rafmacalaba/datause-encoder-data huggingface dataset:
self-employed, farm laborers, and domestic workers, which is similar to the sample restriction applied by Gerard et al. (2018) in a similar validation exercise. The analysis classifies workers as informal if they report that they have worked in the previous week, but do not have formal labor contracts.

The leave-out gender wage ratio is constructed using the PNAD data, but with a few differences. First, PNAD data are not as granular as the RAIS data. Measures are therefore calculated at the state level instead of the municipal level. Second, the industry variable reflects the broad industry definitions of PNAD, which consist of 12 categories, such as agriculture and construction. The analysis continues to use the 2011 baseline shares in each industry as the fixed proportions.

The distributions of the gender wage ratio in the formal and informal sectors overlap pretty closely if one considers the raw wage ratios (appendix A, figure A1). However, the leave-out wage ratio is higher in the informal sector than in the formal sector, suggesting that there is less gender inequality in the informal sector if one is considering a measure that reflects wage changes in other states. Using the raw measure, there are more positive changes between 2011 and 2015 in the informal sector than in the formal sector. However, the same conclusion does not hold if one looks at the leave-out wage ratio. A possible explanation is that most of the improvement in the gender wage ratio was driven by the sorting of women into industries, which is the component that is kept fixed in computing the leave-out measure.

The analysis also considered whether men or women are more well represented in the informal sector. It accomplished this by regressing on sex an indicator of activity in a formal sector job. The analysis controlled for tenure, age, age squared, race, and educational attainment. It also included year and state fixed effects. It found that women are 1 percent more likely to hold a formal sector job (appendix A, table A1). Even though the null hypothesis may be rejected that women and men do not sort selectively into the informal sector, the magnitude of this difference is small.

Given that a difference in differences specification is being used to measure the effects of the gender wage ratio on violence against women, only time-varying changes in wages in the informal sector that are not captured by aggregate time trends would lead to a potential bias in the estimates. Thus, the analysis examined if women’s wages in the informal sector behave differently than men’s wages in the same sector over time. It found that wages are lower in the informal sector among both women and men, but the ratio of wages between the formal and informal sectors is lower among women than men 20
The key independent variable of interest is the number of policy actions. To this end, a new data set was created using the original program documents of all policy‐based loans from the beginning of structural adjustment in 1980, until 2015. This entailed reclassification of all 21,509 policy actions – which term covers specifically listed “actions already taken”, tranche release conditions, and prior actions. The objective with the reclassification was to identify all policy actions that could have had an impact on Clusters A and B of the CPIA, viz. questions 1 to 6. Note that the classification exercise did not attempt to assess the quality or strength of the policy actions, but only their subject matter. Portmanteau actions involving two or more sub‐ actions were unbundled. Loans and credits were retained in the sample if they had one or more policy actions that were germane to Clusters A and B.<sup>14</sup> The reclassification delivers a more precise variable of interest and, arguably, less attenuation bias in our results vis‐à‐vis other studies such as Smets and Knack (2016). Of the 1,634 fast‐disbursing operations from 1980 to 2015, 222 were dropped because they were exclusively sectoral (e.g. education, health, social protection), 62 were dropped as they were sub‐ national, and 209 were dropped because they were supplemental loans/credits and thus bore no explicit policy actions, and 32 were dropped because they lacked policy actions, as frequently happened in the 1980s when many adjustment operations were hybrids of policy‐based operations and investment operations. This left a total of 1,109 economic reform operations.

> 14 One exception was made.  Budget allocations are generally germane to CPIA question 2 about fiscal matters.  But many purely sectoral operations such as education or health include a lone budget allocation.  It would not make sense to include these many operations which were not of any significance for economic reform, purely on account of a lone budget allocation which was placed there in order to ensure that the resources would be made available for the relevant sectoral ministry.  So sectoral operations with lone budget allocations were not included in the sample.

8
Table 6: Effects of mining on payment and seasonality.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_Panel A : Remuneration of _|_work for women_||||
|VARIABLES|Cash|Cash & Kind|Kind|Not Paid|
|active (20 km)|0.014|-0.029***|0.015*|-0.001|
||(0.015)|(0.011)|(0.008)|(0.012)|
|inactive (20 km)|-0.060**|0.017|0.056***|-0.013|
||(0.030)|(0.019)|(0.018)|(0.030)|
|Observations|255,889|255,889|255,889|255,889|
|F test: active-inactive=0|4.864|4.469|4.485|0.155|
|p value|0.0274|0.0345|0.0342|0.694|
|_Panel B : Seasonality of wo_|_rk for women_||||
|VARIABLES|Seasonal|All year|Occasional||
|active (20 km)|-0.075***|0.059***|0.016**||
||(0.015)|(0.013)|(0.008)||
|inactive (20 km)|-0.005|0.029|-0.024*||
||(0.029)|(0.025)|(0.015)||
|Observations|303,291|303,291|303,291||
|F test: active-inactive=0|4.713|1.138|6.084||
|p value|0.0300|0.286|0.0137||
|_Panel C : Remuneration of _|_work for men_||||
|VARIABLES|Cash|Cash & Kind|Kind|Not Paid|
|active (20 km)|0.073***|-0.013|-0.013|-0.047***|
||(0.016)|(0.013)|(0.009)|(0.013)|
|inactive (20 km)|-0.009|-0.021|0.032|-0.002|
||(0.037)|(0.030)|(0.034)|(0.035)|
|Observations|128,135|128,135|128,135|128,135|
|F test: active-inactive=0|4.056|0.0715|1.693|1.399|
|p value|0.0440|0.789|0.193|0.237|
|_Panel D : Seasonality of wo_|_rk for men_||||
|VARIABLES|Seasonal|All year|Occasional||
|active (20 km)|-0.013|0.019|-0.006||
||(0.016)|(0.017)|(0.009)||
|inactive (20 km)|0.004|0.051|-0.055***||
||(0.048)|(0.051)|(0.011)||
|Observations|108,764|108,764|108,764||
|F test: active-inactive=0|0.102|0.374|11.81||
|p value|0.750|0.541|0.001||

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. Please see Table 3 for more information about coefficients of interest.*** p<0.01, ** p<0.05, * p<0.1 18
|**Table 12: Contribution of money f**|**rom business to h**<br>|**ousehold expenditure**<br>|<br>|
|---|---|---|---|
||**Crossover**|**Non‐crossover**|**Difference**|
|Regular contribution of moneyto household|88.7|93.2|‐4.5|
|If contributes,on what?||||
|Food for the family|92.7|99.1|‐6.4|
|School fees|25.5|11.0|14.4**|
|Property payments/rent|10.9|7.3|3.6|
|Familytransport costs|23.6|24.8|‐1.1|
|Cookingfuel|61.8|76.1|‐14.3*|
|Give moneyto husband|1.8|0.0|1.8|
|Takingcare ofparents/elderly|0.0|0.0|0.0|
|Give moneyto extended family|1.8|0.0|1.8|
|Other|3.6|0.0|3.6**|
|N|62|117|179|

*** p<0.01, ** p<0.05, * p<0.1.

In addition to the monetary contributions, it is possible that the household demands on women's time may constrain women's ability to provide the supervision that larger crossover enterprises require. However, as the KASSIDA data discussed above indicate, crossover women manage to combine work and family duties more frequently than their male counterparts with no widespread adverse impacts on profits. Indeed, when we compare crossover women to non‐crossover women using the mixed methods data set, we find that crossover women actually spend significantly fewer hours per week (62 hours versus 67) working in the business. And they spend about the same time taking care of children and/or the elderly (14 hours versus 16 ‐‐ not statistically different).

# **Section VI: Designing policies to support women who want to cross over**

This paper explores an area heretofore unexplored in the literature on enterprises and occupational segregation. As such, there is not a literature on potential policies for us to draw on. In what follows, we provide a set of policy recommendations based on what is suggested by the analysis above. However, given the lack of tested interventions in this area and the limitations of descriptive analysis, 29
Policy Research Working Paper 8898

# **Abstract**

This paper analyses the degree to which infrastructure reliability and urban economic activity in several African cities is impacted by flooding. It combines firm-level micro data, flood maps, and several spatial data layers across cities through a harmonized geospatial network analysis. The analysis shows that a significant share of jobs in cities is directly affected by floods. It further details how transport infrastructure is subjected to significant flood risk that disproportionally affects main roads in many cities. While direct flood effects are revealed to be significant, this work further shows how knock-on implications for the entire urban economy might be even larger. Regardless of the direct flood exposure of firms, flooded transport networks mean that disruptions propagate across the city and drastically reduce the connectivity between firms. Access to hospitals is also found to be reduced significantly—even during relatively light flooding events: From a third of locations in Kampala, floods mean that people would no longer be able to reach hospitals within the “golden hour”— a rule of thumb referring to the window of time that maximizes the likelihood of survival after a severe medical incident. Overall, this study showcases the use of high-detail citylevel analyses to better understand the localized impacts of natural hazards on urban infrastructure networks.

This paper is a product of the Global Facility for Disaster Reduction and Recovery, Climate Change Group. 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://www.worldbank.org/ prwp. The authors may be contacted at jrentschler@worldbank.org and jbraese@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 --> Conflict Type<br>60<br>50<br>40<br>5 30. @ Internationalized<br>3z @@ IntrastateInterstate<br>@ Extrasystemic<br>20<br>10<br>oO<br>RRRSRRRSSSRSSESESRESRSSRSRRRRSRAESFFRFHFFFRAFASRSHSHFHRFSSHRHFHFHRRFHSHFFESSERR3 3 exnxzeve eg 3 ~ SBE<br>©PRIO 2009<br><!-- End of picture text -->
18

# **Table 4** MATURITY EQUATIONS

|dep var:<br>mat2<br>l<br>_______________|Eq.1<br>1st DIFF<br>GMM-I.V.|Eq.2<br>1 st DIFF<br>GMM-I.V.|
|---|---|---|
|mat2_1|.394(11.74)|.501(11.47)|
|assm|.672(8.22)|.484(5.31)|
|ctk-1|-.029(-0.59)|.005(0.09)|
|liq|.279(7.41)||
|liq,_||.020(0.47)|
|lev|.300(6.82)||
|lev__||.035(0.82)|
|lncap|-.082(-4.42)|-.056(-2.76)|
|constt|.011(3.19)|.009(2.34)|
|waldl(d.f)|578.59(6)|424.13(6)|
|wald2(d.f)|34.07(8)|34.20(8)|
|2nd order serial corr.<br>(p value)|0.409<br>(.682)|0.289<br>(.773)|
|Sargan(d.f)|167.19(157)|170.54(156)|
|obs|8739|8739|
|period|1982-89|1982-89|

Notes: (1) **t** statistics in parentheses are based on standard errors which are robust to heteroscedasticity; (ii) the minimum lag length of the instruments is 3 and the choice of specification is based on the value of the Sargan statistic; (iv) waldI for regressors; (v) wald2 for time dummies; (iii) time dunmnies have been included but are not reported.

# **V. Maturity and Performance**

In this section we test whether the conventional wisdom that term finance is important for
no surprise that, due to its uniformity, the simple regulation variable cannot explain substantial variation in MFI profitability or outreach. What is needed is a variable that better summarizes variation in the cost of complying with regulation. Towards that end, we offer three dummy variables indicating whether (1) an MFI faces a regular reporting requirement to a regulatory authority; (2) the MFI faces onsite supervision; and (3) onsite supervision occurs at regular intervals. Because MFIs that face onsite supervision also have a regular reporting requirement to supervisory authorities, the set of MFIs under supervision are a subset of those with reporting requirements. Similarly, MFIs that face regular onsite supervision are a subset of those that face any onsite supervision. Moving from the least to the most stringent type of supervision, 68% of our sample have a reporting requirement, 51% face onsite supervision, and 38% face onsite supervision at regularly scheduled intervals.

**Table 1: Regulatory Variables**

**_Panel A: Summary statistics_**

|**Dummy Variables**|**Observations**|**Mean**|
|---|---|---|
|Regulation<br>(from MBB database)|225|0.85|
|Regular Reporting<br>(Constructed here)|220|0.68|
|Onsite Supervision<br>(Constructed here)|245|0.51|
|Regular Onsite Supervision<br>(Constructed here)|244|0.38|

are regulated in our data while only 67 percent are regulated in Hartarska and Nadolnyak (2007), for example.

8
**Table 1. The hedonic pricing regression for self-assessed rent**

||Neighborhood effect level|District|District|VDC|VDC|
|---|---|---|---|---|---|
||Neighborhood effect model|Linear|Quadratic|Linear|Quadratic|
||Land plot (log Ha)|0.051 ***|0.012|0.057 ***|0.055 ***|
||Dwelling area (log sq ft)|0.214 ***|0.233 ***|0.238 ***|0.238 ***|
||Number of rooms|0.128 ***|0.129 ***|0.114 ***|0.114 ***|
||Dwelling has a kitchen|0.181 ***|0.156 ***|0.161 ***|0.159 ***|
||Dwelling has a cement wall|0.189 ***|0.207 ***|0.156 ***|0.160 ***|
||Dwelling has pillar or cement foundation|0.245 ***|0.279 ***|0.224 ***|0.228 ***|
|**ng**|Dwelling has tin or cement roof|0.339 ***|0.250 ***|0.280 ***|0.278 ***|
|**welli**|Dwelling has windows|0.254 ***|0.244 ***|0.211 ***|0.219 ***|
|**D**|Dwelling has piped water supply|0.030|-0.007|0.031|0.035|
||Dwelling has piped water|0.190 ***|0.196 ***|0.131 ***|0.132 ***|
||Paved road next to dwelling|0.312 ***|0.336 ***|0.120 ***|0.140 ***|
||Year dwelling was built|0.001|0.001 *|0.001|0.001|
||Dwelling is rented|-0.221 ***|-0.222 ***|-0.306 ***|-0.298 ***|
||Dwelling is rented out|0.101 *|0.112 *|0.067|0.075|
||Dwelling is shared|0.191 ***|0.195 ***|0.169 ***|0.168 ***|
||Municipal sewage|0.198 ***|0.261 ***|0.199 ***|0.228 ***|
||Garbage collection|0.167 ***|0.141 ***|0.151 ***|0.160 ***|
||Electricity for lighting|0.211 ***|0.172 ***|0.123 ***|0.114 ***|
|**tion**|Distance to paved road (Km)|-0.001 *|-0.000|0.002 **|0.002 ***|
|**Loca**|Distance to primary school (Km)|-0.001 *|-0.001|-0.001 **|-0.001 *|
||Mountain area|-0.783 ***|0.035|-1.110 ***|-1.162 ***|
||Hill area|-0.507 ***|-0.110 ***|-0.536 ***|-0.513 **|
||Urban area|0.303 ***|0.276 ***|-0.051|-0.007|
|**ood**|District mean rent level (log)|0.324 ***|0.982 **|||
|**orh**|District mean rent squared (log)||-0.038 *|||
|**ghb**|VDC mean rent level (log)|||0.554 ***|1.416 ***|
|**Nei**|VDC mean rent squared (log)||||-0.047 ***|
||Constant|1.702|-2.162|0.183|-3.522 *|
||Region fixed effects|No|Yes|No|No|
||District fixed effects|Yes|No|Yes|Yes|
||Observations|5,568|5,568|5,568|5,568|
||Adjusted R-squared|0.602|0.584|0.639|0.640|
||F-test|18.2 ***|4.9 **|574.5 ***|20.8 ***|

_Note_ : All regressions were estimated using ordinary least squares with household weights. Statistically significant coefficients at the 10, 5 and 1 percent level are indicated by one, two, and three asterisks, respectively. Joint significance F-tests were performed for neighborhood effects.

11
grow at the rate μ _g_ and the tractability of analysis of cyclical properties of the model economy. In this analysis, the cyclical component of a variable _X t_ , that is, _X_ _<u>t</u>_ the deviation of the variable from its trend path is defined as _xt_ = Γ _t_ − 1 .

The Solow residual from the aggregate production function captures productivity processes that contains a transitory and a permanent component: sr _t_ = _at_ + α ln Γ _t_ = ln _Yt_ − (1 −α )ln _Kt_ . (4) Since, the households supply unit labour inelastically and total mass of households is normalised to one, equation (4) measures the Solow residual in terms of per capita output and capital stock. In estimating the Solow residual for India, GDP at factor cost and net fixed capital stock, both in 2004–05 constant prices, proxy for output and capital stock, respectively. The data on GDP and net fixed capital stock are sourced from National Accounts Statistics. The labour force data are sourced from the World Bank. The value of labour share is set to 0.7 from Verma (2008). Given the availability of data on labour force and capital stock, the Solow residual series spans 1980–2009.

The transitory and permanent components in the Solow residual series for India are estimated using the Kalman filter. The underlying model is the following: the Solow residual series _srt_ is a sum of a trend component<sup>_T_</sup> _t_<sup>and a transitory or</sup> cyclical component _Ct_ : where<sup>_V_</sup> _t_<sup>represents measurement error. The trend component is assumed to follow</sup> a random walk process. This Trend-Cycle model in equation (5) can be represented in state-space form as: 9
**Table 6: Family Fixed Effect of Gross Transfer Probability** **<u>(from Children to Parents)</u>**

|**Children Characteristics**|(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|
|---|---|---|---|---|---|---|
||**Tran**|**sfer**|**Financial**|**Transfer**|**In-kind T**|**ransfer**|
|Age|0.060***|(0.011)|0.026***|(0.009)|0.055***|(0.011)|
|Age square/100|-0.050***|(0.012)|-0.020**|(0.009)|-0.047***|(0.011)|
|Oldest son|-0.076**|(0.034)|-0.008|(0.031)|-0.074**|(0.032)|
|Youngest son|-0.002|(0.030)|0.008|(0.029)|-0.007|(0.028)|
|Daughter|-0.043|(0.029)|-0.035|(0.028)|0.031|(0.028)|
|Married|0.143***|(0.055)|0.127**|(0.054)|0.120**|(0.053)|
|# of children (age<16)|0.002|(0.016)|0.010|(0.013)|-0.016|(0.010)|
|_Education (Iliterate_<br>_omitted)_|||||||
|Primary school|0.104***|(0.037)|0.082**|(0.032)|0.075**|(0.037)|
|Middle school|0.069|(0.047)|0.076*|(0.043)|0.068|(0.044)|
|High school and above|0.122**|(0.049)|0.140***|(0.047)|0.072|(0.048)|
|P-value for education|0.011||0.015||0.243||
|Observations|2,068||2,068||2,068||
|R-squared|0.067||0.033||0.058||

Note: 1) The sample includes those who are no younger than 25, with at least one parent no younger than 60, and at least one adult sibing who is not living with parents.

- 2) Clustered standard errors at family level are in parentheses.

- 3) *** p<0.01, ** p<0.05, * p<0.1

**Table 7: Family Fixed Effect of Net Transfer Amount**

|**Child Chii**|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|**ren aracterstcs**|**Tran**|**sfer**|**Financial**|**Transfer**|**In-kind**|**Transfer**|
|Age|-31.192|(54.853)|-46.214|(53.380)|15.022*|(8.036)|
|Age square/100|19.621|(55.265)|36.560|(53.616)|-16.938*|(8.961)|
|Oldest son|539.203|(453.147)|559.623|(444.135)|-20.421|(35.361)|
|Youngest son|-16.693|(237.860)|-30.752|(226.217)|14.059|(48.846)|
|Daughter|-231.014|(141.305)|-225.758*|(124.928)|-5.256|(49.848)|
|Married|140.903|(228.044)|46.342|(208.387)|94.562|(62.126)|
|# of children (age<16)|82.852|(91.813)|81.733|(89.796)|1.119|(9.196)|
|_Education (Iliterate_|||||||
|Primary school|-143.953|(167.942)|-175.741|(160.958)|31.787|(34.413)|
|Middle school|-336.365|(336.321)|-362.308|(322.653)|25.943|(55.805)|
|High school and above|735.808|(449.759)|675.833|(435.832)|59.975|(45.856)|
|P-value for education|0.251||0.202||0.557||
|Observations|2,059||2,059||2,059||
|R-squared|0.017||0.018||0.004||

Note: 1) The sample includes those who are no younger than 25, with at least one parent no younger than 60, and at least one adult sibing who is not living with parents.

- 2) Clustered standard errors at family level are in parentheses.

- 3) *** p<0.01, ** p<0.05, * p<0.1

29
Table 4: Probability of transferring out, by treatment status

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||d|Probit regressio|ns|
|Year||2000||2001|
|Treatment|-0.003|-0.004|0.014|0.011|
||-0.005|-0.004|(0.007)**|(0.005)**|
|Girl||-0.002||0|
|||-0.004||-0.003|
|Female head||0.002||0.009|
|||-0.007||-0.006|
|Nr children <6yrs||-0.004||-0.005|
|in household (2000)||(0.002)**||(0.001)***|
|Age 5||0.014||0.036|
|||(0.007)**||(0.011)***|
|Age 6||0.014||0.049|
|||(0.006)**||(0.011)***|
|Age 7||0.016<br>-0.032|||
|Observations|4378|4354|4402|4373|
|Predicted value comparison|0.02|0.02|0.02|0.02|
|Predicted value treatment|0.02|0.02|0.03|0.03|
|Mean comparison|0.02|0.02|0.02|0.02|
|Mean treatment|0.02|0.02|0.03|0.03|

Notes: The dependent variable is a discrete variable that that takes value 1 if the child transferred during the relevant period, and 0 otherwise. The sample consists of all children age 4-6 in 2000. For 2000, the sample is further restricted to children who were found in school in 2000. For 2001, the sample is restricted to children who were found in school in 2001 or in 2000 and who did not transfer in 2000. Standard errors are clustered at the school level. The regression model is probit with clustering of the standard errors at the school level. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent 18
Figure 4: Change in employment relative to course start: completers and non-completers, MDIC registrants <!-- Start of picture text --> MDIC registrants<br>Course<br>-18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18<br>Months relative to start of employment training<br>Non-completers Completers<br>.11<br>.06<br>.01<br>-.04<br>Parameter estimate with 95% CI<br>-.09<br>-.14<br><!-- End of picture text --> Notes: Figure depicts the set of coefficients for course completers and noncompleters corresponding to ( _αk_ + _βk_ ) and ( _αk_ ), respectively, from equation 2 estimated for employment [0/1]. 95% confidence intervals constructed from heteroskedasticity-consistent standard errors clustered by individual and month.

46