya have shown that with the time saved from water collection and money earned from selling some of the water, women were able to increase their production of vegetables and chickens for sale (Tinker, 1979).

In areas where water has become more easily accessible, women have been found to use the extra time to make additional trips to the water source reflecting the emphasis women place on water for family wellbeing. Women may also use the time saved for childcare, food preparation, community activities and income-generation activities therefore enhancing their ability to assure household food security (World Bank, 1989). The income-generating activities may be possible due to women's increased time and/or directly a result of more accesible water as in the case of women having small irilgated plots for cash crops and vegetables near newly installed standpipes (World Bank, 1989).

# d. Credit

Women's lack of access to credit influences their ability to assure household food security. Women smallholders are constrained from undertaking 22
interpret because the intervention and openness indexes are positively (e.g., 2.63) and significantly (at the 0.01 level) correlated with each other.9 We also examine measures of international price distortions. Dollar (1990) constructs an index of the distortion between domestic and international prices (1976-1985) from the Summers and Heston data set. This ‘real exchange rate distortion" index is significantly positively correlated with the black market exchange rate premium, but it is negatively correlated with the ratio of trade to GDP. These correlations plus the analysis by Pritchett (1990) suggest that one may want to interpret Dollar’s (1990) index as a general measure of international distortions and not a narrow measure of trade policy.

Table 6-A shows that Dollar’s index is negatively though not robustly correlated with growth when the extreme bounds procedure is allowed to choose the African and Latin American continent dummies and the average fiscal deficit to GDP ratio. If the continent dummies are excluded, however, the partial correlation between growth and the real exchange rate distortion index is negative and robust. But, if one restricts the analysis to countries for which Summers and Heston actually collect price data (the benchmark countries) and excludes those countries for which extrapolation techni:.es were employed to construct price data, the results are not robust. If one adds the government consumption less defense and education spending to GDP ratio to the I-variables, the coefficient on the distortion index becomes insignificant.

Finally, we examine the average black market exchange rate premium. Since this variable represents the interactions of many policies, it is very difficult 19 after carefully examining the relationship between different measures of trade policy, Pritchett (1990) concludes that the “alternative objective summary measures of policy outward orientation produce entirely different country rankings ...* (p. 29) This assessment has obviously dour implications for attempts to quantify the relationship between trade policy and long-run growth.

22
|P101209|2008|2009|4/13/2009|2 Active|Honduras|
|---|---|---|---|---|---|
|P101209|2008|2010|11/16/2009|3 Active|Honduras|
|P101209|2008|2010|4/20/2010|4 Active|Honduras|
|P101209|2008|2010|5/27/2010|5 Active|Honduras|
|P101209|2008|2010|5/27/2010|6 Active|Honduras|
|P101209|2008|2011|2/13/2011|7 Active|Honduras|
|P101209|2008|2012|8/10/2011|8 Active|Honduras|
|P101209|2008|2012|12/28/2011|9 Active|Honduras|
|P101209|2008|2012|6/29/2012|10 Active|Honduras|
|P101209|2008|2013|2/13/2013|11 Active|Honduras|
|P101209|2008|2014|8/11/2013|12 Active|Honduras|
|P101209|2008|2014|2/22/2014|13 Active|Honduras|
|P101209|2008|2014|4/8/2014|14 Active|Honduras|
|P101209|2008|2015|11/7/2014|15 Active|Honduras|
|P101209|2008|2015|4/27/2015|16 Active|Honduras|
|P101209|2008|2016|11/19/2015|17 Active|Honduras|
|P101209|2008|2016|3/6/2016|18 Active|Honduras|
|P101209|2008|2017|9/19/2016|19 Active|Honduras|
|P101209|2008|2017|3/17/2017|20 Active|Honduras|
|P101209|2008|2018|9/22/2017|21 Active|Honduras|
|P101209|2008|2018|3/30/2018|22 Active|Honduras|
|P101209|2008|2019|11/19/2018|23 Active|Honduras|
|P101209|2008|2019|5/31/2019|24 Active|Honduras|
|P101209|2008|2020|12/18/2019|25 Active|Honduras|
|P101209|2008|2020|6/26/2020|26 Active|Honduras|
|P101211|2009|2009|2/27/2009|1 Closed|Colombia|
|P101211|2009|2010|11/11/2009|2 Closed|Colombia|
|P101211|2009|2010|6/28/2010|3 Closed|Colombia|
|P101211|2009|2011|12/18/2010|4 Closed|Colombia|
|P101213|2007|2007||0 Closed|Bosnia and Herzegovin|
|P101213|2007|2008|9/10/2007|1 Closed|Bosnia and Herzegovin|
|P101213|2007|2008|11/30/2007|2 Closed|Bosnia and Herzegovin|
|P101213|2007|2008|5/29/2008|3 Closed|Bosnia and Herzegovin|
|P101213|2007|2009|12/30/2008|4 Closed|Bosnia and Herzegovin|
|P101213|2007|2010|8/7/2009|5 Closed|Bosnia and Herzegovin|
|P101213|2007|2010|12/2/2009|6 Closed|Bosnia and Herzegovin|
|P101213|2007|2010|6/10/2010|7 Closed|Bosnia and Herzegovin|
|P101213|2007|2011|2/25/2011|8 Closed|Bosnia and Herzegovin|
|P101213|2007|2012|10/23/2011|9 Closed|Bosnia and Herzegovin|
|P101213|2007|2012|4/24/2012|10 Closed|Bosnia and Herzegovin|
|P101213|2007|2013|11/27/2012|11 Closed|Bosnia and Herzegovin|
|P101213|2007|2013|12/30/2012|12 Closed|Bosnia and Herzegovin|
|P101213|2007|2013|6/29/2013|13 Closed|Bosnia and Herzegovin|
|P101213|2007|2014|1/4/2014|14 Closed|Bosnia and Herzegovin|
|P101213|2007|2015|7/3/2014|15 Closed|Bosnia and Herzegovin|
|P101213|2007|2015|12/22/2014|16 Closed|Bosnia and Herzegovin|
|P101213|2007|2015|6/10/2015|17 Closed|Bosnia and Herzegovin|
**Figure 3.2 Regionally Differentiated Model for Air Quality by Region, 1995-99** _(for 16 delegations in FD and 28 municipalities in the State of Mexico)_

### **Ozone**

**Annual** average daily 1-hour maximum

**PM,0** Annual average

<!-- Start of picture text --> briS- , ~ ~ t<br>Mun. & Deleg.<br>03 AA I h max in PPM Mun. & Deleg.<br>0.08 - 0.094 PM10 AA in ugIm3<br>0.094 - 0.109 40 - 50<br>0.109 - 0.123 50 -60<br>0.123 -0.138 60 -70<br>0.138 - 0.152 70-80<br>o80 -95<br><!-- End of picture text -->

## **Modelling Future Air Quality**

The previous section developed a baseline for air quality for the ZMVM thought to be representative of the end of the 1990s. Since a program to improve air quality would typically take some years to show results, we chose the year 2010 as the reference year for the future. So two research questions arise:

- What reference or business-as-usual scenario for the air quality in 2010 do we use, assuming no air quality policy beyond current measures?

- What future air quality scenario do we want to evaluate, assuming some air quality policy beyond current measures?

# _The_ **_Future_** _Reference Scenario_

For an assessment of future air quality one needs insight into factors that determine air quality. The main determinants are emissions and climate, although the latter is not likely to change significantly over the next 10 years. So, the key question is "How will emissions develop over the next decade?" In our speculations about future emissions we deal separately with ozone and PM1 o, since the origins of these two problems are for the most part unrelated.

Ozone pollution is generated in the presence of NO, (nitrogen oxides) and VOCs (volatile organic compounds or hydrocarbons) in the atmosphere, and depends on geographical, climatological and meteorological conditions. Most emissions of NO, and VOCs-50 to _75_ 9
(weather anomalies) on consumption and poverty in 2018, rather than measure the longterm equilibrium effect arising as a result of such historical processes (Dell et al., 2014).

Lastly, we also explore heterogeneity in impacts between different groups in the population. We will fit the semi-parametric partial linear regression model separately for each sub-group in the population.

We may worry that our measures of weather shocks for NDVI are endogenous. We seek to attenuate any concerns about simultaneity in households’ consumption patterns and vegetation levels concerns by studying the 2018 growing season weather shock’s lagged effect on 2019 welfare aggregates. The use of a Z score should mitigate concerns about settlements internalizing the communities’ long term weather conditions and adapting accordingly, leading to improved vegetation. As deviations from the long term mean, Z scores represent unpredictable shock for households residing in the clusters. Our results are also robust in sign and significant to using temperature z-scores, suggesting we are not introducing a differential bias with the use of NDVI.

_Within-district variability of z-scores:_ Since we include district dummies, we formally check for within-district variation of weather anomalies. In Figure 7, we plot residuals from the regression of weather anomalies at the cluster level on district dummies. These residuals capture the within-district variation in rainfall, NDVI, and temperature weather anomalies. Consistent with our discussion in section 3.5, there is very little variation in rainfall z-scores within districts, whereas there is sufficient within-district variation in NDVI and temperature anomalies.

_District Compositions:_ Since we include district dummies, ideally, we want the district’s demographic composition to not vary with the intensity of weather anomalies to identify the effects correctly. We test for this in Section C. Overall, NDVI z-scores pass this test well, and we find no significant variation in district demography with district NDVI anomalies. Broadly, the demographic composition of districts also does not vary significantly with temperature z-scores. Except higher temperature anomalies are correlated with slightly lower access to private water and private electricity connection with the sampled households in the district.

14
local financial markets, which in turn may hamper efficiency and growth.<sup>49</sup> And controls on capital inflows also entail an associated increase in domestic financing costs, as the controls permit local interest rates to remain above international levels. Microeconomic studies have found large adverse effects of Chile’s higher domestic interest rates on firms’ availability and cost of financing.<sup>50</sup> Moreover, because larger firms could better afford the cost of evasion strategies, or had direct access to foreign financing, the cost of the controls – in the form of a higher cost of borrowing -- was disproportionately borne by small and medium-sized firms.

Finally, what about the ability of controls on capital inflows to prevent volatility and crises? Overall, there is not much evidence that they have helped in this regard. Many observers have noted that the _encaje_ did not prevent Chile from suffering a major sudden stop in 1998, like most other emerging markets. In effect, the _encaje_ may have helped Chile mitigate the ‘normal’ ups and downs of capital inflows in the 1990s, but not a major global disruption like the Russia crisis (Edwards 1999). In retrospect, perhaps this should not be surprising. At times of acute financial turmoil, the run for the exit is not limited to the unwinding of short-term domestic asset positions – the kind that the _encaje_ and similar mechanisms try to deter -- but includes also that of longer-term ones, and involves domestic and international investors alike. The only deterrent is the possibility that the entry cost would have to be incurred in the future, should investors decide to rebuild their positions again. But in past crisis episodes most countries have in fact dismantled their inflow controls, so not even this deterrent remains. This puts into

> 49 See for example Bekaert, Harvey and Lundblad (2003).

> 50 Forbes (2007).

44
# **Figure A1: Financial data**

# A.  10-year government bond yields

<!-- Start of picture text --> Percent<br>Median Interquartile range<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text -->

# C. Sovereign spread (EMBI+)

<!-- Start of picture text --> Percent<br>Median Interquartile range<br>25<br>20<br>15<br>10<br>5<br>0<br>E. Equity prices<br>Percent Median Interquartile range<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text -->

# E. Equity prices

# B. 3-month interest rates

<!-- Start of picture text --> Percent<br>Median Interquartile range<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>D. Real exchange rate<br>Percent Median Interquartile range<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>F. Portfolio and other capital inflows<br>Percent of GDP<br>Median Interquartile range<br>15<br>13<br>11<br>9<br>7<br>5<br>3<br>1<br>-1<br>-3<br>-5<br>2000 2003 2006 2009 2012 2015 2018 2021<br>1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text --> _Source:_ Haver Analytics _Note:_ Shaded area reflects Federal Reserve hiking cycles.

- A. Sample includes a maximum of 25 countries from 1995Q3 to 2021Q4.

- B. Sample includes a maximum of 19 countries from 2000Q1 to 2021Q4.

- C. Sample includes a maximum of 74 countries from 1994Q1 to 2021Q4. EMBI = emerging market bond index.

- D. Year-on-year. Sample includes a maximum of 133 countries from 1993Q2 to 2021Q3.

- E. Year-on-year. Sample includes a maximum of 55 countries from 1990Q1 to 2021Q4.

- F. Sample includes a maximum of 103 countries from 1990Q1 to 2021Q2.

36
<!-- Start of picture text --> Female-headed<br>Male-headed<br>Refugee - Current<br>Refugee - Before disp<br>Host community<br>0 10 20 30 40 50 60 70 80 90 100<br>Agriculture Self Employment Paid Employment Aid Remittances<br><!-- End of picture text --> Source: Author’s calculation using Skills Profile Survey (2017), Ethiopia Figure 4: Source of livelihood by gender of the household head and refugee status To examine the factors influencing the gender differences in livelihood activities among the four refugee hosting regions, we undertook an in-depth empirical study. We therefore focus on the empirical work to estimate the gender gaps among refugees and hosts, understand what drives the gender gaps, as well as compare refugees and hosts across regions. Thus, the gendered analysis will help to determine the various needs and capacities of refugee women and men.

# 4 Empirical Strategy

In our empirical analysis, we use two different models to examine the gender differences in livelihoods among the forcibly displaced and host communities in Ethiopia.

We begin by examining the gender differences in the probability of being in employment (formal or informal). A probit model is used where the dependent variable takes a value of one if an individual is in employment and zero otherwise. 1 2 )--------------------------------------------------- (1) ii dd ii ii PP = FF(αα + ββ XX + ββ ZZ The model is estimated separately for female- and male-headed households as well as female and male individuals to see the differences at both household and individual levels. Where i is the probability of an individual/household in year being in employment (regular and casual), are camp fixed effects that capture local labor market level. i is a vector of PP ii tt individual/household head characteristics, which contains education attainment (primary, ddii secondary, and higher education), and marriage status. αα i is a vector of household XX characteristics, including age and sex of household head, household size, household head ZZ education level, number of children, access to land, and livestock ownership.

13
**Table 3 continued from previous page**

||Abidjan|Grand Bassam||Sample|
|---|---|---|---|---|
|Male|Female Dif|Male<br>Female Dif|Male|Female Dif|

Notes: Robust standard errors in parentheses.

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

This table shows the descriptive statistics, i.e., the characteristics of the sample. For each characteristic, we indicate the mean. When the variable is continuous, the standard deviation (SD) is shown in italics below the mean. The acronym EICT stands for Energy or Information Communication and Technology. Gender attitudes are measured as the ratio of stereotypes to which the respondent agrees, divided by the number of questions on gender attitudes. Hence, when the variable gender attitudes is equal to 1, the respondent agrees with all stereotypes. Standard errors are clustered by zone. Due to a low number of clusters (6), we rely on wild bootstrap to compute p-values.
road. Hence, denoting by _M_<sup>_c_</sup> the monetary value the household places on having the road’s services, where _M_<sup>_c_</sup> is just the corresponding equivalent variation. A full analysis of the household’s decision problem is to be found in Bell (2009b), in which it is argued that if the reductions in unit transport costs are sufficiently small relative to the ruling prices **p** , then the interval defined by these upper and lower bounds will also be small. Illustrative calculations with commonly employed functional forms and a range of broadly plausible parameter values indicate that, when unit transport costs are halved, the – lower bound is about 3 3.5 percent lower than the EV and the upper bound is about – 6.5 7 percent higher, which point to the relevant compensated demand and supply schedules being convex. At all events, this finding suggests that a very simple formula using suitable, one-shot survey data covering villages with and without all-weather roads will yield a defensible and rather precise estimate of the benefit accruing to a sampled household as a result of reductions in the costs of transporting the goods making up ( **y** _,_ **x** ).

An alternative is to make no particular assumptions about how land and other nonlabor resources are supplied, nor about the extent of market integration, but to leave open the possibility that labor is under-employed. With the usual convention that the elements of **y** that are inputs take a negative sign, the household’s income from production is **q**<sup>_k_</sup> **y**<sup>_k_</sup> _, k_ = 1 _,_ 2. To any improvement therein must be added the benefit arising from changes in the prices of the goods consumed, viz. those in **x** , which is analogous to that appearing in (3). We have where the fact that households are intensive consumers of farm outputs, whose farmgate prices are higher with the road, leaves open whether ( **q**<sup>2</sup> _−_ **q**<sup>1</sup> ) **x**<sup>1</sup> exceeds ( **q**<sup>2</sup> _−_ **q**<sup>1</sup> ) **x**<sup>2</sup> , or conversely. In this alternative, the upper and lower bounds on the total benefit incorporate any changes in the production plan induced by the road.

One can be more precise by imposing some restrictions on households’ preferences. Let the indirect utility function be written _v_ ( **q** _, M_ ), where _M_ denotes the level of lump-sum income. Then, by definition, _M_<sup>_c_</sup> is such that _v_ ( **q**<sup>2</sup> _, M_ ) = _v_ ( **q**<sup>1</sup> _, M_ + _M_<sup>_c_</sup> ). If 11